A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

International Nuclear Information System (INIS)

Yang, Jinzhong; Aristophanous, Michalis; Beadle, Beth M.; Garden, Adam S.; Schwartz, David L.

2015-01-01

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm"3 (range, 6.6–44.3 cm"3), while the PET segmented GTV was 10.2 cm"3 (range, 2.8–45.1 cm"3). The median physician-defined GTV was 22.1 cm"3 (range, 4.2–38.4 cm"3). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was

Automatic segmentation of the heart in radiotherapy for breast cancer

DEFF Research Database (Denmark)

Laugaard Lorenzen, Ebbe; Ewertz, Marianne; Brink, Carsten

2014-01-01

Background. The aim of this study was to evaluate two fully automatic segmentation methods in comparison with manual delineations for their use in delineating the heart on planning computed tomography (CT) used in radiotherapy for breast cancer. Material and methods. Automatic delineation of heart...... in 15 breast cancer patients was performed by two different automatic delineation systems. Analysis of accuracy and precision of the differences between manual and automatic delineations were evaluated on volume, mean dose, maximum dose and spatial distance differences. Two sets of manual delineations...

Forest Delineation Based on Airborne LIDAR Data

Directory of Open Access Journals (Sweden)

Norbert Pfeifer

2012-03-01

Full Text Available The delineation of forested areas is a critical task, because the resulting maps are a fundamental input for a broad field of applications and users. Different national and international forest definitions are available for manual or automatic delineation, but unfortunately most definitions lack precise geometrical descriptions for the different criteria. A mandatory criterion in forest definitions is the criterion of crown coverage (CC, which defines the proportion of the forest floor covered by the vertical projection of the tree crowns. For loosely stocked areas, this criterion is especially critical, because the size and shape of the reference area for calculating CC is not clearly defined in most definitions. Thus current forest delineations differ and tend to be non-comparable because of different settings for checking the criterion of CC in the delineation process. This paper evaluates a new approach for the automatic delineation of forested areas, based on airborne laser scanning (ALS data with a clearly defined method for calculating CC. The new approach, the ‘tree triples’ method, is based on defining CC as a relation between the sum of the crown areas of three neighboring trees and the area of their convex hull. The approach is applied and analyzed for two study areas in Tyrol, Austria. The selected areas show a loosely stocked forest at the upper timberline and a fragmented forest on the hillside. The fully automatic method presented for delineating forested areas from ALS data shows promising results with an overall accuracy of 96%, and provides a beneficial tool for operational applications.

Interactive Cadastral Boundary Delineation from Uav Data

Science.gov (United States)

Crommelinck, S.; Höfle, B.; Koeva, M. N.; Yang, M. Y.; Vosselman, G.

2018-05-01

Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire land tenure data. UAVs can capture geospatial data at high quality and resolution in a cost-effective, transparent and flexible manner, from which visible land parcel boundaries, i.e., cadastral boundaries are delineable. This delineation is to no extent automated, even though physical objects automatically retrievable through image analysis methods mark a large portion of cadastral boundaries. This study proposes (i) a methodology that automatically extracts and processes candidate cadastral boundary features from UAV data, and (ii) a procedure for a subsequent interactive delineation. Part (i) consists of two state-of-the-art computer vision methods, namely gPb contour detection and SLIC superpixels, as well as a classification part assigning costs to each outline according to local boundary knowledge. Part (ii) allows a user-guided delineation by calculating least-cost paths along previously extracted and weighted lines. The approach is tested on visible road outlines in two UAV datasets from Germany. Results show that all roads can be delineated comprehensively. Compared to manual delineation, the number of clicks per 100 m is reduced by up to 86 %, while obtaining a similar localization quality. The approach shows promising results to reduce the effort of manual delineation that is currently employed for indirect (cadastral) surveying.

Automated delineation of stroke lesions using brain CT images

Directory of Open Access Journals (Sweden)

Céline R. Gillebert

2014-01-01

Full Text Available Computed tomographic (CT images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner.

Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

CERN Document Server

Kainmueller, Dagmar

2014-01-01

? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

An automatic virtual patient reconstruction from CT-scans for hepatic surgical planning.

Science.gov (United States)

Soler, L; Delingette, H; Malandain, G; Ayache, N; Koehl, C; Clément, J M; Dourthe, O; Marescaux, J

2000-01-01

PROBLEM/BACKGROUND: In order to help hepatic surgical planning we perfected automatic 3D reconstruction of patients from conventional CT-scan, and interactive visualization and virtual resection tools. From a conventional abdominal CT-scan, we have developed several methods allowing the automatic 3D reconstruction of skin, bones, kidneys, lung, liver, hepatic lesions, and vessels. These methods are based on deformable modeling or thresholding algorithms followed by the application of mathematical morphological operators. From these anatomical and pathological models, we have developed a new framework for translating anatomical knowledge into geometrical and topological constraints. More precisely, our approach allows to automatically delineate the hepatic and portal veins but also to label the portal vein and finally to build an anatomical segmentation of the liver based on Couinaud definition which is currently used by surgeons all over the world. Finally, we have developed a user friendly interface for the 3D visualization of anatomical and pathological structures, the accurate evaluation of volumes and distances and for the virtual hepatic resection along a user-defined cutting plane. A validation study on a 30 patients database gives 2 mm of precision for liver delineation and less than 1 mm for all other anatomical and pathological structures delineation. An in vivo validation performed during surgery also showed that anatomical segmentation is more precise than the delineation performed by a surgeon based on external landmarks. This surgery planning system has been routinely used by our medical partner, and this has resulted in an improvement of the planning and performance of hepatic surgery procedures. We have developed new tools for hepatic surgical planning allowing a better surgery through an automatic delineation and visualization of anatomical and pathological structures. These tools represent a first step towards the development of an augmented

Automatic emissive probe apparatus for accurate plasma and vacuum space potential measurements

Science.gov (United States)

Jianquan, LI; Wenqi, LU; Jun, XU; Fei, GAO; Younian, WANG

2018-02-01

We have developed an automatic emissive probe apparatus based on the improved inflection point method of the emissive probe for accurate measurements of both plasma potential and vacuum space potential. The apparatus consists of a computer controlled data acquisition card, a working circuit composed by a biasing unit and a heating unit, as well as an emissive probe. With the set parameters of the probe scanning bias, the probe heating current and the fitting range, the apparatus can automatically execute the improved inflection point method and give the measured result. The validity of the automatic emissive probe apparatus is demonstrated in a test measurement of vacuum potential distribution between two parallel plates, showing an excellent accuracy of 0.1 V. Plasma potential was also measured, exhibiting high efficiency and convenient use of the apparatus for space potential measurements.

Crowdsourcing for error detection in cortical surface delineations.

Science.gov (United States)

Ganz, Melanie; Kondermann, Daniel; Andrulis, Jonas; Knudsen, Gitte Moos; Maier-Hein, Lena

2017-01-01

With the recent trend toward big data analysis, neuroimaging datasets have grown substantially in the past years. While larger datasets potentially offer important insights for medical research, one major bottleneck is the requirement for resources of medical experts needed to validate automatic processing results. To address this issue, the goal of this paper was to assess whether anonymous nonexperts from an online community can perform quality control of MR-based cortical surface delineations derived by an automatic algorithm. So-called knowledge workers from an online crowdsourcing platform were asked to annotate errors in automatic cortical surface delineations on 100 central, coronal slices of MR images. On average, annotations for 100 images were obtained in less than an hour. When using expert annotations as reference, the crowd on average achieves a sensitivity of 82 % and a precision of 42 %. Merging multiple annotations per image significantly improves the sensitivity of the crowd (up to 95 %), but leads to a decrease in precision (as low as 22 %). Our experiments show that the detection of errors in automatic cortical surface delineations generated by anonymous untrained workers is feasible. Future work will focus on increasing the sensitivity of our method further, such that the error detection tasks can be handled exclusively by the crowd and expert resources can be focused on error correction.

Clipping of tumour resection margins allows accurate target volume delineation in head and neck cancer adjuvant radiation therapy

International Nuclear Information System (INIS)

Bittermann, Gido; Wiedenmann, Nicole; Bunea, Andrei; Schwarz, Steffen J.; Grosu, Anca-L.; Schmelzeisen, Rainer; Metzger, Marc C.

2015-01-01

Background: Accurate tumour bed localisation is a key requirement for adjuvant radiotherapy. A new procedure is described for head and neck cancer treatment that improves tumour bed localisation using titanium clips. Materials and methods: Following complete local excision of the primary tumour, the tumour bed was marked with titanium clips. Preoperative gross target volume (GTV) and postoperative tumour bed were examined and the distances between the centres of gravity were evaluated. Results: 49 patients with squamous cell carcinoma of the oral cavity were prospectively enrolled in this study. All patients underwent tumour resection, neck lymph node dissection and defect reconstruction in one stage. During surgery, 7–49 clips were placed in the resection cavity. Surgical clip insertion was successful in 88% (n = 43). Clip identification and tumour bed delineation was successful in all 43 patients. The overall distance between the centres of gravity of the preoperative tumour extension to the tumour bed was 0.9 cm. A significant relationship between the preoperative tumour extension and the postoperative tumour bed volume could be demonstrated. Conclusion: We demonstrate a precise delineation of the former tumour cavity. Improvements in tumour bed delineation allow an increase of accuracy for adjuvant treatment

Automatic delineation of brain regions on MRI and PET images from the pig

DEFF Research Database (Denmark)

Villadsen, Jonas; Hansen, Hanne D; Jørgensen, Louise M

2018-01-01

: Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. NEW METHOD: A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer....... RESULTS: MRI and [11C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same...... the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [11C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames...

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

Science.gov (United States)

Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

2017-11-01

Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

A wavelet-based ECG delineation algorithm for 32-bit integer online processing.

Science.gov (United States)

Di Marco, Luigi Y; Chiari, Lorenzo

2011-04-03

Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding. This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.

Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.

Science.gov (United States)

Thomson, David; Boylan, Chris; Liptrot, Tom; Aitkenhead, Adam; Lee, Lip; Yap, Beng; Sykes, Andrew; Rowbottom, Carl; Slevin, Nicholas

2014-08-03

The accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT. Five clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA). For all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency. Improvements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.

SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

Energy Technology Data Exchange (ETDEWEB)

Liu, Y; Stojadinovic, S; Jiang, S; Timmerman, R; Abdulrahman, R; Nedzi, L; Gu, X [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0

Accurate and precise determination of small quantity uranium by means of automatic potentiometric titration

International Nuclear Information System (INIS)

Liu Quanwei; Luo Zhongyan; Zhu Haiqiao; Wu Jizong

2007-01-01

For high radioactivity level of dissolved solution of spent fuel and the solution of uranium product, radioactive hazard must be considered and reduced as low as possible during accurate determination of uranium. In this work automatic potentiometric titration was applied and the sample only 10 mg of uranium contained was taken in order to reduce the harm of analyzer suffered from the radioactivity. RSD<0.06%, at the same time the result can be corrected for more reliable and accurate measurement. The determination method can effectively reduce the harm of analyzer suffered from the radioactivity, and meets the requirement of reliable accurate measurement of uranium. (authors)

SLIC superpixels for object delineation UAV data

NARCIS (Netherlands)

Crommelinck, Sophie Charlotte; Bennett, R.M.; Gerke, Markus; Koeva, M.N.; Yang, M.Y.; Vosselman, G.; Stachniss, C.; Förstner, W.; Schneider, J.

2017-01-01

Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with

TU-H-CAMPUS-JeP2-05: Can Automatic Delineation of Cardiac Substructures On Noncontrast CT Be Used for Cardiac Toxicity Analysis?

Energy Technology Data Exchange (ETDEWEB)

Luo, Y; Liao, Z; Jiang, W; Gomez, D; Williamson, R; Court, L; Yang, J [MD Anderson Cancer Center, Houston, TX (United States)

2016-06-15

Purpose: To evaluate the feasibility of using an automatic segmentation tool to delineate cardiac substructures from computed tomography (CT) images for cardiac toxicity analysis for non-small cell lung cancer (NSCLC) patients after radiotherapy. Methods: A multi-atlas segmentation tool developed in-house was used to delineate eleven cardiac substructures including the whole heart, four heart chambers, and six greater vessels automatically from the averaged 4DCT planning images for 49 NSCLC patients. The automatic segmented contours were edited appropriately by two experienced radiation oncologists. The modified contours were compared with the auto-segmented contours using Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate how much modification was needed. In addition, the dose volume histogram (DVH) of the modified contours were compared with that of the auto-segmented contours to evaluate the dosimetric difference between modified and auto-segmented contours. Results: Of the eleven structures, the averaged DSC values ranged from 0.73 ± 0.08 to 0.95 ± 0.04 and the averaged MSD values ranged from 1.3 ± 0.6 mm to 2.9 ± 5.1mm for the 49 patients. Overall, the modification is small. The pulmonary vein (PV) and the inferior vena cava required the most modifications. The V30 (volume receiving 30 Gy or above) for the whole heart and the mean dose to the whole heart and four heart chambers did not show statistically significant difference between modified and auto-segmented contours. The maximum dose to the greater vessels did not show statistically significant difference except for the PV. Conclusion: The automatic segmentation of the cardiac substructures did not require substantial modification. The dosimetric evaluation showed no statistically significant difference between auto-segmented and modified contours except for the PV, which suggests that auto-segmented contours for the cardiac dose response study are feasible in the clinical

TU-H-CAMPUS-JeP2-05: Can Automatic Delineation of Cardiac Substructures On Noncontrast CT Be Used for Cardiac Toxicity Analysis?

International Nuclear Information System (INIS)

Luo, Y; Liao, Z; Jiang, W; Gomez, D; Williamson, R; Court, L; Yang, J

2016-01-01

Purpose: To evaluate the feasibility of using an automatic segmentation tool to delineate cardiac substructures from computed tomography (CT) images for cardiac toxicity analysis for non-small cell lung cancer (NSCLC) patients after radiotherapy. Methods: A multi-atlas segmentation tool developed in-house was used to delineate eleven cardiac substructures including the whole heart, four heart chambers, and six greater vessels automatically from the averaged 4DCT planning images for 49 NSCLC patients. The automatic segmented contours were edited appropriately by two experienced radiation oncologists. The modified contours were compared with the auto-segmented contours using Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate how much modification was needed. In addition, the dose volume histogram (DVH) of the modified contours were compared with that of the auto-segmented contours to evaluate the dosimetric difference between modified and auto-segmented contours. Results: Of the eleven structures, the averaged DSC values ranged from 0.73 ± 0.08 to 0.95 ± 0.04 and the averaged MSD values ranged from 1.3 ± 0.6 mm to 2.9 ± 5.1mm for the 49 patients. Overall, the modification is small. The pulmonary vein (PV) and the inferior vena cava required the most modifications. The V30 (volume receiving 30 Gy or above) for the whole heart and the mean dose to the whole heart and four heart chambers did not show statistically significant difference between modified and auto-segmented contours. The maximum dose to the greater vessels did not show statistically significant difference except for the PV. Conclusion: The automatic segmentation of the cardiac substructures did not require substantial modification. The dosimetric evaluation showed no statistically significant difference between auto-segmented and modified contours except for the PV, which suggests that auto-segmented contours for the cardiac dose response study are feasible in the clinical

Dentalmaps: Automatic Dental Delineation for Radiotherapy Planning in Head-and-Neck Cancer

International Nuclear Information System (INIS)

Thariat, Juliette; Ramus, Liliane; Maingon, Philippe; Odin, Guillaume; Gregoire, Vincent; Darcourt, Vincent; Guevara, Nicolas; Orlanducci, Marie-Helene; Marcie, Serge; Poissonnet, Gilles; Marcy, Pierre-Yves

2012-01-01

Purpose: To propose an automatic atlas-based segmentation framework of the dental structures, called Dentalmaps, and to assess its accuracy and relevance to guide dental care in the context of intensity-modulated radiotherapy. Methods and Materials: A multi-atlas–based segmentation, less sensitive to artifacts than previously published head-and-neck segmentation methods, was used. The manual segmentations of a 21-patient database were first deformed onto the query using nonlinear registrations with the training images and then fused to estimate the consensus segmentation of the query. Results: The framework was evaluated with a leave-one-out protocol. The maximum doses estimated using manual contours were considered as ground truth and compared with the maximum doses estimated using automatic contours. The dose estimation error was within 2-Gy accuracy in 75% of cases (with a median of 0.9 Gy), whereas it was within 2-Gy accuracy in 30% of cases only with the visual estimation method without any contour, which is the routine practice procedure. Conclusions: Dose estimates using this framework were more accurate than visual estimates without dental contour. Dentalmaps represents a useful documentation and communication tool between radiation oncologists and dentists in routine practice. Prospective multicenter assessment is underway on patients extrinsic to the database.

Automatic generation of a subject-specific model for accurate markerless motion capture and biomechanical applications.

Science.gov (United States)

Corazza, Stefano; Gambaretto, Emiliano; Mündermann, Lars; Andriacchi, Thomas P

2010-04-01

A novel approach for the automatic generation of a subject-specific model consisting of morphological and joint location information is described. The aim is to address the need for efficient and accurate model generation for markerless motion capture (MMC) and biomechanical studies. The algorithm applied and expanded on previous work on human shapes space by embedding location information for ten joint centers in a subject-specific free-form surface. The optimal locations of joint centers in the 3-D mesh were learned through linear regression over a set of nine subjects whose joint centers were known. The model was shown to be sufficiently accurate for both kinematic (joint centers) and morphological (shape of the body) information to allow accurate tracking with MMC systems. The automatic model generation algorithm was applied to 3-D meshes of different quality and resolution such as laser scans and visual hulls. The complete method was tested using nine subjects of different gender, body mass index (BMI), age, and ethnicity. Experimental training error and cross-validation errors were 19 and 25 mm, respectively, on average over the joints of the ten subjects analyzed in the study.

Training shortest-path tractography: Automatic learning of spatial priors

DEFF Research Database (Denmark)

Kasenburg, Niklas; Liptrot, Matthew George; Reislev, Nina Linde

2016-01-01

Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior...... knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we...

Comparison of manual and automatic MR-CT registration for radiotherapy of prostate cancer.

Science.gov (United States)

Korsager, Anne Sofie; Carl, Jesper; Riis Østergaard, Lasse

2016-05-08

In image-guided radiotherapy (IGRT) of prostate cancer, delineation of the clini-cal target volume (CTV) often relies on magnetic resonance (MR) because of its good soft-tissue visualization. Registration of MR and computed tomography (CT) is required in order to add this accurate delineation to the dose planning CT. An automatic approach for local MR-CT registration of the prostate has previously been developed using a voxel property-based registration as an alternative to a manual landmark-based registration. The aim of this study is to compare the two registration approaches and to investigate the clinical potential for replacing the manual registration with the automatic registration. Registrations and analysis were performed for 30 prostate cancer patients treated with IGRT using a Ni-Ti prostate stent as a fiducial marker. The comparison included computing translational and rotational differences between the approaches, visual inspection, and computing the overlap of the CTV. The computed mean translational difference was 1.65, 1.60, and 1.80mm and the computed mean rotational difference was 1.51°, 3.93°, and 2.09° in the superior/inferior, anterior/posterior, and medial/lateral direction, respectively. The sensitivity of overlap was 87%. The results demonstrate that the automatic registration approach performs registrations comparable to the manual registration.

Slic Superpixels for Object Delineation from Uav Data

Science.gov (United States)

Crommelinck, S.; Bennett, R.; Gerke, M.; Koeva, M. N.; Yang, M. Y.; Vosselman, G.

2017-08-01

Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64 %. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.

Phase II modification of the Water Availability Tool for Environmental Resources (WATER) for Kentucky: The sinkhole-drainage process, point-and-click basin delineation, and results of karst test-basin simulations

Science.gov (United States)

Taylor, Charles J.; Williamson, Tanja N.; Newson, Jeremy K.; Ulery, Randy L.; Nelson, Hugh L.; Cinotto, Peter J.

2012-01-01

This report describes Phase II modifications made to the Water Availability Tool for Environmental Resources (WATER), which applies the process-based TOPMODEL approach to simulate or predict stream discharge in surface basins in the Commonwealth of Kentucky. The previous (Phase I) version of WATER did not provide a means of identifying sinkhole catchments or accounting for the effects of karst (internal) drainage in a TOPMODEL-simulated basin. In the Phase II version of WATER, sinkhole catchments are automatically identified and delineated as internally drained subbasins, and a modified TOPMODEL approach (called the sinkhole drainage process, or SDP-TOPMODEL) is applied that calculates mean daily discharges for the basin based on summed area-weighted contributions from sinkhole drain-age (SD) areas and non-karstic topographically drained (TD) areas. Results obtained using the SDP-TOPMODEL approach were evaluated for 12 karst test basins located in each of the major karst terrains in Kentucky. Visual comparison of simulated hydrographs and flow-duration curves, along with statistical measures applied to the simulated discharge data (bias, correlation, root mean square error, and Nash-Sutcliffe efficiency coefficients), indicate that the SDPOPMODEL approach provides acceptably accurate estimates of discharge for most flow conditions and typically provides more accurate simulation of stream discharge in karstic basins compared to the standard TOPMODEL approach. Additional programming modifications made to the Phase II version of WATER included implementation of a point-and-click graphical user interface (GUI), which fully automates the delineation of simulation-basin boundaries and improves the speed of input-data processing. The Phase II version of WATER enables the user to select a pour point anywhere on a stream reach of interest, and the program will automatically delineate all upstream areas that contribute drainage to that point. This capability enables

Validation of Simple Quantification Methods for (18)F-FP-CIT PET Using Automatic Delineation of Volumes of Interest Based on Statistical Probabilistic Anatomical Mapping and Isocontour Margin Setting.

Science.gov (United States)

Kim, Yong-Il; Im, Hyung-Jun; Paeng, Jin Chul; Lee, Jae Sung; Eo, Jae Seon; Kim, Dong Hyun; Kim, Euishin E; Kang, Keon Wook; Chung, June-Key; Lee, Dong Soo

2012-12-01

(18)F-FP-CIT positron emission tomography (PET) is an effective imaging for dopamine transporters. In usual clinical practice, (18)F-FP-CIT PET is analyzed visually or quantified using manual delineation of a volume of interest (VOI) for the striatum. In this study, we suggested and validated two simple quantitative methods based on automatic VOI delineation using statistical probabilistic anatomical mapping (SPAM) and isocontour margin setting. Seventy-five (18)F-FP-CIT PET images acquired in routine clinical practice were used for this study. A study-specific image template was made and the subject images were normalized to the template. Afterwards, uptakes in the striatal regions and cerebellum were quantified using probabilistic VOI based on SPAM. A quantitative parameter, QSPAM, was calculated to simulate binding potential. Additionally, the functional volume of each striatal region and its uptake were measured in automatically delineated VOI using isocontour margin setting. Uptake-volume product (QUVP) was calculated for each striatal region. QSPAM and QUVP were compared with visual grading and the influence of cerebral atrophy on the measurements was tested. Image analyses were successful in all the cases. Both the QSPAM and QUVP were significantly different according to visual grading (P Simple quantitative measurements of QSPAM and QUVP showed acceptable agreement with visual grading. Although QSPAM in some group may be influenced by cerebral atrophy, these simple methods are expected to be effective in the quantitative analysis of (18)F-FP-CIT PET in usual clinical practice.

Improvement of the exponential experiment system for the automatical and accurate measurement of the exponential decay constant

International Nuclear Information System (INIS)

Shin, Hee Sung; Jang, Ji Woon; Lee, Yoon Hee; Hwang, Yong Hwa; Kim, Ho Dong

2004-01-01

The previous exponential experiment system has been improved for the automatical and accurate axial movement of the neutron source and detector with attaching the automatical control system which consists of a Programmable Logical Controller(PLC) and a stepping motor set. The automatic control program which controls MCA and PLC consistently has been also developed on the basis of GENIE 2000 library. The exponential experiments have been carried out for Kori 1 unit spent fuel assemblies, C14, J14 and G23, and Kori 2 unit spent fuel assembly, J44, using the improved systematical measurement system. As the results, the average exponential decay constants for 4 assemblies are determined to be 0.1302, 0.1267, 0.1247, and 0.1210, respectively, with the application of poisson regression

Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

International Nuclear Information System (INIS)

Dolz, J.; Kirişli, H. A.; Massoptier, L.; Fechter, T.; Karnitzki, S.; Oehlke, O.; Nestle, U.; Vermandel, M.

2016-01-01

Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs

Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients.

Science.gov (United States)

Dolz, J; Kirişli, H A; Fechter, T; Karnitzki, S; Oehlke, O; Nestle, U; Vermandel, M; Massoptier, L

2016-05-01

Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has

Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

Energy Technology Data Exchange (ETDEWEB)

Dolz, J., E-mail: jose.dolz.upv@gmail.com [AQUILAB, Loos-les-Lille 59120, France and University Lille, Inserm, CHU Lille, U1189–ONCO-THAI–Image Assisted Laser Therapy for Oncology, Lille F-59000 (France); Kirişli, H. A.; Massoptier, L. [AQUILAB, Loos-les-Lille 59120 (France); Fechter, T.; Karnitzki, S.; Oehlke, O.; Nestle, U. [Department of Radiation Oncology, University Medical Center, Freiburg 79106 (Germany); Vermandel, M. [Inserm Onco Thai U1189, Université Lille 2, CHRU Lille, Lille 59037 (France)

2016-05-15

Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs

Comparison of manual and automatic MR‐CT registration for radiotherapy of prostate cancer

Science.gov (United States)

Carl, Jesper; Østergaard, Lasse Riis

2016-01-01

In image‐guided radiotherapy (IGRT) of prostate cancer, delineation of the clinical target volume (CTV) often relies on magnetic resonance (MR) because of its good soft‐tissue visualization. Registration of MR and computed tomography (CT) is required in order to add this accurate delineation to the dose planning CT. An automatic approach for local MR‐CT registration of the prostate has previously been developed using a voxel property‐based registration as an alternative to a manual landmark‐based registration. The aim of this study is to compare the two registration approaches and to investigate the clinical potential for replacing the manual registration with the automatic registration. Registrations and analysis were performed for 30 prostate cancer patients treated with IGRT using a Ni‐Ti prostate stent as a fiducial marker. The comparison included computing translational and rotational differences between the approaches, visual inspection, and computing the overlap of the CTV. The computed mean translational difference was 1.65, 1.60, and 1.80 mm and the computed mean rotational difference was 1.51°, 3.93°, and 2.09° in the superior/inferior, anterior/posterior, and medial/lateral direction, respectively. The sensitivity of overlap was 87%. The results demonstrate that the automatic registration approach performs registrations comparable to the manual registration. PACS number(s): 87.57.nj, 87.61.‐c, 87.57.Q‐, 87.56.J‐ PMID:27167285

Atlas-based delineation of lymph node levels in head and neck computed tomography images

International Nuclear Information System (INIS)

Commowick, Olivier; Gregoire, Vincent; Malandain, Gregoire

2008-01-01

Purpose: Radiotherapy planning requires accurate delineations of the tumor and of the critical structures. Atlas-based segmentation has been shown to be very efficient to automatically delineate brain critical structures. We therefore propose to construct an anatomical atlas of the head and neck region. Methods and materials: Due to the high anatomical variability of this region, an atlas built from a single image as for the brain is not adequate. We address this issue by building a symmetric atlas from a database of manually segmented images. First, we develop an atlas construction method and apply it to a database of 45 Computed Tomography (CT) images from patients with node-negative pharyngo-laryngeal squamous cell carcinoma manually delineated for radiotherapy. Then, we qualitatively and quantitatively evaluate the results generated by the built atlas based on Leave-One-Out framework on the database. Results: We present qualitative and quantitative results using this atlas construction method. The evaluation was performed on a subset of 12 patients among the original CT database of 45 patients. Qualitative results depict visually well delineated structures. The quantitative results are also good, with an error with respect to the best achievable results ranging from 0.196 to 0.404 with a mean of 0.253. Conclusions: These results show the feasibility of using such an atlas for radiotherapy planning. Many perspectives are raised from this work ranging from extensive validation to the construction of several atlases representing sub-populations, to account for large inter-patient variabilities, and populations with node-positive tumors

Image-Based Delineation and Classification of Built Heritage Masonry

Directory of Open Access Journals (Sweden)

Noelia Oses

2014-02-01

Full Text Available Fundación Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls.

B0-correction and k-means clustering for accurate and automatic identification of regions with reduced apparent diffusion coefficient (ADC) in adva nced cervical cancer at the time of brachytherapy

DEFF Research Database (Denmark)

Haack, Søren; Pedersen, Erik Morre; Vinding, Mads Sloth

in dose planning of radiotherapy. This study evaluates the use of k-means clustering for automatic user independent delineation of regions of reduced apparent diffusion coefficient (ADC) and the value of B0-correction of DW-MRI for reduction of geometrical distortions during dose planning of brachytherapy...

How large is the Upper Indus Basin? The pitfalls of auto-delineation using DEMs

Science.gov (United States)

Khan, Asif; Richards, Keith S.; Parker, Geoffrey T.; McRobie, Allan; Mukhopadhyay, Biswajit

2014-02-01

Extraction of watershed areas from Digital Elevation Models (DEMs) is increasingly required in a variety of environmental analyses. It is facilitated by the availability of DEMs based on remotely sensed data, and by Geographical Information System (GIS) software. However, accurate delineation depends on the quality of the DEM and the methodology adopted. This paper considers automated and supervised delineation in a case study of the Upper Indus Basin (UIB), Pakistan, for which published estimates of the basin area show significant disagreement, ranging from 166,000 to 266,000 km2. Automated delineation used ArcGIS Archydro and hydrology tools applied to three good quality DEMs (two from SRTM data with 90m resolution, and one from 30m resolution ASTER data). Automatic delineation defined a basin area of c.440,000 km2 for the UIB, but included a large area of internal drainage in the western Tibetan Plateau. It is shown that discrepancies between different estimates reflect differences in the initial extent of the DEM used for watershed delineation, and the unchecked effect of iterative pit-filling of the DEM (going beyond the filling of erroneous pixels to filling entire closed basins). For the UIB we have identified critical points where spurious addition of catchment area has arisen, and use Google Earth to examine the geomorphology adjacent to these points, and also examine the basin boundary data provided by the HydroSHEDS database. We show that the Pangong Tso watershed and some other areas in the western Tibetan plateau are not part of the UIB, but are areas of internal drainage. Our best estimate of the area of the Upper Indus Basin (at Besham Qila) is 164,867 km2 based on the SRTM DEM, and 164,853 km2 using the ASTER DEM). This matches the catchment area measured by WAPDA SWHP. An important lesson from this investigation is that one should not rely on automated delineation, as iterative pit-filling can produce spurious drainage networks and basins, when

Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT

International Nuclear Information System (INIS)

Tufvesson, Jane; Carlsson, Marcus; Aletras, Anthony H.; Engblom, Henrik; Deux, Jean-François; Koul, Sasha; Sörensson, Peder; Pernow, John; Atar, Dan; Erlinge, David; Arheden, Håkan; Heiberg, Einar

2016-01-01

Efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI = 1-MI/MaR). Cardiovascular magnetic resonance (CMR) can be used to assess MI by late gadolinium enhancement (LGE) and MaR by either T2-weighted imaging or contrast enhanced SSFP (CE-SSFP). Automatic segmentation algorithms have been developed and validated for MI by LGE as well as for MaR by T2-weighted imaging. There are, however, no algorithms available for CE-SSFP. Therefore, the aim of this study was to develop and validate automatic segmentation of MaR in CE-SSFP. The automatic algorithm applies surface coil intensity correction and classifies myocardial intensities by Expectation Maximization to define a MaR region based on a priori regional criteria, and infarct region from LGE. Automatic segmentation was validated against manual delineation by expert readers in 183 patients with reperfused acute MI from two multi-center randomized clinical trials (RCT) (CHILL-MI and MITOCARE) and against myocardial perfusion SPECT in an additional set (n = 16). Endocardial and epicardial borders were manually delineated at end-diastole and end-systole. Manual delineation of MaR was used as reference and inter-observer variability was assessed for both manual delineation and automatic segmentation of MaR in a subset of patients (n = 15). MaR was expressed as percent of left ventricular mass (%LVM) and analyzed by bias (mean ± standard deviation). Regional agreement was analyzed by Dice Similarity Coefficient (DSC) (mean ± standard deviation). MaR assessed by manual and automatic segmentation were 36 ± 10 % and 37 ± 11 %LVM respectively with bias 1 ± 6 %LVM and regional agreement DSC 0.85 ± 0.08 (n = 183). MaR assessed by SPECT and CE-SSFP automatic segmentation were 27 ± 10 %LVM and 29 ± 7 %LVM respectively with bias 2 ± 7 %LVM. Inter-observer variability was 0 ± 3 %LVM for manual delineation and

SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

International Nuclear Information System (INIS)

Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu; Pradier, Olivier; Cheze Le Rest, Catherine

2015-01-01

Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments

Comparison of [11C]choline Positron Emission Tomography With T2- and Diffusion-Weighted Magnetic Resonance Imaging for Delineating Malignant Intraprostatic Lesions

International Nuclear Information System (INIS)

Chang, Joe H.; Lim Joon, Daryl; Davis, Ian D.; Lee, Sze Ting; Hiew, Chee-Yan; Esler, Stephen; Gong, Sylvia J.; Wada, Morikatsu; Clouston, David; O'Sullivan, Richard; Goh, Yin P.; Bolton, Damien; Scott, Andrew M.; Khoo, Vincent

2015-01-01

Purpose: The purpose of this study was to compare the accuracy of [ 11 C]choline positron emission tomography (CHOL-PET) with that of the combination of T2-weighted and diffusion-weighted (T2W/DW) magnetic resonance imaging (MRI) for delineating malignant intraprostatic lesions (IPLs) for guiding focal therapies and to investigate factors predicting the accuracy of CHOL-PET. Methods and Materials: This study included 21 patients who underwent CHOL-PET and T2W/DW MRI prior to radical prostatectomy. Two observers manually delineated IPL contours for each scan, and automatic IPL contours were generated on CHOL-PET based on varying proportions of the maximum standardized uptake value (SUV). IPLs identified on prostatectomy specimens defined reference standard contours. The imaging-based contours were compared with the reference standard contours using Dice similarity coefficient (DSC), and sensitivity and specificity values. Factors that could potentially predict the DSC of the best contouring method were analyzed using linear models. Results: The best automatic contouring method, 60% of the maximum SUV (SUV 60 ) , had similar correlations (DSC: 0.59) with the manual PET contours (DSC: 0.52, P=.127) and significantly better correlations than the manual MRI contours (DSC: 0.37, P<.001). The sensitivity and specificity values were 72% and 71% for SUV 60 ; 53% and 86% for PET manual contouring; and 28% and 92% for MRI manual contouring. The tumor volume and transition zone pattern could independently predict the accuracy of CHOL-PET. Conclusions: CHOL-PET is superior to the combination of T2W/DW MRI for delineating IPLs. The accuracy of CHOL-PET is insufficient for gland-sparing focal therapies but may be accurate enough for focal boost therapies. The transition zone pattern is a new classification that may predict how well CHOL-PET delineates IPLs

Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.

Science.gov (United States)

Cheimariotis, Grigorios-Aris; Al-Mashat, Mariam; Haris, Kostas; Aletras, Anthony H; Jögi, Jonas; Bajc, Marika; Maglaveras, Nicolaos; Heiberg, Einar

2018-02-01

Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p automatic quantification of wide range of measurements.

3D automatic anatomy segmentation based on iterative graph-cut-ASM.

Science.gov (United States)

Chen, Xinjian; Bagci, Ulas

2011-08-01

all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

3D automatic anatomy segmentation based on iterative graph-cut-ASM

International Nuclear Information System (INIS)

Chen, Xinjian; Bagci, Ulas

2011-01-01

all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. Conclusions: The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model.

Science.gov (United States)

Yang, Zhen; Bogovic, John A; Carass, Aaron; Ye, Mao; Searson, Peter C; Prince, Jerry L

2013-03-13

With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images.

Science.gov (United States)

Tian, Jing; Marziliano, Pina; Baskaran, Mani; Tun, Tin Aung; Aung, Tin

2013-03-01

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch's membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch's membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra's algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice's Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.

Intrinsic properties of channel network structure and the hierarchical classification approach for stream-limits delineation

Energy Technology Data Exchange (ETDEWEB)

Afana, A.; Barrio, G. del

2009-07-01

Delineation of drainage networks is an essential task in hydrological and geomorphologic analysis. Manual channel definition depends on topographic contrast and is highly subjective, leading to important errors at high resolutions. different automatic methods have proposed the use of a constant threshold of up sole contributing are to define channel initiation. Actually, these are the most commonly used for the automatic-channel network extraction from Digital Models (DEMs). However, these methods fall to detect and appropriate threshold when the basin is made up to heterogeneous sub-zones, as they only work either lumped or locally. In this study, the critical threshold area for channel delineation has been defined through the analysis of dominant geometric and topologic properties of stream network formation. (Author) 5 refs.

Using Cut-off grade isograms to delineate ore body and calculate parameter

International Nuclear Information System (INIS)

Yu Yongfeng; Zhu Xiaobing; Deng Yonghui

2014-01-01

Taking a uranium mine for an example, using cut-off grade isograms to achieve automatic delineation of ore body and calculation of parameters are explored. With center line of catalog sampling as baseline, the number of sampling and length of sampling constructing rectangular grid and grade as elevation value, isograms of cut-off grade were drawn, thus achieving the delineation of the ore body. Then, the other parameters of the ore body can be calculated. Compared with the traditional hand drawing method, the work efficiency was greatly improved, and the material inquiry was more convenient. (authors)

Validation of simple quantification methods for 18F FP CIT PET Using Automatic Delineation of volumes of interest based on statistical probabilistic anatomical mapping and isocontour margin setting

International Nuclear Information System (INIS)

Kim, Yong Il; Im, Hyung Jun; Paeng, Jin Chul; Lee, Jae Sung; Eo, Jae Seon; Kim, Dong Hyun; Kim, Euishin E.; Kang, Keon Wook; Chung, June Key; Lee Dong Soo

2012-01-01

18 F FP CIT positron emission tomography (PET) is an effective imaging for dopamine transporters. In usual clinical practice, 18 F FP CIT PET is analyzed visually or quantified using manual delineation of a volume of interest (VOI) fir the stratum. in this study, we suggested and validated two simple quantitative methods based on automatic VOI delineation using statistical probabilistic anatomical mapping (SPAM) and isocontour margin setting. Seventy five 18 F FP CIT images acquired in routine clinical practice were used for this study. A study-specific image template was made and the subject images were normalized to the template. afterwards, uptakes in the striatal regions and cerebellum were quantified using probabilistic VOI based on SPAM. A quantitative parameter, Q SPAM, was calculated to simulate binding potential. additionally, the functional volume of each striatal region and its uptake were measured in automatically delineated VOI using isocontour margin setting. Uptake volume product(Q UVP) was calculated for each striatal region. Q SPAMa nd Q UVPw as calculated for each visual grading and the influence of cerebral atrophy on the measurements was tested. Image analyses were successful in all the cases. Both the Q SPAMa nd Q UVPw ere significantly different according to visual grading (0.001). The agreements of Q UVPa nd Q SPAMw ith visual grading were slight to fair for the caudate nucleus (K= 0.421 and 0.291, respectively) and good to prefect to the putamen (K=0.663 and 0.607, respectively). Also, Q SPAMa nd Q UVPh ad a significant correlation with each other (0.001). Cerebral atrophy made a significant difference in Q SPAMa nd Q UVPo f the caudate nuclei regions with decreased 18 F FP CIT uptake. Simple quantitative measurements of Q SPAMa nd Q UVPs howed acceptable agreement with visual grad-ing. although Q SPAMi n some group may be influenced by cerebral atrophy, these simple methods are expected to be effective in the quantitative analysis of F FP

Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology

International Nuclear Information System (INIS)

Hatt, Mathieu; Visvikis, Dimitris; Tixier, Florent; Albarghach, Nidal M.; Pradier, Olivier; Cheze-le Rest, Catherine

2011-01-01

18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) image-derived parameters, such as standardized uptake value (SUV), functional tumour length (TL) and tumour volume (TV) or total lesion glycolysis (TLG), may be useful for determining prognosis in patients with oesophageal carcinoma. The objectives of this work were to investigate the prognostic value of these indices in oesophageal cancer patients undergoing combined chemoradiotherapy treatment and the impact of TV delineation strategies. A total of 45 patients were retrospectively analysed. Tumours were delineated on pretreatment 18 F-FDG scans using adaptive threshold and automatic (fuzzy locally adaptive Bayesian, FLAB) methodologies. The maximum standardized uptake value (SUV max ), SUV peak , SUV mean , TL, TV and TLG were computed. The prognostic value of each parameter for overall survival was investigated using Kaplan-Meier and Cox regression models for univariate and multivariate analyses, respectively. Large differences were observed between methodologies (from -140 to +50% for TV). SUV measurements were not significant prognostic factors for overall survival, whereas TV, TL and TLG were, irrespective of the segmentation strategy. After multivariate analysis including standard tumour staging, only TV (p < 0.002) and TL (p = 0.042) determined using FLAB were independent prognostic factors. Whereas no SUV measurement was a significant prognostic factor, TV, TL and TLG were significant prognostic factors for overall survival, irrespective of the delineation methodology. Only functional TV and TL derived using FLAB were independent prognostic factors, highlighting the need for accurate and robust PET tumour delineation tools for oncology applications. (orig.)

Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

Science.gov (United States)

Daisne, Jean-François; Blumhofer, Andreas

2013-06-26

Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

Robust automatic high resolution segmentation of SOFC anode porosity in 3D

DEFF Research Database (Denmark)

Jørgensen, Peter Stanley; Bowen, Jacob R.

2008-01-01

Routine use of 3D characterization of SOFCs by focused ion beam (FIB) serial sectioning is generally restricted by the time consuming task of manually delineating structures within each image slice. We apply advanced image analysis algorithms to automatically segment the porosity phase of an SOFC...... anode in 3D. The technique is based on numerical approximations to partial differential equations to evolve a 3D surface to the desired phase boundary. Vector fields derived from the experimentally acquired data are used as the driving force. The automatic segmentation compared to manual delineation...... reveals and good correspondence and the two approaches are quantitatively compared. It is concluded that the. automatic approach is more robust, more reproduceable and orders of magnitude quicker than manual segmentation of SOFC anode porosity for subsequent quantitative 3D analysis. Lastly...

A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems.

Science.gov (United States)

Bote, Jose Manuel; Recas, Joaquin; Rincon, Francisco; Atienza, David; Hermida, Roman

2018-03-01

This work presents a new modular and low-complexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets, and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform real-time delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in runtime to a wide range of modes and sampling rates, from a ultralow-power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete high-accuracy delineation mode, in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in the case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography Committee in the high-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultralow-power 8-MHz TI MSP430 series microcontroller ranges from 0.2% to 8.5% according to the mode used.

The Use of Remote Sensing and Gis For Catchment Delineation in Northwestern Coast of Egypt: An Assessment of Water Resources and Soil Potential

International Nuclear Information System (INIS)

El BastaWesy, M.A.; NASR, A.H.; Ali, R.R.

2008-01-01

The manual delineation of drainage networks and catchment from topographic maps has widely been replaced by the automatic extraction from Digital Elevation Model (DEM) using different processing algorithms. The automatic extraction requires first removing all the sinks (depressions) in the DEM by filling their elevation to the nearest neighbouring cells. The sinkholes are true inherited landscape in the karstified Marmarica Limestone Plateau covering the northwestern coast of Egypt. Following the traditional methods of automatic extraction all the catchment outlets are located on the Mediterranean coast, but the centripetal catchment on the plateau surface cannot be delineated. A new technique is presented on how to delineate these centripetal catchment along with the coastal catchment, by masking the true sinks layer derived from topographic maps and satellite images from the DEM throughout the delineation process. The analysis of Tropical Rainfall Monitoring Mission (TRMM) data reveals that these centripetal catchment of the study area receive more precipitation than the coastal ones in contrary of the previous extrapolated isohyets maps. The runoff and soil potential for one of these centripetal catchment were initially assessed. The estimated average annual surface runoff is 1.8 million m 3 and the soils are moderate to marginally suitable for citrus, peach, olives, wheat, sunflower and alfalfa

Influence of experience and qualification on PET-based target volume delineation. When there is no expert - ask your colleague

International Nuclear Information System (INIS)

Doll, C.; Grosu, A.L.; Nestle, U.; Duncker-Rohr, V.; Ruecker, G.; Mix, M.; MacManus, M.; Ruysscher, D. de; Vogel, W.; Eriksen, J.G.; Oyen, W.; Weber, W.

2014-01-01

The integration of positron emission tomography (PET) information for target volume delineation in radiation treatment planning is routine in many centers. In contrast to automatic contouring, research on visual-manual delineation is scarce. The present study investigates the dependency of manual delineation on experience and qualification. A total of 44 international interdisciplinary observers each defined a [ 18 F]fluorodeoxyglucose (FDG)-PET based gross tumor volume (GTV) using the same PET/CT scan from a patient with lung cancer. The observers were ''experts'' (E; n = 3), ''experienced interdisciplinary pairs'' (EP; 9 teams of radiation oncologist (RO) + nuclear medicine physician (NP)), ''single field specialists'' (SFS; n = 13), and ''students'' (S; n = 10). Five automatic delineation methods (AM) were also included. Volume sizes and concordance indices within the groups (pCI) and relative to the experts (eCI) were calculated. E (pCI = 0.67) and EP (pCI = 0.53) showed a significantly higher agreement within the groups as compared to SFS (pCI = 0.43, p = 0.03, and p = 0.006). In relation to the experts, EP (eCI = 0.55) showed better concordance compared to SFS (eCI = 0.49) or S (eCI = 0.47). The intermethod variability of the AM (pCI = 0.44) was similar to that of SFS and S, showing poorer agreement with the experts (eCI = 0.35). The results suggest that interdisciplinary cooperation could be beneficial for consistent contouring. Joint delineation by a radiation oncologist and a nuclear medicine physician showed remarkable agreement and better concordance with the experts compared to other specialists. The relevant intermethod variability of the automatic algorithms underlines the need for further standardization and optimization in this field. (orig.) [de

Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation

International Nuclear Information System (INIS)

Daisne, Jean-François; Blumhofer, Andreas

2013-01-01

Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for “manual to automatic” and “manual to corrected” volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert

Automatic re-contouring in 4D radiotherapy

International Nuclear Information System (INIS)

Lu, Weiguo; Olivera, Gustavo H; Chen, Quan; Chen, Ming-Li; Ruchala, Kenneth J

2006-01-01

Delineating regions of interest (ROIs) on each phase of four-dimensional (4D) computed tomography (CT) images is an essential step for 4D radiotherapy. The requirement of manual phase-by-phase contouring prohibits the routine use of 4D radiotherapy. This paper develops an automatic re-contouring algorithm that combines techniques of deformable registration and surface construction. ROIs are manually contoured slice-by-slice in the reference phase image. A reference surface is constructed based on these reference contours using a triangulated surface construction technique. The deformable registration technique provides the voxel-to-voxel mapping between the reference phase and the test phase. The vertices of the reference surface are displaced in accordance with the deformation map, resulting in a deformed surface. The new contours are reconstructed by cutting the deformed surface slice-by-slice along the transversal, sagittal or coronal direction. Since both the inputs and outputs of our automatic re-contouring algorithm are contours, it is relatively easy to cope with any treatment planning system. We tested our automatic re-contouring algorithm using a deformable phantom and 4D CT images of six lung cancer patients. The proposed algorithm is validated by visual inspections and quantitative comparisons of the automatic re-contours with both the gold standard segmentations and the manual contours. Based on the automatic delineated ROIs, changes of tumour and sensitive structures during respiration are quantitatively analysed. This algorithm could also be used to re-contour daily images for treatment evaluation and adaptive radiotherapy

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

Science.gov (United States)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Automatic differentiation bibliography

Energy Technology Data Exchange (ETDEWEB)

Corliss, G.F. [comp.

1992-07-01

This is a bibliography of work related to automatic differentiation. Automatic differentiation is a technique for the fast, accurate propagation of derivative values using the chain rule. It is neither symbolic nor numeric. Automatic differentiation is a fundamental tool for scientific computation, with applications in optimization, nonlinear equations, nonlinear least squares approximation, stiff ordinary differential equation, partial differential equations, continuation methods, and sensitivity analysis. This report is an updated version of the bibliography which originally appeared in Automatic Differentiation of Algorithms: Theory, Implementation, and Application.

A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal

International Nuclear Information System (INIS)

Burnett, Stuart S.C.; Starkschall, George; Stevens, Craig W.; Liao Zhongxing

2004-01-01

Because of the importance of accurately defining the target in radiation treatment planning, we have developed a deformable-template algorithm for the semi-automatic delineation of normal tissue structures on computed tomography (CT) images. We illustrate the method by applying it to the spinal canal. Segmentation is performed in three steps: (a) partial delineation of the anatomic structure is obtained by wavelet-based edge detection; (b) a deformable-model template is fitted to the edge set by chamfer matching; and (c) the template is relaxed away from its original shape into its final position. Appropriately chosen ranges for the model parameters limit the deformations of the template, accounting for interpatient variability. Our approach differs from those used in other deformable models in that it does not inherently require the modeling of forces. Instead, the spinal canal was modeled using Fourier descriptors derived from four sets of manually drawn contours. Segmentation was carried out, without manual intervention, on five CT data sets and the algorithm's performance was judged subjectively by two radiation oncologists. Two assessments were considered: in the first, segmentation on a random selection of 100 axial CT images was compared with the corresponding contours drawn manually by one of six dosimetrists, also chosen randomly; in the second assessment, the segmentation of each image in the five evaluable CT sets (a total of 557 axial images) was rated as either successful, unsuccessful, or requiring further editing. Contours generated by the algorithm were more likely than manually drawn contours to be considered acceptable by the oncologists. The mean proportions of acceptable contours were 93% (automatic) and 69% (manual). Automatic delineation of the spinal canal was deemed to be successful on 91% of the images, unsuccessful on 2% of the images, and requiring further editing on 7% of the images. Our deformable template algorithm thus gives a robust

Tools to analyse and display variations in anatomical delineation

International Nuclear Information System (INIS)

Ebert, Martin A.; McDermott, L.N.; Haworth, A; Van der Wath, E.; Hooton, B.

2012-01-01

Variations in anatomical delineation, principally due to a combination of inter-observer contributions and image-specificity, remain one of the most significant impediments to geometrically-accurate radiotherapy. Quantification of spatial variability of the delineated contours comprising a structure can be made with a variety of metrics, and the availability of software tools to apply such metrics to data collected during inter-observer or repeat-imaging studies would allow their validation. A suite of such tools have been developed which use an Extensible Markup Language format for the exchange of delineated 3D structures with radiotherapy planning or review systems. These tools provide basic operations for manipulating and operating on individual structures and related structure sets, and for deriving statistics on spatial variations of contours that can be mapped onto the surface of a reference structure. Use of these tools on a sample dataset is demonstrated together with import and display of results in the SWAN treatment plan review system.

Detection and delineation of underground septic tanks in sandy terrain using ground penetrating radar

Science.gov (United States)

Omolaiye, Gabriel Efomeh; Ayolabi, Elijah A.

2010-09-01

A ground penetrating radar (GPR) survey was conducted on the Lekki Peninsula, Lagos State, Nigeria. The primary target of the survey was the delineation of underground septic tanks (ST). A total of four GPR profiles were acquired on the survey site using Ramac X3M GPR equipment with a 250MHz antenna, chosen based on the depth of interest and resolution. An interpretable depth of penetration of 4.5m below the surface was achieved after processing. The method accurately delineated five underground ST. The tops of the ST were easily identified on the radargram based on the strong-amplitude anomalies, the length and the depths to the base of the ST were estimated with 99 and 73 percent confidence respectively. The continuous vertical profiles provide uninterrupted subsurface data along the lines of traverse, while the non-intrusive nature makes it an ideal tool for the accurate mapping and delineation of underground utilities.

Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

Science.gov (United States)

Karami, Elham; Gaede, Stewart; Lee, Ting-Yim; Samani, Abbas

2015-03-01

The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach.

Science.gov (United States)

Hui, Cheukkai B; Nourzadeh, Hamidreza; Watkins, William T; Trifiletti, Daniel M; Alonso, Clayton E; Dutta, Sunil W; Siebers, Jeffrey V

2018-02-26

To develop a quality assurance (QA) tool that identifies inaccurate organ at risk (OAR) delineations. The QA tool computed volumetric features from prior OAR delineation data from 73 thoracic patients to construct a reference database. All volumetric features of the OAR delineation are computed in three-dimensional space. Volumetric features of a new OAR are compared with respect to those in the reference database to discern delineation outliers. A multicriteria outlier detection system warns users of specific delineation outliers based on combinations of deviant features. Fifteen independent experimental sets including automatic, propagated, and clinically approved manual delineation sets were used for verification. The verification OARs included manipulations to mimic common errors. Three experts reviewed the experimental sets to identify and classify errors, first without; and then 1 week after with the QA tool. In the cohort of manual delineations with manual manipulations, the QA tool detected 94% of the mimicked errors. Overall, it detected 37% of the minor and 85% of the major errors. The QA tool improved reviewer error detection sensitivity from 61% to 68% for minor errors (P = 0.17), and from 78% to 87% for major errors (P = 0.02). The QA tool assists users to detect potential delineation errors. QA tool integration into clinical procedures may reduce the frequency of inaccurate OAR delineation, and potentially improve safety and quality of radiation treatment planning. © 2018 American Association of Physicists in Medicine.

Radio-anatomy Atlas for delineation SIRIADE web site: features and 1 year results

International Nuclear Information System (INIS)

Denisa, F.; Pointreau, Y.

2010-01-01

3-D conformal radiotherapy is based on accurate target volumes delineation. Radio-anatomy knowledge's are useful but sometimes difficult to obtain. Moreover, the sources of recommendations for volume definition are disparate. We thus developed a free radio-anatomy web site dedicated to volumes delineation for radiation-oncologists (www.siriade.org). This web site is a search engine allowing to access to delineation characteristics of main tumours illustrated with clinical cases. It does not aim to provide guidelines. Its main purpose is to provide an iconographic training support with frequent up-datings. We present the features of this web site and one year connexion statistics. (authors)

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

International Nuclear Information System (INIS)

Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Vermandel, Maximilien; Baillet, Clio

2015-01-01

Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging.Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used.Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results.The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging. (paper)

Is STAPLE algorithm confident to assess segmentation methods in PET imaging?

Science.gov (United States)

Dewalle-Vignion, Anne-Sophie; Betrouni, Nacim; Baillet, Clio; Vermandel, Maximilien

2015-12-01

Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians’ manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging. Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used. Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results. The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

Radio-anatomy Atlas for delineation SIRIADE web site: features and 1 year results; Site de radio-anatomie et d'aide a la delineation (SIRIADE): presentation et bilan a un an

Energy Technology Data Exchange (ETDEWEB)

Denisa, F. [Centre Jean-Bernard, Clinique Victor-Hugo, 72 - Le Mans (France); Pointreau, Y. [Clinique d' oncologie radiotherapie, Centre Henry-S.-Kaplan, CHU Bretonneau, 37 - Tours (France)

2010-07-01

3-D conformal radiotherapy is based on accurate target volumes delineation. Radio-anatomy knowledge's are useful but sometimes difficult to obtain. Moreover, the sources of recommendations for volume definition are disparate. We thus developed a free radio-anatomy web site dedicated to volumes delineation for radiation-oncologists (www.siriade.org). This web site is a search engine allowing to access to delineation characteristics of main tumours illustrated with clinical cases. It does not aim to provide guidelines. Its main purpose is to provide an iconographic training support with frequent up-datings. We present the features of this web site and one year connexion statistics. (authors)

A local contrast based approach to threshold segmentation for PET target volume delineation

International Nuclear Information System (INIS)

Drever, Laura; Robinson, Don M.; McEwan, Alexander; Roa, Wilson

2006-01-01

Current radiation therapy techniques, such as intensity modulated radiation therapy and three-dimensional conformal radiotherapy rely on the precise delivery of high doses of radiation to well-defined volumes. CT, the imaging modality that is most commonly used to determine treatment volumes cannot, however, easily distinguish between cancerous and normal tissue. The ability of positron emission tomography (PET) to more readily differentiate between malignant and healthy tissues has generated great interest in using PET images to delineate target volumes for radiation treatment planning. At present the accurate geometric delineation of tumor volumes is a subject open to considerable interpretation. The possibility of using a local contrast based approach to threshold segmentation to accurately delineate PET target cross sections is investigated using well-defined cylindrical and spherical volumes. Contrast levels which yield correct volumetric quantification are found to be a function of the activity concentration ratio between target and background, target size, and slice location. Possibilities for clinical implementation are explored along with the limits posed by this form of segmentation

Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models

Science.gov (United States)

Matsumoto, Monica M. S.; Beig, Niha G.; Udupa, Jayaram K.; Archer, Steven; Torigian, Drew A.

2014-03-01

Lung cancer is associated with the highest cancer mortality rates among men and women in the United States. The accurate and precise identification of the lymph node stations on computed tomography (CT) images is important for staging disease and potentially for prognosticating outcome in patients with lung cancer, as well as for pretreatment planning and response assessment purposes. To facilitate a standard means of referring to lymph nodes, the International Association for the Study of Lung Cancer (IASLC) has recently proposed a definition of the different lymph node stations and zones in the thorax. However, nodal station identification is typically performed manually by visual assessment in clinical radiology. This approach leaves room for error due to the subjective and potentially ambiguous nature of visual interpretation, and is labor intensive. We present a method of automatically recognizing the mediastinal IASLC-defined lymph node stations by modifying a hierarchical fuzzy modeling approach previously developed for body-wide automatic anatomy recognition (AAR) in medical imagery. Our AAR-lymph node (AAR-LN) system follows the AAR methodology and consists of two steps. In the first step, the various lymph node stations are manually delineated on a set of CT images following the IASLC definitions. These delineations are then used to build a fuzzy hierarchical model of the nodal stations which are considered as 3D objects. In the second step, the stations are automatically located on any given CT image of the thorax by using the hierarchical fuzzy model and object recognition algorithms. Based on 23 data sets used for model building, 22 independent data sets for testing, and 10 lymph node stations, a mean localization accuracy of within 1-6 voxels has been achieved by the AAR-LN system.

Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma.

Science.gov (United States)

Ciller, Carlos; De Zanet, Sandro I; Rüegsegger, Michael B; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L; Kowal, Jens H; Cuadra, Meritxell Bach

2015-07-15

Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor. Copyright © 2015 Elsevier Inc. All rights reserved.

A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

Science.gov (United States)

Rios Velazquez, Emmanuel; Aerts, Hugo J W L; Gu, Yuhua; Goldgof, Dmitry B; De Ruysscher, Dirk; Dekker, Andre; Korn, René; Gillies, Robert J; Lambin, Philippe

2012-11-01

To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC). For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org. High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96). Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Delineation, characterization, and classification of topographic eminences

Science.gov (United States)

Sinha, Gaurav

Topographic eminences are defined as upwardly rising, convex shaped topographic landforms that are noticeably distinct in their immediate surroundings. As opposed to everyday objects, the properties of a topographic eminence are dependent not only on how it is conceptualized, but is also intrinsically related to its spatial extent and its relative location in the landscape. In this thesis, a system for automated detection, delineation and characterization of topographic eminences based on an analysis of digital elevation models is proposed. Research has shown that conceptualization of eminences (and other landforms) is linked to the cultural and linguistic backgrounds of people. However, the perception of stimuli from our physical environment is not subject to cultural or linguistic bias. Hence, perceptually salient morphological and spatial properties of the natural landscape can form the basis for generically applicable detection and delineation of topographic eminences. Six principles of cognitive eminence modeling are introduced to develop the philosophical foundation of this research regarding eminence delineation and characterization. The first step in delineating eminences is to automatically detect their presence within digital elevation models. This is achieved by the use of quantitative geomorphometric parameters (e.g., elevation, slope and curvature) and qualitative geomorphometric features (e.g., peaks, passes, pits, ridgelines, and valley lines). The process of eminence delineation follows that of eminence detection. It is posited that eminences may be perceived either as monolithic terrain objects, or as composites of morphological parts (e.g., top, bottom, slope). Individual eminences may also simultaneously be conceived as comprising larger, higher order eminence complexes (e.g., mountain ranges). Multiple algorithms are presented for the delineation of simple and complex eminences, and the morphological parts of eminences. The proposed eminence

Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing

Directory of Open Access Journals (Sweden)

Kwang Baek Kim

2015-01-01

Full Text Available Ultrasound examination (US does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases in extracting appendix.

Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices

Science.gov (United States)

Kelly, Joshua T.; Gontz, Allen M.

2018-03-01

Satellite remote sensing has been used extensively in a variety of shoreline studies and validated using aerial photography. This ground truth method only represents an instantaneous depiction of the shoreline at the time of acquisition and does not take into account the spatial and temporal variability of the dynamic shoreline boundary. Landsat 8‧s Operational Land Imager sensor's capability to accurately delineate a shoreline is assessed by comparing all known Landsat water index-derived shorelines with two GPS-surveyed intertidal zones that coincide with the satellite flyover date, one of which had near-neap tide conditions. Seven indices developed for automatically classifying water pixels were evaluated for their ability to delineate shorelines. The shoreline is described here as the area above and below maximum low and high tide, otherwise known as the intertidal zone. The high-water line, or wet/dry sediment line, was chosen as the shoreline indicator to be mapped using a handheld GPS. The proportion of the Landsat-derived shorelines that fell within this zone and their alongshore profile lengths were calculated. The most frequently used water index and the predecessor to Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), was found to be the least accurate by a significant margin. Other indices required calibration of their threshold value to achieve accurate results, thus diminishing their replicability success for other regions. MNDWI was determined to be the best index for automated shoreline mapping, based on its superior accuracy and repeatable, stable threshold value.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

Science.gov (United States)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Riparian ecotone: A functional definition and delineation for resource assessment

Science.gov (United States)

E. S Verry; C. A Dolloff; M. E. Manning

2004-01-01

We propose a geomorphic basis for defining riparian areas using the term: riparian ecotone, discuss how past definitions fall short, and illustrate how a linked sequence of definition, delineation, and riparian sampling are used to accurately assess riparian resources on the ground. Our riparian ecotone is based on the width of the valley (its floodprone area width)...

SU-E-J-134: Optimizing Technical Parameters for Using Atlas Based Automatic Segmentation for Evaluation of Contour Accuracy Experience with Cardiac Structures From NRG Oncology/RTOG 0617

Energy Technology Data Exchange (ETDEWEB)

Yu, J; Gong, Y; Bar-Ad, V; Giaddui, T; Galvin, J; Xiao, Y [Thomas Jefferson University, Philadelphia, PA (United States); Hu, C [NRG oncology, Philadelphia, PA (United States); Gore, E; Wheatley, M [Medical College of Wisconsin, Milwaukee, WI (United States); Witt, J; Robinson, C; Bradley, J [Washington University in St. Louis School of Medicine, St. Louis, MO (United States); Kong, F [Georgia Regents University, Augusta, GA (Georgia)

2015-06-15

Purpose: Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation. Methods: Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeen patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five-patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five-patient averaged DSCs were also calculated for each cardiac structure. Results: DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles. Conclusion: By comparing DSC values, the combination AN=4 and LS=100 gives the best performance. This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol-Myers Squibb and Eli Lilly.

PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

D. Koc-San

2016-06-01

Full Text Available Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML, Random Forest (RF, and Support Vector Machines (SVM classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

Fast, accurate, and robust automatic marker detection for motion correction based on oblique kV or MV projection image pairs

International Nuclear Information System (INIS)

Slagmolen, Pieter; Hermans, Jeroen; Maes, Frederik; Budiharto, Tom; Haustermans, Karin; Heuvel, Frank van den

2010-01-01

Purpose: A robust and accurate method that allows the automatic detection of fiducial markers in MV and kV projection image pairs is proposed. The method allows to automatically correct for inter or intrafraction motion. Methods: Intratreatment MV projection images are acquired during each of five treatment beams of prostate cancer patients with four implanted fiducial markers. The projection images are first preprocessed using a series of marker enhancing filters. 2D candidate marker locations are generated for each of the filtered projection images and 3D candidate marker locations are reconstructed by pairing candidates in subsequent projection images. The correct marker positions are retrieved in 3D by the minimization of a cost function that combines 2D image intensity and 3D geometric or shape information for the entire marker configuration simultaneously. This optimization problem is solved using dynamic programming such that the globally optimal configuration for all markers is always found. Translational interfraction and intrafraction prostate motion and the required patient repositioning is assessed from the position of the centroid of the detected markers in different MV image pairs. The method was validated on a phantom using CT as ground-truth and on clinical data sets of 16 patients using manual marker annotations as ground-truth. Results: The entire setup was confirmed to be accurate to around 1 mm by the phantom measurements. The reproducibility of the manual marker selection was less than 3.5 pixels in the MV images. In patient images, markers were correctly identified in at least 99% of the cases for anterior projection images and 96% of the cases for oblique projection images. The average marker detection accuracy was 1.4±1.8 pixels in the projection images. The centroid of all four reconstructed marker positions in 3D was positioned within 2 mm of the ground-truth position in 99.73% of all cases. Detecting four markers in a pair of MV images

Automatic design of magazine covers

Science.gov (United States)

Jahanian, Ali; Liu, Jerry; Tretter, Daniel R.; Lin, Qian; Damera-Venkata, Niranjan; O'Brien-Strain, Eamonn; Lee, Seungyon; Fan, Jian; Allebach, Jan P.

2012-03-01

In this paper, we propose a system for automatic design of magazine covers that quantifies a number of concepts from art and aesthetics. Our solution to automatic design of this type of media has been shaped by input from professional designers, magazine art directors and editorial boards, and journalists. Consequently, a number of principles in design and rules in designing magazine covers are delineated. Several techniques are derived and employed in order to quantify and implement these principles and rules in the format of a software framework. At this stage, our framework divides the task of design into three main modules: layout of magazine cover elements, choice of color for masthead and cover lines, and typography of cover lines. Feedback from professional designers on our designs suggests that our results are congruent with their intuition.

Automatic anatomy recognition via multiobject oriented active shape models.

Science.gov (United States)

Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

2010-12-01

This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a

A whole body atlas for segmentation and delineation of organs for radiation therapy planning

International Nuclear Information System (INIS)

Qatarneh, S.M.; Crafoord, J.; Kramer, E.L.; Maguire, G.Q.; Brahme, A.; Noz, M.E.; Hyoedynmaa, S.

2001-01-01

A semi-automatic procedure for delineation of organs to be used as the basis of a whole body atlas database for radiation therapy planning was developed. The Visible Human Male Computed Tomography (CT)-data set was used as a 'standard man' reference. The organ of interest was outlined manually and then transformed by a polynomial warping algorithm onto a clinical patient CT. This provided an initial contour, which was then adjusted and refined by the semi-automatic active contour model to find the final organ outline. The liver was used as a test organ for evaluating the performance of the procedure. Liver outlines obtained by the segmentation algorithm on six patients were compared to those manually drawn by a radiologist. The combination of warping and semi-automatic active contour model generally provided satisfactory segmentation results, but the procedure has to be extended to three dimensions

A new automatic blood pressure kit auscultates for accurate reading with a smartphone

Science.gov (United States)

Wu, Hongjun; Wang, Bingjian; Zhu, Xinpu; Chu, Guang; Zhang, Zhi

2016-01-01

Abstract The widely used oscillometric automated blood pressure (BP) monitor was continuously questioned on its accuracy. A novel BP kit named Accutension which adopted Korotkoff auscultation method was then devised. Accutension worked with a miniature microphone, a pressure sensor, and a smartphone. The BP values were automatically displayed on the smartphone screen through the installed App. Data recorded in the phone could be played back and reconfirmed after measurement. They could also be uploaded and saved to the iCloud. The accuracy and consistency of this novel electronic auscultatory sphygmomanometer was preliminarily verified here. Thirty-two subjects were included and 82 qualified readings were obtained. The mean differences ± SD for systolic and diastolic BP readings between Accutension and mercury sphygmomanometer were 0.87 ± 2.86 and −0.94 ± 2.93 mm Hg. Agreements between Accutension and mercury sphygmomanometer were highly significant for systolic (ICC = 0.993, 95% confidence interval (CI): 0.989–0.995) and diastolic (ICC = 0.987, 95% CI: 0.979–0.991). In conclusion, Accutension worked accurately based on our pilot study data. The difference was acceptable. ICC and Bland–Altman plot charts showed good agreements with manual measurements. Systolic readings of Accutension were slightly higher than those of manual measurement, while diastolic readings were slightly lower. One possible reason was that Accutension captured the first and the last korotkoff sound more sensitively than human ear during manual measurement and avoided sound missing, so that it might be more accurate than traditional mercury sphygmomanometer. By documenting and analyzing of variant tendency of BP values, Accutension helps management of hypertension and therefore contributes to the mobile heath service. PMID:27512876

The application value of diffusion-weighted magnetic resonance imaging in gross tumor volume delineation of esophageal squamous cell carcinoma

International Nuclear Information System (INIS)

Hou Dongliang; Shi Gaofeng; Gao Xianshu

2012-01-01

Objective: To analyze the application value of diffusion-weighted magnetic resonance imaging (DWMRI) in gross tumor volume (GTV) delineation of esophageal squamous cell carcinoma (SCC). Methods: Twenty-nine patients with esophageal SCC treated with radical surgery were analyzed. Routine CT scan, MRI T 2 -weighted and DWMRI were employed before surgery; diffusion-sensitive gradient b-values were taken 400, 600 and 800 s/mm 2 . GTVs were delineated using CT, MRI T 2 -weighted images and DWMRI under different b-value images. The length of GTVs measured under different images was compared with the pathological length and confirm the most accurate imaging condition. Use radiotherapy planning system to fuse DWMRI images and CT images to investigate the possibility of delineate GTVs on fused images. Results: The difference of GTV length value between CT, T 2 WI images and specimen was 3.36 mm and 2.84 mm. When b =400,600 and 800 s/mm 2 , the difference between GTV length value on the DWMRI images and on specimen was 0.47 mm, -0.47 mm and - 1.53 mm; the correlation coefficient of the measuring esophageal lengths on DWMRI images and the pathological lengths was 0.928, 0.927 and 0.938. DWMRI images and CT images could fuse accurately on radiotherapy planning system. GTV margin could.show clearly on fused images. Conclusions: DWMRI images can display the esophageal carcinoma lengths and margin accurately. When DWMRI images fused with CT images, GTV margin could show clearly,it can be used to delineate GTV accurately. (authors)

Optical system for object detection and delineation in space

Science.gov (United States)

Handelman, Amir; Shwartz, Shoam; Donitza, Liad; Chaplanov, Loran

2018-01-01

Object recognition and delineation is an important task in many environments, such as in crime scenes and operating rooms. Marking evidence or surgical tools and attracting the attention of the surrounding staff to the marked objects can affect people's lives. We present an optical system comprising a camera, computer, and small laser projector that can detect and delineate objects in the environment. To prove the optical system's concept, we show that it can operate in a hypothetical crime scene in which a pistol is present and automatically recognize and segment it by various computer-vision algorithms. Based on such segmentation, the laser projector illuminates the actual boundaries of the pistol and thus allows the persons in the scene to comfortably locate and measure the pistol without holding any intermediator device, such as an augmented reality handheld device, glasses, or screens. Using additional optical devices, such as diffraction grating and a cylinder lens, the pistol size can be estimated. The exact location of the pistol in space remains static, even after its removal. Our optical system can be fixed or dynamically moved, making it suitable for various applications that require marking of objects in space.

Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

Energy Technology Data Exchange (ETDEWEB)

Ciller, Carlos, E-mail: carlos.cillerruiz@unil.ch [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Centre d’Imagerie BioMédicale, University of Lausanne, Lausanne (Switzerland); De Zanet, Sandro I.; Rüegsegger, Michael B. [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); Pica, Alessia [Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern (Switzerland); Sznitman, Raphael [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); Thiran, Jean-Philippe [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Signal Processing Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland); Maeder, Philippe [Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne (Switzerland); Munier, Francis L. [Unit of Pediatric Ocular Oncology, Jules Gonin Eye Hospital, Lausanne (Switzerland); Kowal, Jens H. [Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern (Switzerland); Department of Ophthalmology, Inselspital, Bern University Hospital, Bern (Switzerland); and others

2015-07-15

Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

International Nuclear Information System (INIS)

Ciller, Carlos; De Zanet, Sandro I.; Rüegsegger, Michael B.; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L.; Kowal, Jens H.

2015-01-01

Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor

Impact of the accuracy of automatic tumour functional volume delineation on radiotherapy treatment planning

International Nuclear Information System (INIS)

Le Maitre, Amandine; Hatt, Mathieu; Pradier, Olivier; Cheze-le Rest, Catherine; Visvikis, Dimitris

2012-01-01

Over the past few years several automatic and semi-automatic PET segmentation methods for target volume definition in radiotherapy have been proposed. The objective of this study is to compare different methods in terms of dosimetry. For such a comparison, a gold standard is needed. For this purpose, realistic GATE-simulated PET images were used. Three lung cases and three H and N cases were designed with various shapes, contrasts and heterogeneities. Four different segmentation approaches were compared: fixed and adaptive thresholds, a fuzzy C-mean and the fuzzy locally adaptive Bayesian method. For each of these target volumes, an IMRT treatment plan was defined. The different algorithms and resulting plans were compared in terms of segmentation errors and ground-truth volume coverage using different metrics (V 95 , D 95 , homogeneity index and conformity index). The major differences between the threshold-based methods and automatic methods occurred in the most heterogeneous cases. Within the two groups, the major differences occurred for low contrast cases. For homogeneous cases, equivalent ground-truth volume coverage was observed for all methods but for more heterogeneous cases, significantly lower coverage was observed for threshold-based methods. Our study demonstrates that significant dosimetry errors can be avoided by using more advanced image-segmentation methods. (paper)

Automatic CT simulation optimization for radiation therapy: A general strategy

Energy Technology Data Exchange (ETDEWEB)

Li, Hua, E-mail: huli@radonc.wustl.edu; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M.; Mutic, Sasa [Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110 (United States); Yu, Lifeng [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States); Anastasio, Mark A. [Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63110 (United States); Low, Daniel A. [Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095 (United States)

2014-03-15

Purpose: In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. Methods: The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Results: Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube

Automatic CT simulation optimization for radiation therapy: A general strategy.

Science.gov (United States)

Li, Hua; Yu, Lifeng; Anastasio, Mark A; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M; Low, Daniel A; Mutic, Sasa

2014-03-01

In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes

ARCOCT: Automatic detection of lumen border in intravascular OCT images.

Science.gov (United States)

Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

2017-11-01

Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border

Tumor and target delineation: current research and future challenges

International Nuclear Information System (INIS)

Austin-Seymour, Mary; Chen, George T.Y.; Rosenman, Julian; Michalski, Jeff; Lindsley, Karen; Goitein, Michael

1995-01-01

In the past decade, significant progress has been made in the imaging of tumors, three dimensional (3D) treatment planning, and radiation treatment delivery. At this time one of the greatest challenges for conformal radiation therapy is the accurate delineation of tumor and target volumes. The physician encounters many uncertainties in the process of defining both tumor and target. The sources of these uncertainties are discussed, as well as the issues requiring study to reduce these uncertainties

WE-AB-BRA-05: Fully Automatic Segmentation of Male Pelvic Organs On CT Without Manual Intervention

International Nuclear Information System (INIS)

Gao, Y; Lian, J; Chen, R; Wang, A; Shen, D

2015-01-01

Purpose: We aim to develop a fully automatic tool for accurate contouring of major male pelvic organs in CT images for radiotherapy without any manual initialization, yet still achieving superior performance than the existing tools. Methods: A learning-based 3D deformable shape model was developed for automatic contouring. Specifically, we utilized a recent machine learning method, random forest, to jointly learn both image regressor and classifier for each organ. In particular, the image regressor is trained to predict the 3D displacement from each vertex of the 3D shape model towards the organ boundary based on the local image appearance around the location of this vertex. The predicted 3D displacements are then used to drive the 3D shape model towards the target organ. Once the shape model is deformed close to the target organ, it is further refined by an organ likelihood map estimated by the learned classifier. As the organ likelihood map provides good guideline for the organ boundary, the precise contouring Result could be achieved, by deforming the 3D shape model locally to fit boundaries in the organ likelihood map. Results: We applied our method to 29 previously-treated prostate cancer patients, each with one planning CT scan. Compared with manually delineated pelvic organs, our method obtains overlap ratios of 85.2%±3.74% for the prostate, 94.9%±1.62% for the bladder, and 84.7%±1.97% for the rectum, respectively. Conclusion: This work demonstrated feasibility of a novel machine-learning based approach for accurate and automatic contouring of major male pelvic organs. It shows the potential to replace the time-consuming and inconsistent manual contouring in the clinic. Also, compared with the existing works, our method is more accurate and also efficient since it does not require any manual intervention, such as manual landmark placement. Moreover, our method obtained very similar contouring results as the clinical experts. Project is partially support

WE-AB-BRA-05: Fully Automatic Segmentation of Male Pelvic Organs On CT Without Manual Intervention

Energy Technology Data Exchange (ETDEWEB)

Gao, Y; Lian, J; Chen, R; Wang, A; Shen, D [Univ North Carolina, Chapel Hill, NC (United States)

2015-06-15

Purpose: We aim to develop a fully automatic tool for accurate contouring of major male pelvic organs in CT images for radiotherapy without any manual initialization, yet still achieving superior performance than the existing tools. Methods: A learning-based 3D deformable shape model was developed for automatic contouring. Specifically, we utilized a recent machine learning method, random forest, to jointly learn both image regressor and classifier for each organ. In particular, the image regressor is trained to predict the 3D displacement from each vertex of the 3D shape model towards the organ boundary based on the local image appearance around the location of this vertex. The predicted 3D displacements are then used to drive the 3D shape model towards the target organ. Once the shape model is deformed close to the target organ, it is further refined by an organ likelihood map estimated by the learned classifier. As the organ likelihood map provides good guideline for the organ boundary, the precise contouring Result could be achieved, by deforming the 3D shape model locally to fit boundaries in the organ likelihood map. Results: We applied our method to 29 previously-treated prostate cancer patients, each with one planning CT scan. Compared with manually delineated pelvic organs, our method obtains overlap ratios of 85.2%±3.74% for the prostate, 94.9%±1.62% for the bladder, and 84.7%±1.97% for the rectum, respectively. Conclusion: This work demonstrated feasibility of a novel machine-learning based approach for accurate and automatic contouring of major male pelvic organs. It shows the potential to replace the time-consuming and inconsistent manual contouring in the clinic. Also, compared with the existing works, our method is more accurate and also efficient since it does not require any manual intervention, such as manual landmark placement. Moreover, our method obtained very similar contouring results as the clinical experts. Project is partially support

Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

Science.gov (United States)

Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

2017-03-01

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Aspen Delineation - Aspen Delineation Project [ds362

Data.gov (United States)

California Natural Resource Agency — The database represents delineations of aspen stands, where aspen assessment data was gathered. Aspen assessment information corresponding to this polygon layer can...

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

Directory of Open Access Journals (Sweden)

Shiguang Xu

2014-12-01

Full Text Available False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB37V and TB19V, respectively and the sum of TB37V and TB19V derived from the observations of the Tropical Rainfall Measuring Mission (TRMM Microwave Imager (TMI. The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI, a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods. Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets.

Generic and robust method for automatic segmentation of PET images using an active contour model

Energy Technology Data Exchange (ETDEWEB)

Zhuang, Mingzan [Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen (Netherlands)

2016-08-15

Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer’s thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work

A novel method for delineation of oral mucosa for radiotherapy dose–response studies

International Nuclear Information System (INIS)

Dean, Jamie A.; Welsh, Liam C.; Gulliford, Sarah L.; Harrington, Kevin J.; Nutting, Christopher M.

2015-01-01

There is currently no standard method for delineating the oral mucosa and most attempts are oversimplified. A new method to obtain anatomically accurate contours of the oral mucosa surfaces was developed and applied to 11 patients. This is expected to represent an opportunity for improved toxicity modelling of oral mucositis

A framework for automatic segmentation in three dimensions of microstructural tomography data

DEFF Research Database (Denmark)

Jørgensen, Peter Stanley; Hansen, Karin Vels; Larsen, Rasmus

2010-01-01

Routine use of quantitative three dimensional analysis of material microstructure by in particular, focused ion beam (FIB) serial sectioning is generally restricted by the time consuming task of manually delineating structures within each image slice or the quality of manual and automatic...... segmentation schemes. We present here a framework for performing automatic segmentation of complex microstructures using a level set method. The technique is based on numerical approximations to partial differential equations to evolve a 3D surface to capture the phase boundaries. Vector fields derived from...

Semiautomatic segmentation of liver metastases on volumetric CT images

International Nuclear Information System (INIS)

Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng

2015-01-01

Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation

Sequential versus simultaneous market delineation

DEFF Research Database (Denmark)

Haldrup, Niels; Møllgaard, Peter; Kastberg Nielsen, Claus

2005-01-01

and geographical markets. Using a unique data setfor prices of Norwegian and Scottish salmon, we propose a methodologyfor simultaneous market delineation and we demonstrate that comparedto a sequential approach conclusions will be reversed.JEL: C3, K21, L41, Q22Keywords: Relevant market, econometric delineation......Delineation of the relevant market forms a pivotal part of most antitrustcases. The standard approach is sequential. First the product marketis delineated, then the geographical market is defined. Demand andsupply substitution in both the product dimension and the geographicaldimension...

30 CFR 282.22 - Delineation Plan.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 2 2010-07-01 2010-07-01 false Delineation Plan. 282.22 Section 282.22 Mineral... § 282.22 Delineation Plan. All exploration activities shall be conducted in accordance with a Delineation Plan submitted by the lessee and approved by the Director. The Delineation Plan shall describe the...

An accurate segmentation method for volumetry of brain tumor in 3D MRI

Science.gov (United States)

Wang, Jiahui; Li, Qiang; Hirai, Toshinori; Katsuragawa, Shigehiko; Li, Feng; Doi, Kunio

2008-03-01

Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3-D) image segmentation technique. First, the central location of a tumor was identified by a radiologist, and then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3-D image of the tumor into a two-dimensional (2-D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the tumor scanned the 3-D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial line provided a transformed 2-D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2-D image. We then transformed the optimal outline back into 3-D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and a radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88%.

A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data.

Science.gov (United States)

Engblom, Henrik; Tufvesson, Jane; Jablonowski, Robert; Carlsson, Marcus; Aletras, Anthony H; Hoffmann, Pavel; Jacquier, Alexis; Kober, Frank; Metzler, Bernhard; Erlinge, David; Atar, Dan; Arheden, Håkan; Heiberg, Einar

2016-05-04

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in

A probabilistic approach to delineating functional brain regions

DEFF Research Database (Denmark)

Kalbitzer, Jan; Svarer, Claus; Frokjaer, Vibe G

2009-01-01

The purpose of this study was to develop a reliable observer-independent approach to delineating volumes of interest (VOIs) for functional brain regions that are not identifiable on structural MR images. The case is made for the raphe nuclei, a collection of nuclei situated in the brain stem known...... to be densely packed with serotonin transporters (5-hydroxytryptaminic [5-HTT] system). METHODS: A template set for the raphe nuclei, based on their high content of 5-HTT as visualized in parametric (11)C-labeled 3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile PET images, was created for 10...... healthy subjects. The templates were subsequently included in the region sets used in a previously published automatic MRI-based approach to create an observer- and activity-independent probabilistic VOI map. The probabilistic map approach was tested in a different group of 10 subjects and compared...

Automatic localization of the prostate for on-line or off-line image-guided radiotherapy

International Nuclear Information System (INIS)

Smitsmans, Monique H.P.; Wolthaus, Jochem W.H.; Artignan, Xavier; Bois, Josien de; Jaffray, David A.; Lebesque, Joos V.; Herk, Marcel van

2004-01-01

Purpose: With higher radiation dose, higher cure rates have been reported in prostate cancer patients. The extra margin needed to account for prostate motion, however, limits the level of dose escalation, because of the presence of surrounding organs at risk. Knowledge of the precise position of the prostate would allow significant reduction of the treatment field. Better localization of the prostate at the time of treatment is therefore needed, e.g. using a cone-beam computed tomography (CT) system integrated with the linear accelerator. Localization of the prostate relies upon manual delineation of contours in successive axial CT slices or interactive alignment and is fairly time-consuming. A faster method is required for on-line or off-line image-guided radiotherapy, because of prostate motion, for patient throughput and efficiency. Therefore, we developed an automatic method to localize the prostate, based on 3D gray value registration. Methods and materials: A study was performed on conventional repeat CT scans of 19 prostate cancer patients to develop the methodology to localize the prostate. For each patient, 8-13 repeat CT scans were made during the course of treatment. First, the planning CT scan and the repeat CT scan were registered onto the rigid bony structures. Then, the delineated prostate in the planning CT scan was enlarged by an optimum margin of 5 mm to define a region of interest in the planning CT scan that contained enough gray value information for registration. Subsequently, this region was automatically registered to a repeat CT scan using 3D gray value registration to localize the prostate. The performance of automatic prostate localization was compared to prostate localization using contours. Therefore, a reference set was generated by registering the delineated contours of the prostates in all scans of all patients. Gray value registrations that showed large differences with respect to contour registrations were detected with a χ 2

Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm.

Science.gov (United States)

Conversano, Francesco; Franchini, Roberto; Demitri, Christian; Massoptier, Laurent; Montagna, Francesco; Maffezzoli, Alfonso; Malvasi, Antonio; Casciaro, Sergio

2011-04-01

The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery. A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features. The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively. A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections. Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

Delineation of geological facies from poorly differentiated data

Energy Technology Data Exchange (ETDEWEB)

Wohlberg, Brendt [Los Alamos National Laboratory; Tartakovsky, Daniel [UCSC

2008-01-01

The ability to delineate geologic facies and to estima.te their properties from sparse data is essential for modeling physical and biochemical processes occurring in the 'ubsurface. If such data are poorly differentiated, this challcnrring task is complicated further by the absence of a clear distinction between different hydrofacies even at locations where data. are available. vVe consider three alt mative approaches for analysis of poorly differentiated data: a k-means clU!:iterinrr algorithm, an expectation-maximization algorithm, and a minimum-variance algorithm. Two distinct synthetically generated geological settings are used to r:tnalyze the ability of these algorithmti to as ign accurately the membership of such data in a given geologic facies. On average, the minimum-variance algorithm provides a more robust p rformance than its two counterparts and when combined with a nearest-neighbor algorithm, it also yields the most accurate reconstruction of the boundaries between the facies.

Target volume delineation and field setup. A practical guide for conformal and intensity-modulated radiation therapy

Energy Technology Data Exchange (ETDEWEB)

Lee, Nancy Y. [Memorial Sloan-Kettering Cancer Center, New York, NY (United States). Radiation Oncology; Lu, Jiade J. (eds.) [National Univ. Health System, Singapore (Singapore). Dept. of Radiation Oncology; National Univ. of Singapore (Singapore). Dept. of Medicine

2013-03-01

Practical handbook on selection and delineation of tumor volumes and fields for conformal radiation therapy, including IMRT. Helpful format facilitating use on a step-by-step basis in daily practice. Designed to ensure accurate coverage of commonly encountered tumors along their routes of spread. This handbook is designed to enable radiation oncologists to appropriately and confidently delineate tumor volumes/fields for conformal radiation therapy, including intensity-modulated radiation therapy (IMRT), in patients with commonly encountered cancers. The orientation of this handbook is entirely practical, in that the focus is on the illustration of clinical target volume (CTV) delineation for each major malignancy. Each chapter provides guidelines and concise knowledge on CTV selection for a particular disease, explains how the anatomy of lymphatic drainage shapes the selection of the target volume, and presents detailed illustrations of volumes, slice by slice, on planning CT images. While the emphasis is on target volume delineation for three-dimensional conformal therapy and IMRT, information is also provided on conventional radiation therapy field setup and planning for certain malignancies for which IMRT is not currently suitable.

Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model

International Nuclear Information System (INIS)

Schoot, A. J. A. J. van de; Schooneveldt, G.; Wognum, S.; Stalpers, L. J. A.; Rasch, C. R. N.; Bel, A.; Hoogeman, M. S.; Chai, X.

2014-01-01

Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used to guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation

Accurate activity recognition in a home setting

NARCIS (Netherlands)

van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B.

2008-01-01

A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its

Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

International Nuclear Information System (INIS)

Fortunati, Valerio; Niessen, Wiro J; Veenland, Jifke F; Van Walsum, Theo; Verhaart, René F; Paulides, Margarethus M

2015-01-01

A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck.Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available.The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used.Using the proposed approach we improved the performance of the approach previously presented for H and N hyperthermia treatment planning, making the method suitable for clinical application. (paper)

Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

Science.gov (United States)

Fortunati, Valerio; Verhaart, René F.; Niessen, Wiro J.; Veenland, Jifke F.; Paulides, Margarethus M.; van Walsum, Theo

2015-08-01

A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck. Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available. The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used. Using the proposed approach we improved the performance of the approach previously presented for H&N hyperthermia treatment planning, making the method suitable for clinical application.

Automatic Quantification of Radiographic Wrist Joint Space Width of Patients With Rheumatoid Arthritis.

Science.gov (United States)

Huo, Yinghe; Vincken, Koen L; van der Heijde, Desiree; de Hair, Maria J H; Lafeber, Floris P; Viergever, Max A

2017-11-01

Objective: Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification for RA patients has not been widely investigated. The aim of this paper is to present an automatic method to quantify the JSW of three wrist joints that are least affected by bone overlapping and are frequently involved in RA. These joints are located around the scaphoid bone, viz. the multangular-navicular, capitate-navicular-lunate, and radiocarpal joints. Methods: The joint space around the scaphoid bone is detected by using consecutive searches of separate path segments, where each segment location aids in constraining the subsequent one. For joint margin delineation, first the boundary not affected by X-ray projection is extracted, followed by a backtrace process to obtain the actual joint margin. The accuracy of the quantified JSW is evaluated by comparison with the manually obtained ground truth. Results: Two of the 50 radiographs used for evaluation of the method did not yield a correct path through all three wrist joints. The delineated joint margins of the remaining 48 radiographs were used for JSW quantification. It was found that 90% of the joints had a JSW deviating less than 20% from the mean JSW of manual indications, with the mean JSW error less than 10%. Conclusion: The proposed method is able to automatically quantify the JSW of radiographic wrist joints reliably. The proposed method may aid clinical researchers to study the progression of wrist joint damage in RA studies. Objective: Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification for RA patients has not been widely investigated. The aim of this paper is to present an automatic method to quantify the JSW of three wrist joints that are least affected by bone overlapping and are frequently involved in RA. These joints

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

Science.gov (United States)

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Local Histograms for Per-Pixel Classification

Science.gov (United States)

2012-03-01

few axioms for such models are presented. These axioms are shown to be satisfied using the convergence of random wavelet expansions. The authors of...pathologists can accurately and consistently identify and delineate tissues and their pathologies , it is an expensive and time-consuming task, therefore...Automatic Identification and Delineation of Tissues and Pathologies in H&E Stained Images. PhD Thesis. Carnegie Mellon University, Pittsburgh, PA (September

Methods for Delineating Degraded Land at Citarum Watershed, West Java, Indonesia

Directory of Open Access Journals (Sweden)

Suria Darma Tarigan

2012-09-01

Full Text Available Accurate information on the extent and spatial location of degraded lands is very important to plan their rehabilitation.So far, various institutions issue different estimation on the extent of degraded land in Indonesia led to big confusion for rehabilitation planning. Ministry of Forestry estimates around 30.2 million ha of degraded land both inside and outside forestry area throughout Indonesia based on data released in 2007. Ministry of Forestry implementes the socalled scoring method in delineating degraded land. Criteria used in the scoring methods are: land cover, slope steepness, erosion, and management. Scoring method applies different weight to each of those criteria. This study aimed to analyze accuracy of scoring method and to compare it to propose alternative methods in delineating degraded land such as: a Inconsistency of land use, and b Combination of Inconsistency of land use and scoring method. The accuracy of these methods were obtained by comparing to the field observation. The slope map was derived from SRTM 30 m, soil map was obtained from Soil Research Institute and land cover/land use from Ministry for Environment. Using GIS analysis, those maps were used to compose land capability classification (LCC and inconsistency of land use. The study showed that scoring method had 66% accuracy in delineating degraded land. When scoring method was combined with Inconsistency method the accuracy increased about 7%.

Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance

Directory of Open Access Journals (Sweden)

Sjögren Jane

2012-01-01

Full Text Available Abstract Background T2-weighted cardiovascular magnetic resonance (CMR has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR, after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD, full width half maximum intensity (FWHM or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Methods Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. Results MaR was 32.9 ± 10.9% of left ventricular mass (LVM when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p Conclusions There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a

Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts

International Nuclear Information System (INIS)

Berthon, B.; Marshall, C.; Evans, M.; Spezi, E.

2014-01-01

Purpose: The use of positron emission tomography (PET) within radiotherapy treatment planning requires the availability of reliable and accurate segmentation tools. PET automatic segmentation (PET-AS) methods have been recommended for the delineation of tumors, but there is still a lack of thorough validation and cross-comparison of such methods using clinically relevant data. In particular, studies validating PET segmentation tools mainly use phantoms with thick plastic walls inserts of simple spherical geometry and have not specifically investigated the effect of the target object geometry on the delineation accuracy. Our work therefore aimed at generating clinically realistic data using nonspherical thin-wall plastic inserts, for the evaluation and comparison of a set of eight promising PET-AS approaches. Methods: Sixteen nonspherical inserts were manufactured with a plastic wall of 0.18 mm and scanned within a custom plastic phantom. These included ellipsoids and toroids derived with different volumes, as well as tubes, pear- and drop-shaped inserts with different aspect ratios. A set of six spheres of volumes ranging from 0.5 to 102 ml was used for a baseline study. A selection of eight PET-AS methods, written in house, was applied to the images obtained. The methods represented promising segmentation approaches such as adaptive iterative thresholding, region-growing, clustering and gradient-based schemes. The delineation accuracy was measured in terms of overlap with the computed tomography reference contour, using the dice similarity coefficient (DSC), and error in dimensions. Results: The delineation accuracy was lower for nonspherical inserts than for spheres of the same volume in 88% cases. Slice-by-slice gradient-based methods, showed particularly lower DSC for tori (DSC 0.76 except for tori) but showed the largest errors in the recovery of pears and drops dimensions (higher than 10% and 30% of the true length, respectively). Large errors were visible

A segmentation approach for a delineation of terrestrial ecoregions

Science.gov (United States)

Nowosad, J.; Stepinski, T.

2017-12-01

Terrestrial ecoregions are the result of regionalization of land into homogeneous units of similar ecological and physiographic features. Terrestrial Ecoregions of the World (TEW) is a commonly used global ecoregionalization based on expert knowledge and in situ observations. Ecological Land Units (ELUs) is a global classification of 250 meters-sized cells into 4000 types on the basis of the categorical values of four environmental variables. ELUs are automatically calculated and reproducible but they are not a regionalization which makes them impractical for GIS-based spatial analysis and for comparison with TEW. We have regionalized terrestrial ecosystems on the basis of patterns of the same variables (land cover, soils, landform, and bioclimate) previously used in ELUs. Considering patterns of categorical variables makes segmentation and thus regionalization possible. Original raster datasets of the four variables are first transformed into regular grids of square-sized blocks of their cells called eco-sites. Eco-sites are elementary land units containing local patterns of physiographic characteristics and thus assumed to contain a single ecosystem. Next, eco-sites are locally aggregated using a procedure analogous to image segmentation. The procedure optimizes pattern homogeneity of all four environmental variables within each segment. The result is a regionalization of the landmass into land units characterized by uniform pattern of land cover, soils, landforms, climate, and, by inference, by uniform ecosystem. Because several disjoined segments may have very similar characteristics, we cluster the segments to obtain a smaller set of segment types which we identify with ecoregions. Our approach is automatic, reproducible, updatable, and customizable. It yields the first automatic delineation of ecoregions on the global scale. In the resulting vector database each ecoregion/segment is described by numerous attributes which make it a valuable GIS resource for

Revisiting the dose-effect correlations in irradiated head and neck cancer using automatic segmentation tools of the dental structures, mandible and maxilla

International Nuclear Information System (INIS)

Thariat, J.; Ramus, L.; Odin, G.; Vincent, S.; Orlanducci, M.H.; Dassonville, O.; Darcourt, V.; Lacout, A.; Marcy, P.Y.; Cagnol, G.; Malandain, G.

2011-01-01

Purpose. - Manual delineation of dental structures is too time-consuming to be feasible in routine practice. Information on dose risk levels is crucial for dentists following irradiation of the head and neck to avoid post-extraction osteoradionecrosis based on empirical dose-effects data established on bidimensional radiation therapy plans. Material and methods. - We present an automatic atlas-based segmentation framework of the dental structures, called Dentalmaps, constructed from a patient image-segmentation database. Results. - This framework is accurate (within 2 Gy accuracy) and relevant for the routine use. It has the potential to guide dental care in the context of new irradiation techniques. Conclusion. - This tool provides a user-friendly interface for dentists and radiation oncologists in the context of irradiated head and neck cancer patients. It will likely improve the knowledge of dose-effect correlations for dental complications and osteoradionecrosis. (authors)

SU-E-J-07: A Functional MR Protocol for the Pancreatic Tumor Delineation

International Nuclear Information System (INIS)

Andreychenko, A; Heerkens, H; Meijer, G; Vulpen, M van; Lagendijk, J; Berg, C van den

2014-01-01

Purpose: Pancreatic cancer is one of the cancers with the poorest survival prognosis. At the time of diagnosis most of pancreatic cancers are unresectable and those patients can be treated by radiotherapy. Radiotherapy for pancreatic cancer is limited due to uncertainties in CT-based delineations. MRI provides an excellent soft tissue contrast. Here, an MR protocol is developed to improve delineations for radiotherapy treatment of pancreatic cancer. In a later stage this protocol can also be used for on-line visualization of the pancreas during MRI guided treatments. Methods: Nine pancreatic cancer patients were included. The MR protocol included T2 weighted(T2w), T1 weighted(T1w), diffusion weighted(DWI) and dynamic contrast enhanced(DCE) techniques. The tumor was delineated on T2w and T1w MRI by an experienced radiation oncologist. Healthy pancreas or pancreatitis (assigned by the oncologist based on T2w) areas were also delineated. Apparent diffusion coefficient(ADC), and area under the curve(AUC)/time to peak(TTP) maps were obtained from DWI and DCE scans, respectively. Results: A clear demarcation of tumor area was visible on b800 DWI images in 5 patients. ADC maps of those patients characterized tumor as an area with restricted water diffusion. Tumor delineations based on solely DCE were possible in 7 patients. In 6 of those patients AUC maps demonstrated tumor heterogeneity: a hypointense area with a hyperintense ring. TTP values clearly discriminated the tumor and the healthy pancreas but could not distinguish tumor and the pancreatitis accurately. Conclusion: MR imaging results in a more pronounced tumor contrast than contrast enhanced CT. The addition of quantitative, functional MRI provides valuable, additional information to the radiation oncologist on the spatial tumor extent by discriminating tumor from the healthy pancreas(TTP, DWI) and characterizing the tumor(ADC). Our findings indicate that tumor delineation in pancreatic cancer can greatly

SU-E-J-07: A Functional MR Protocol for the Pancreatic Tumor Delineation

Energy Technology Data Exchange (ETDEWEB)

Andreychenko, A; Heerkens, H; Meijer, G; Vulpen, M van; Lagendijk, J; Berg, C van den [UMC Utrecht, Utrecht, Utrecht (Netherlands)

2014-06-01

Purpose: Pancreatic cancer is one of the cancers with the poorest survival prognosis. At the time of diagnosis most of pancreatic cancers are unresectable and those patients can be treated by radiotherapy. Radiotherapy for pancreatic cancer is limited due to uncertainties in CT-based delineations. MRI provides an excellent soft tissue contrast. Here, an MR protocol is developed to improve delineations for radiotherapy treatment of pancreatic cancer. In a later stage this protocol can also be used for on-line visualization of the pancreas during MRI guided treatments. Methods: Nine pancreatic cancer patients were included. The MR protocol included T2 weighted(T2w), T1 weighted(T1w), diffusion weighted(DWI) and dynamic contrast enhanced(DCE) techniques. The tumor was delineated on T2w and T1w MRI by an experienced radiation oncologist. Healthy pancreas or pancreatitis (assigned by the oncologist based on T2w) areas were also delineated. Apparent diffusion coefficient(ADC), and area under the curve(AUC)/time to peak(TTP) maps were obtained from DWI and DCE scans, respectively. Results: A clear demarcation of tumor area was visible on b800 DWI images in 5 patients. ADC maps of those patients characterized tumor as an area with restricted water diffusion. Tumor delineations based on solely DCE were possible in 7 patients. In 6 of those patients AUC maps demonstrated tumor heterogeneity: a hypointense area with a hyperintense ring. TTP values clearly discriminated the tumor and the healthy pancreas but could not distinguish tumor and the pancreatitis accurately. Conclusion: MR imaging results in a more pronounced tumor contrast than contrast enhanced CT. The addition of quantitative, functional MRI provides valuable, additional information to the radiation oncologist on the spatial tumor extent by discriminating tumor from the healthy pancreas(TTP, DWI) and characterizing the tumor(ADC). Our findings indicate that tumor delineation in pancreatic cancer can greatly

Automatically high accurate and efficient photomask defects management solution for advanced lithography manufacture

Science.gov (United States)

Zhu, Jun; Chen, Lijun; Ma, Lantao; Li, Dejian; Jiang, Wei; Pan, Lihong; Shen, Huiting; Jia, Hongmin; Hsiang, Chingyun; Cheng, Guojie; Ling, Li; Chen, Shijie; Wang, Jun; Liao, Wenkui; Zhang, Gary

2014-04-01

Defect review is a time consuming job. Human error makes result inconsistent. The defects located on don't care area would not hurt the yield and no need to review them such as defects on dark area. However, critical area defects can impact yield dramatically and need more attention to review them such as defects on clear area. With decrease in integrated circuit dimensions, mask defects are always thousands detected during inspection even more. Traditional manual or simple classification approaches are unable to meet efficient and accuracy requirement. This paper focuses on automatic defect management and classification solution using image output of Lasertec inspection equipment and Anchor pattern centric image process technology. The number of mask defect found during an inspection is always in the range of thousands or even more. This system can handle large number defects with quick and accurate defect classification result. Our experiment includes Die to Die and Single Die modes. The classification accuracy can reach 87.4% and 93.3%. No critical or printable defects are missing in our test cases. The missing classification defects are 0.25% and 0.24% in Die to Die mode and Single Die mode. This kind of missing rate is encouraging and acceptable to apply on production line. The result can be output and reloaded back to inspection machine to have further review. This step helps users to validate some unsure defects with clear and magnification images when captured images can't provide enough information to make judgment. This system effectively reduces expensive inline defect review time. As a fully inline automated defect management solution, the system could be compatible with current inspection approach and integrated with optical simulation even scoring function and guide wafer level defect inspection.

Dynamic weighing for accurate fertilizer application and monitoring

NARCIS (Netherlands)

Bergeijk, van J.; Goense, D.; Willigenburg, van L.G.; Speelman, L.

2001-01-01

The mass flow of fertilizer spreaders must be calibrated for the different types of fertilizers used. To obtain accurate fertilizer application manual calibration of actual mass flow must be repeated frequently. Automatic calibration is possible by measurement of the actual mass flow, based on

An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

Science.gov (United States)

Plata, Jose J.; Nath, Pinku; Usanmaz, Demet; Carrete, Jesús; Toher, Cormac; de Jong, Maarten; Asta, Mark; Fornari, Marco; Nardelli, Marco Buongiorno; Curtarolo, Stefano

2017-10-01

One of the most accurate approaches for calculating lattice thermal conductivity, , is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path: high computational costs and lack of automation in the frameworks using this methodology, which affect the discovery rate of novel materials with ad-hoc properties. Here, the Automatic Anharmonic Phonon Library (AAPL) is presented. It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis, it solves the Boltzmann transport equation to obtain , and allows a fully integrated operation with minimum user intervention, a rational addition to the current high-throughput accelerated materials development framework AFLOW. An "experiment vs. theory" study of the approach is shown, comparing accuracy and speed with respect to other available packages, and for materials characterized by strong electron localization and correlation. Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

Validation of a method for accurate and highly reproducible quantification of brain dopamine transporter SPECT studies

DEFF Research Database (Denmark)

Jensen, Peter S; Ziebell, Morten; Skouboe, Glenna

2011-01-01

In nuclear medicine brain imaging, it is important to delineate regions of interest (ROIs) so that the outcome is both accurate and reproducible. The purpose of this study was to validate a new time-saving algorithm (DATquan) for accurate and reproducible quantification of the striatal dopamine t...... transporter (DAT) with appropriate radioligands and SPECT and without the need for structural brain scanning....

Automatically sweeping dual-channel boxcar integrator

International Nuclear Information System (INIS)

Keefe, D.J.; Patterson, D.R.

1978-01-01

An automatically sweeping dual-channel boxcar integrator has been developed to automate the search for a signal that repeatedly follows a trigger pulse by a constant or slowly varying time delay when that signal is completely hidden in random electrical noise and dc-offset drifts. The automatically sweeping dual-channel boxcar integrator improves the signal-to-noise ratio and eliminates dc-drift errors in the same way that a conventional dual-channel boxcar integrator does, but, in addition, automatically locates the hidden signal. When the signal is found, its time delay is displayed with 100-ns resolution, and its peak value is automatically measured and displayed. This relieves the operator of the tedious, time-consuming, and error-prone search for the signal whenever the time delay changes. The automatically sweeping boxcar integrator can also be used as a conventional dual-channel boxcar integrator. In either mode, it can repeatedly integrate a signal up to 990 times and thus make accurate measurements of the signal pulse height in the presence of random noise, dc offsets, and unsynchronized interfering signals

A new automatic blood pressure kit auscultates for accurate reading with a smartphone: A diagnostic accuracy study.

Science.gov (United States)

Wu, Hongjun; Wang, Bingjian; Zhu, Xinpu; Chu, Guang; Zhang, Zhi

2016-08-01

The widely used oscillometric automated blood pressure (BP) monitor was continuously questioned on its accuracy. A novel BP kit named Accutension which adopted Korotkoff auscultation method was then devised. Accutension worked with a miniature microphone, a pressure sensor, and a smartphone. The BP values were automatically displayed on the smartphone screen through the installed App. Data recorded in the phone could be played back and reconfirmed after measurement. They could also be uploaded and saved to the iCloud. The accuracy and consistency of this novel electronic auscultatory sphygmomanometer was preliminarily verified here. Thirty-two subjects were included and 82 qualified readings were obtained. The mean differences ± SD for systolic and diastolic BP readings between Accutension and mercury sphygmomanometer were 0.87 ± 2.86 and -0.94 ± 2.93 mm Hg. Agreements between Accutension and mercury sphygmomanometer were highly significant for systolic (ICC = 0.993, 95% confidence interval (CI): 0.989-0.995) and diastolic (ICC = 0.987, 95% CI: 0.979-0.991). In conclusion, Accutension worked accurately based on our pilot study data. The difference was acceptable. ICC and Bland-Altman plot charts showed good agreements with manual measurements. Systolic readings of Accutension were slightly higher than those of manual measurement, while diastolic readings were slightly lower. One possible reason was that Accutension captured the first and the last korotkoff sound more sensitively than human ear during manual measurement and avoided sound missing, so that it might be more accurate than traditional mercury sphygmomanometer. By documenting and analyzing of variant tendency of BP values, Accutension helps management of hypertension and therefore contributes to the mobile heath service.

Automatic River Network Extraction from LIDAR Data

Science.gov (United States)

Maderal, E. N.; Valcarcel, N.; Delgado, J.; Sevilla, C.; Ojeda, J. C.

2016-06-01

National Geographic Institute of Spain (IGN-ES) has launched a new production system for automatic river network extraction for the Geospatial Reference Information (GRI) within hydrography theme. The goal is to get an accurate and updated river network, automatically extracted as possible. For this, IGN-ES has full LiDAR coverage for the whole Spanish territory with a density of 0.5 points per square meter. To implement this work, it has been validated the technical feasibility, developed a methodology to automate each production phase: hydrological terrain models generation with 2 meter grid size and river network extraction combining hydrographic criteria (topographic network) and hydrological criteria (flow accumulation river network), and finally the production was launched. The key points of this work has been managing a big data environment, more than 160,000 Lidar data files, the infrastructure to store (up to 40 Tb between results and intermediate files), and process; using local virtualization and the Amazon Web Service (AWS), which allowed to obtain this automatic production within 6 months, it also has been important the software stability (TerraScan-TerraSolid, GlobalMapper-Blue Marble , FME-Safe, ArcGIS-Esri) and finally, the human resources managing. The results of this production has been an accurate automatic river network extraction for the whole country with a significant improvement for the altimetric component of the 3D linear vector. This article presents the technical feasibility, the production methodology, the automatic river network extraction production and its advantages over traditional vector extraction systems.

AUTOMATIC RIVER NETWORK EXTRACTION FROM LIDAR DATA

Directory of Open Access Journals (Sweden)

E. N. Maderal

2016-06-01

Full Text Available National Geographic Institute of Spain (IGN-ES has launched a new production system for automatic river network extraction for the Geospatial Reference Information (GRI within hydrography theme. The goal is to get an accurate and updated river network, automatically extracted as possible. For this, IGN-ES has full LiDAR coverage for the whole Spanish territory with a density of 0.5 points per square meter. To implement this work, it has been validated the technical feasibility, developed a methodology to automate each production phase: hydrological terrain models generation with 2 meter grid size and river network extraction combining hydrographic criteria (topographic network and hydrological criteria (flow accumulation river network, and finally the production was launched. The key points of this work has been managing a big data environment, more than 160,000 Lidar data files, the infrastructure to store (up to 40 Tb between results and intermediate files, and process; using local virtualization and the Amazon Web Service (AWS, which allowed to obtain this automatic production within 6 months, it also has been important the software stability (TerraScan-TerraSolid, GlobalMapper-Blue Marble , FME-Safe, ArcGIS-Esri and finally, the human resources managing. The results of this production has been an accurate automatic river network extraction for the whole country with a significant improvement for the altimetric component of the 3D linear vector. This article presents the technical feasibility, the production methodology, the automatic river network extraction production and its advantages over traditional vector extraction systems.

Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

Directory of Open Access Journals (Sweden)

Yuhan Yang

2013-01-01

Full Text Available Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF, Thin-Plate Spline (TPS, and an adapted active contour (Snake, used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS mean is about 0.88 and the maximum of Hausdorff distance (HD is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

Directory of Open Access Journals (Sweden)

K. T. Chang

2016-06-01

Full Text Available Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs. The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM from a digital surface model (DSM, and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC and a variable window filter (VWF, are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits" that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

Automated Tree Crown Delineation and Biomass Estimation from Airborne LiDAR data: A Comparison of Statistical and Machine Learning Methods

Science.gov (United States)

Gleason, C. J.; Im, J.

2011-12-01

Airborne LiDAR remote sensing has been used effectively in assessing forest biomass because of its canopy penetrating effects and its ability to accurately describe the canopy surface. Current research in assessing biomass using airborne LiDAR focuses on either the individual tree as a base unit of study or statistical representations of a small aggregation of trees (i.e., plot level), and both methods usually rely on regression against field data to model the relationship between the LiDAR-derived data (e.g., volume) and biomass. This study estimates biomass for mixed forests and coniferous plantations (Picea Abies) within Heiberg Memorial Forest, Tully, NY, at both the plot and individual tree level. Plots are regularly spaced with a radius of 13m, and field data include diameter at breast height (dbh), tree height, and tree species. Field data collection and LiDAR data acquisition were seasonally coincident and both obtained in August of 2010. Resulting point cloud density was >5pts/m2. LiDAR data were processed to provide a canopy height surface, and a combination of watershed segmentation, active contouring, and genetic algorithm optimization was applied to delineate individual trees from the surface. This updated delineation method was shown to be more accurate than traditional watershed segmentation. Once trees had been delineated, four biomass estimation models were applied and compared: support vector regression (SVR), linear mixed effects regression (LME), random forest (RF), and Cubist regression. Candidate variables to be used in modeling were derived from the LiDAR surface, and include metrics of height, width, and volume per delineated tree footprint. Previously published allometric equations provided field estimates of biomass to inform the regressions and calculate their accuracy via leave-one-out cross validation. This study found that for forests such as found in the study area, aggregation of individual trees to form a plot-based estimate of

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

Science.gov (United States)

Hatfield, Fraser N.; Dehmeshki, Jamshid

1998-09-01

Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

Automatic and accurate reconstruction of distal humerus contours through B-Spline fitting based on control polygon deformation.

Science.gov (United States)

Mostafavi, Kamal; Tutunea-Fatan, O Remus; Bordatchev, Evgueni V; Johnson, James A

2014-12-01

The strong advent of computer-assisted technologies experienced by the modern orthopedic surgery prompts for the expansion of computationally efficient techniques to be built on the broad base of computer-aided engineering tools that are readily available. However, one of the common challenges faced during the current developmental phase continues to remain the lack of reliable frameworks to allow a fast and precise conversion of the anatomical information acquired through computer tomography to a format that is acceptable to computer-aided engineering software. To address this, this study proposes an integrated and automatic framework capable to extract and then postprocess the original imaging data to a common planar and closed B-Spline representation. The core of the developed platform relies on the approximation of the discrete computer tomography data by means of an original two-step B-Spline fitting technique based on successive deformations of the control polygon. In addition to its rapidity and robustness, the developed fitting technique was validated to produce accurate representations that do not deviate by more than 0.2 mm with respect to alternate representations of the bone geometry that were obtained through different-contact-based-data acquisition or data processing methods. © IMechE 2014.

Delineation and geometric modeling of road networks

Science.gov (United States)

Poullis, Charalambos; You, Suya

In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

Security and Hyper-accurate Positioning Monitoring with Automatic Dependent Surveillance-Broadcast (ADS-B), Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Lightning Ridge Technologies, working in collaboration with The Innovation Laboratory, Inc., extend Automatic Dependent Surveillance Broadcast (ADS-B) into a safe,...

Security and Hyper-accurate Positioning Monitoring with Automatic Dependent Surveillance-Broadcast (ADS-B), Phase II

Data.gov (United States)

National Aeronautics and Space Administration — Lightning Ridge Technologies, LLC, working in collaboration with The Innovation Laboratory, Inc., extend Automatic Dependent Surveillance -- Broadcast (ADS-B) into a...

Automatic segmentation of time-lapse microscopy images depicting a live Dharma embryo.

Science.gov (United States)

Zacharia, Eleni; Bondesson, Maria; Riu, Anne; Ducharme, Nicole A; Gustafsson, Jan-Åke; Kakadiaris, Ioannis A

2011-01-01

Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed. Experiments performed in four stacks of 3D images over time have shown promising results.

High accurate time system of the Low Latitude Meridian Circle.

Science.gov (United States)

Yang, Jing; Wang, Feng; Li, Zhiming

In order to obtain the high accurate time signal for the Low Latitude Meridian Circle (LLMC), a new GPS accurate time system is developed which include GPS, 1 MC frequency source and self-made clock system. The second signal of GPS is synchronously used in the clock system and information can be collected by a computer automatically. The difficulty of the cancellation of the time keeper can be overcomed by using this system.

Tumor bed delineation for external beam accelerated partial breast irradiation: A systematic review

International Nuclear Information System (INIS)

Yang, T. Jonathan; Tao, Randa; Elkhuizen, Paula H.M.; Vliet-Vroegindeweij, Corine van; Li, Guang; Powell, Simon N.

2013-01-01

In recent years, accelerated partial breast irradiation (APBI) has been considered an alternative to whole breast irradiation for patients undergoing breast-conserving therapy. APBI delivers higher doses of radiation in fewer fractions to the post-lumpectomy tumor bed with a 1–2 cm margin, targeting the area at the highest risk of local recurrence while sparing normal breast tissue. However, there are inherent challenges in defining accurate target volumes for APBI. Studies have shown that significant interobserver variation exists among radiation oncologists defining the lumpectomy cavity, which raises the question of how to improve the accuracy and consistency in the delineation of tumor bed volumes. The combination of standardized guidelines and surgical clips significantly improves an observer’s ability in delineation, and it is the standard in multiple ongoing external-beam APBI trials. However, questions about the accuracy of the clips to mark the lumpectomy cavity remain, as clips only define a few points at the margin of the cavity. This paper reviews the techniques that have been developed so far to improve target delineation in APBI delivered by conformal external beam radiation therapy, including the use of standardized guidelines, surgical clips or fiducial markers, pre-operative computed tomography imaging, and additional imaging modalities, including magnetic resonance imaging, ultrasound imaging, and positron emission tomography/computed tomography. Alternatives to post-operative APBI, future directions, and clinical recommendations were also discussed

Discrete Model Reference Adaptive Control System for Automatic Profiling Machine

Directory of Open Access Journals (Sweden)

Peng Song

2012-01-01

Full Text Available Automatic profiling machine is a movement system that has a high degree of parameter variation and high frequency of transient process, and it requires an accurate control in time. In this paper, the discrete model reference adaptive control system of automatic profiling machine is discussed. Firstly, the model of automatic profiling machine is presented according to the parameters of DC motor. Then the design of the discrete model reference adaptive control is proposed, and the control rules are proven. The results of simulation show that adaptive control system has favorable dynamic performances.

Improved longitudinal length accuracy of gross tumor volume delineation with diffusion weighted magnetic resonance imaging for esophageal squamous cell carcinoma

International Nuclear Information System (INIS)

Hou, Dong-Liang; Shi, Gao-Feng; Gao, Xian-Shu; Asaumi, Junichi; Li, Xue-Ying; Liu, Hui; Yao, Chen; Chang, Joe Y

2013-01-01

To analyze the longitudinal length accuracy of gross tumor volume (GTV) delineation with diffusion weighted magnetic resonance imaging for esophageal squamous cell carcinoma (SCC). Forty-two patients from December 2011 to June 2012 with esophageal SCC who underwent radical surgery were analyzed. Routine computed tomography (CT) scan, T2-weighted MRI and diffusion weighted magnetic resonance imaging (DWI) were employed before surgery. Diffusion-sensitive gradient b-values were taken at 400, 600, and 800 s/mm 2 . Gross tumor volumes (GTV) were delineated using CT, T2-weighted MRI and DWI on different b-value images. GTV longitude length measured using the imaging modalities listed above was compared with pathologic lesion length to determine the most accurate imaging modality. CMS Xio radiotherapy planning system was used to fuse DWI scans and CT images to investigate the possibility of delineating GTV on fused images. The differences between the GTV length according to CT, T2-weighted MRI and pathology were 3.63 ± 12.06 mm and 3.46 ± 11.41 mm, respectively. When the diffusion-sensitive gradient b-value was 400, 600, and 800 s/mm 2 , the differences between the GTV length using DWI and pathology were 0.73 ± 6.09 mm, -0.54 ± 6.03 mm and −1.58 ± 5.71 mm, respectively. DWI scans and CT images were fused accurately using the radiotherapy planning system. GTV margins were depicted clearly on fused images. DWI displays esophageal SCC lengths most precisely when compared with CT or regular MRI. DWI scans fused with CT images can be used to improve accuracy to delineate GTV in esophageal SCC

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

Energy Technology Data Exchange (ETDEWEB)

He, Baochun; Huang, Cheng; Zhou, Shoujun; Hu, Qingmao; Jia, Fucang, E-mail: fc.jia@siat.ac.cn [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055 (China); Sharp, Gregory [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States); Fang, Chihua; Fan, Yingfang [Department of Hepatology (I), Zhujiang Hospital, Southern Medical University, Guangzhou 510280 (China)

2016-05-15

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

Science.gov (United States)

He, Baochun; Huang, Cheng; Sharp, Gregory; Zhou, Shoujun; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

2016-05-01

A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. The proposed automatic approach achieves robust, accurate, and fast liver

Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

International Nuclear Information System (INIS)

Chen Antong; Deeley, Matthew A.; Niermann, Kenneth J.; Moretti, Luigi; Dawant, Benoit M.

2010-01-01

Purpose: Intensity-modulated radiation therapy (IMRT) is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model (ASM) approach. Methods: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution. Results: The method was evaluated through a leave-one-out experiment. The ASM- and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient (DSC) for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively. Conclusions: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.

Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images

Science.gov (United States)

Tebbi, Mohsene Abdelfettah; Haddad, Boualem

2016-09-01

The aim of this study is to investigate the potential of cloud classification by means of support vector machines using high resolution images from northern Algeria. The images were taken from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of the Meteosat Second Generation (MSG) satellite. An automatic system was developed to operate during both day and nighttime by following two steps of data processing. The first aims to detect rainy areas in cloud systems, whereas the second delineates convective cells from stratiform ones. A set of 12 spectral parameters was selected to extract information about cloud properties, which are different from day to night. The training and validation steps of this study were performed by in-situ rainfall measurement data, collected during the rainy season of years 2011 and 2012 via automatic rain gauge stations distributed in northern Algeria. Artificial neural networks (ANNs) and support vector machine (SVM) were explored, by combining spectral parameters derived from MSG images. Better performances were obtained by the SVM classifier, in terms of Critical Success Index and Probability of Detection for rainy areas detection (CSI = 0.81, POD = 91%), and also for convective/stratiform delineation (CSI = 0.55, POD = 74%).

Evaluation of semi-automatic arterial stenosis quantification

International Nuclear Information System (INIS)

Hernandez Hoyos, M.; Universite Claude Bernard Lyon 1, 69 - Villeurbanne; Univ. de los Andes, Bogota; Serfaty, J.M.; Douek, P.C.; Universite Claude Bernard Lyon 1, 69 - Villeurbanne; Hopital Cardiovasculaire et Pneumologique L. Pradel, Bron; Maghiar, A.; Mansard, C.; Orkisz, M.; Magnin, I.; Universite Claude Bernard Lyon 1, 69 - Villeurbanne

2006-01-01

Object: To assess the accuracy and reproducibility of semi-automatic vessel axis extraction and stenosis quantification in 3D contrast-enhanced Magnetic Resonance Angiography (CE-MRA) of the carotid arteries (CA). Materials and methods: A total of 25 MRA datasets was used: 5 phantoms with known stenoses, and 20 patients (40 CAs) drawn from a multicenter trial database. Maracas software extracted vessel centerlines and quantified the stenoses, based on boundary detection in planes perpendicular to the centerline. Centerline accuracy was visually scored. Semi-automatic measurements were compared with: (1) theoretical phantom morphometric values, and (2) stenosis degrees evaluated by two independent radiologists. Results: Exploitable centerlines were obtained in 97% of CA and in all phantoms. In phantoms, the software achieved a better agreement with theoretic stenosis degrees (weighted kappa Κ W = 0.91) than the radiologists (Κ W = 0.69). In patients, agreement between software and radiologists varied from Κ W =0.67 to 0.90. In both, Maracas was substantially more reproducible than the readers. Mean operating time was within 1 min/ CA. Conclusion: Maracas software generates accurate 3D centerlines of vascular segments with minimum user intervention. Semi-automatic quantification of CA stenosis is also accurate, except in very severe stenoses that cannot be segmented. It substantially reduces the inter-observer variability. (orig.)

Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach

Science.gov (United States)

Atehortúa, Angélica; Zuluaga, Maria A.; Ourselin, Sébastien; Giraldo, Diana; Romero, Eduardo

2016-03-01

An accurate ventricular function quantification is important to support evaluation, diagnosis and prognosis of several cardiac pathologies. However, expert heart delineation, specifically for the right ventricle, is a time consuming task with high inter-and-intra observer variability. A fully automatic 3D+time heart segmentation framework is herein proposed for short-axis-cardiac MRI sequences. This approach estimates the heart using exclusively information from the sequence itself without tuning any parameters. The proposed framework uses a coarse-to-fine approach, which starts by localizing the heart via spatio-temporal analysis, followed by a segmentation of the basal heart that is then propagated to the apex by using a non-rigid-registration strategy. The obtained volume is then refined by estimating the ventricular muscle by locally searching a prior endocardium- pericardium intensity pattern. The proposed framework was applied to 48 patients datasets supplied by the organizers of the MICCAI 2012 Right Ventricle segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.

A new automatic blood pressure kit auscultates for accurate reading with a smartphone

OpenAIRE

Wu, Hongjun; Wang, Bingjian; Zhu, Xinpu; Chu, Guang; Zhang, Zhi

2016-01-01

Abstract The widely used oscillometric automated blood pressure (BP) monitor was continuously questioned on its accuracy. A novel BP kit named Accutension which adopted Korotkoff auscultation method was then devised. Accutension worked with a miniature microphone, a pressure sensor, and a smartphone. The BP values were automatically displayed on the smartphone screen through the installed App. Data recorded in the phone could be played back and reconfirmed after measurement. They could also b...

Influence of experience and qualification on PET-based target volume delineation. When there is no expert - ask your colleague

Energy Technology Data Exchange (ETDEWEB)

Doll, C.; Grosu, A.L.; Nestle, U. [University Medical Center Freiburg, Radiation Oncology Department, Freiburg/Breisgau (Germany); Duncker-Rohr, V. [University Medical Center Freiburg, Radiation Oncology Department, Freiburg/Breisgau (Germany); Ortenau Clinical Center Offenburg, Radiation Oncology Department, Offenburg (Germany); Ruecker, G. [University of Freiburg, Institute of Medical Biometry und Medical Informatics, Freiburg (Germany); Mix, M. [University Medical Center Freiburg, Nuclear Medicine Department, Freiburg (Germany); MacManus, M. [University of Melbourne, The Sir Peter MacCallum Department of Oncology, Melbourne (Australia); Ruysscher, D. de [University Hospital Leuven/KU Leuven, Department of Radiation Oncology, Leuven (Belgium); Vogel, W. [Antoni van Leeuwenhoek Hospital, Department of Nuclear Medicine, The Netherlands Cancer Institute, Amsterdam (Netherlands); Eriksen, J.G. [Odense University Hospital, Department of Oncology, Odense (Denmark); Oyen, W. [Radboud University Nijmegen Medical Center, Department of Nuclear Medicine, Nijmegen (Netherlands); Weber, W. [University Medical Center Freiburg, Nuclear Medicine Department, Freiburg (Germany); Memorial Sloan-Kettering Cancer Center, Department of Radiology/Molecular Imaging and Therapy Service, New York (United States)

2014-06-15

The integration of positron emission tomography (PET) information for target volume delineation in radiation treatment planning is routine in many centers. In contrast to automatic contouring, research on visual-manual delineation is scarce. The present study investigates the dependency of manual delineation on experience and qualification. A total of 44 international interdisciplinary observers each defined a [{sup 18}F]fluorodeoxyglucose (FDG)-PET based gross tumor volume (GTV) using the same PET/CT scan from a patient with lung cancer. The observers were ''experts'' (E; n = 3), ''experienced interdisciplinary pairs'' (EP; 9 teams of radiation oncologist (RO) + nuclear medicine physician (NP)), ''single field specialists'' (SFS; n = 13), and ''students'' (S; n = 10). Five automatic delineation methods (AM) were also included. Volume sizes and concordance indices within the groups (pCI) and relative to the experts (eCI) were calculated. E (pCI = 0.67) and EP (pCI = 0.53) showed a significantly higher agreement within the groups as compared to SFS (pCI = 0.43, p = 0.03, and p = 0.006). In relation to the experts, EP (eCI = 0.55) showed better concordance compared to SFS (eCI = 0.49) or S (eCI = 0.47). The intermethod variability of the AM (pCI = 0.44) was similar to that of SFS and S, showing poorer agreement with the experts (eCI = 0.35). The results suggest that interdisciplinary cooperation could be beneficial for consistent contouring. Joint delineation by a radiation oncologist and a nuclear medicine physician showed remarkable agreement and better concordance with the experts compared to other specialists. The relevant intermethod variability of the automatic algorithms underlines the need for further standardization and optimization in this field. (orig.) [German] Die Daten aus der Positronenemissionstomographie (PET) werden in vielen Kliniken routinemaessig zur

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

Science.gov (United States)

Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

12 CFR 228.41 - Assessment area delineation.

Science.gov (United States)

2010-01-01

... does not evaluate the bank's delineation of its assessment area(s) as a separate performance criterion..., such as those consumer loans on which the bank elects to have its performance assessed). (d... area(s) delineated by a bank in its evaluation of the bank's CRA performance unless the Board...

Estimating spatial travel times using automatic vehicle identification data

Science.gov (United States)

2001-01-01

Prepared ca. 2001. The paper describes an algorithm that was developed for estimating reliable and accurate average roadway link travel times using Automatic Vehicle Identification (AVI) data. The algorithm presented is unique in two aspects. First, ...

12 CFR 345.41 - Assessment area delineation.

Science.gov (United States)

2010-01-01

... the bank's delineation of its assessment area(s) as a separate performance criterion, but the FDIC..., such as those consumer loans on which the bank elects to have its performance assessed). (d... area(s) delineated by a bank in its evaluation of the bank's CRA performance unless the FDIC determines...

An Automatic Assembling System for Sealing Rings Based on Machine Vision

Directory of Open Access Journals (Sweden)

Mingyu Gao

2017-01-01

Full Text Available In order to grab and place the sealing rings of battery lid quickly and accurately, an automatic assembling system for sealing rings based on machine vision is developed in this paper. The whole system is composed of the light sources, cameras, industrial control units, and a 4-degree-of-freedom industrial robot. Specifically, the sealing rings are recognized and located automatically with the machine vision module. Then industrial robot is controlled for grabbing the sealing rings dynamically under the joint work of multiple control units and visual feedback. Furthermore, the coordinates of the fast-moving battery lid are tracked by the machine vision module. Finally the sealing rings are placed on the sealing ports of battery lid accurately and automatically. Experimental results demonstrate that the proposed system can grab the sealing rings and place them on the sealing port of the fast-moving battery lid successfully. More importantly, the proposed system can improve the efficiency of the battery production line obviously.

Automatic generation of accurate subject-specific bone finite element models to be used in clinical studies.

Science.gov (United States)

Viceconti, Marco; Davinelli, Mario; Taddei, Fulvia; Cappello, Angelo

2004-10-01

Most of the finite element models of bones used in orthopaedic biomechanics research are based on generic anatomies. However, in many cases it would be useful to generate from CT data a separate finite element model for each subject of a study group. In a recent study a hexahedral mesh generator based on a grid projection algorithm was found very effective in terms of accuracy and automation. However, so far the use of this method has been documented only on data collected in vitro and only for long bones. The present study was aimed at verifying if this method represents a procedure for the generation of finite element models of human bones from data collected in vivo, robust, accurate, automatic and general enough to be used in clinical studies. Robustness, automation and numerical accuracy of the proposed method were assessed on five femoral CT data sets of patients affected by various pathologies. The generality of the method was verified by processing a femur, an ileum, a phalanx, a proximal femur reconstruction, and the micro-CT of a small sample of spongy bone. The method was found robust enough to cope with the variability of the five femurs, producing meshes with a numerical accuracy and a computational weight comparable to those found in vitro. Even when the method was used to process the other bones the levels of mesh conditioning remained within acceptable limits. Thus, it may be concluded that the method presents a generality sufficient to cope with almost any orthopaedic application.

Influence of experience and qualification on PET-based target volume delineation. When there is no expert--ask your colleague

DEFF Research Database (Denmark)

Doll, C; Duncker-Rohr, V; Rücker, G

2014-01-01

"experts" (E; n = 3), "experienced interdisciplinary pairs" (EP; 9 teams of radiation oncologist (RO) + nuclear medicine physician (NP)), "single field specialists" (SFS; n = 13), and "students" (S; n = 10). Five automatic delineation methods (AM) were also included. Volume sizes and concordance indices...... compared to SFS (eCI = 0.49) or S (eCI = 0.47). The intermethod variability of the AM (pCI = 0.44) was similar to that of SFS and S, showing poorer agreement with the experts (eCI = 0.35). CONCLUSION: The results suggest that interdisciplinary cooperation could be beneficial for consistent contouring...

Operator overloading as an enabling technology for automatic differentiation

International Nuclear Information System (INIS)

Corliss, G.F.; Griewank, A.

1993-01-01

We present an example of the science that is enabled by object-oriented programming techniques. Scientific computation often needs derivatives for solving nonlinear systems such as those arising in many PDE algorithms, optimization, parameter identification, stiff ordinary differential equations, or sensitivity analysis. Automatic differentiation computes derivatives accurately and efficiently by applying the chain rule to each arithmetic operation or elementary function. Operator overloading enables the techniques of either the forward or the reverse mode of automatic differentiation to be applied to real-world scientific problems. We illustrate automatic differentiation with an example drawn from a model of unsaturated flow in a porous medium. The problem arises from planning for the long-term storage of radioactive waste

Prosody's Contribution to Fluency: An Examination of the Theory of Automatic Information Processing

Science.gov (United States)

Schrauben, Julie E.

2010-01-01

LaBerge and Samuels' (1974) theory of automatic information processing in reading offers a model that explains how and where the processing of information occurs and the degree to which processing of information occurs. These processes are dependent upon two criteria: accurate word decoding and automatic word recognition. However, LaBerge and…

Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.

Science.gov (United States)

Shimol, Eli Ben; Joskowicz, Leo; Eliahou, Ruth; Shoshan, Yigal

2018-02-01

Stereotactic radiosurgery (SRS) is a common treatment for intracranial meningiomas. SRS is planned on a pre-therapy gadolinium-enhanced T1-weighted MRI scan (Gd-T1w MRI) in which the meningioma contours have been delineated. Post-SRS therapy serial Gd-T1w MRI scans are then acquired for longitudinal treatment evaluation. Accurate tumor volume change quantification is required for treatment efficacy evaluation and for treatment continuation. We present a new algorithm for the automatic segmentation and volumetric assessment of meningioma in post-therapy Gd-T1w MRI scans. The inputs are the pre- and post-therapy Gd-T1w MRI scans and the meningioma delineation in the pre-therapy scan. The output is the meningioma delineations and volumes in the post-therapy scan. The algorithm uses the pre-therapy scan and its meningioma delineation to initialize an extended Chan-Vese active contour method and as a strong patient-specific intensity and shape prior for the post-therapy scan meningioma segmentation. The algorithm is automatic, obviates the need for independent tumor localization and segmentation initialization, and incorporates the same tumor delineation criteria in both the pre- and post-therapy scans. Our experimental results on retrospective pre- and post-therapy scans with a total of 32 meningiomas with volume ranges 0.4-26.5 cm[Formula: see text] yield a Dice coefficient of [Formula: see text]% with respect to ground-truth delineations in post-therapy scans created by two clinicians. These results indicate a high correspondence to the ground-truth delineations. Our algorithm yields more reliable and accurate tumor volume change measurements than other stand-alone segmentation methods. It may be a useful tool for quantitative meningioma prognosis evaluation after SRS.

Automatic assessment of volume asymmetries applied to hip abductor muscles in patients with hip arthroplasty

Science.gov (United States)

Klemt, Christian; Modat, Marc; Pichat, Jonas; Cardoso, M. J.; Henckel, Joahnn; Hart, Alister; Ourselin, Sebastien

2015-03-01

Metal-on-metal (MoM) hip arthroplasties have been utilised over the last 15 years to restore hip function for 1.5 million patients worldwide. Althoug widely used, this hip arthroplasty releases metal wear debris which lead to muscle atrophy. The degree of muscle wastage differs across patients ranging from mild to severe. The longterm outcomes for patients with MoM hip arthroplasty are reduced for increasing degrees of muscle atrophy, highlighting the need to automatically segment pathological muscles. The automated segmentation of pathological soft tissues is challenging as these lack distinct boundaries and morphologically differ across subjects. As a result, there is no method reported in the literature which has been successfully applied to automatically segment pathological muscles. We propose the first automated framework to delineate severely atrophied muscles by applying a novel automated segmentation propagation framework to patients with MoM hip arthroplasty. The proposed algorithm was used to automatically quantify muscle wastage in these patients.

Magnetic Resonance Imaging and conformal radiotherapy: Characterization of MRI alone simulation for conformal radiotherapy. Development and evaluation of an automatic volumes of interest segmentation tool for prostate cancer radiotherapy

International Nuclear Information System (INIS)

Pasquier, David

2006-01-01

Radiotherapy is a curative treatment of malignant tumours. Radiotherapy techniques considerably evolved last years with the increasing integration of medical images in conformal radiotherapy. This technique makes it possible to elaborate a complex ballistics conforming to target volume and sparing healthy tissues. The examination currently used to delineate volumes of interest is Computed Tomography (CT), on account of its geometrical precision and the information that it provides on electronic densities needed to dose calculation. Magnetic Resonance Imaging (MRI) ensures a more precise delineation of target volumes in many locations, such as pelvis and brain. For pelvic tumours, the use of MRI needs image registration, which complicates treatment planning and poses the problem of the lack of in vivo standard method of validation. The obstacles in the use of MRI alone in treatment planning were evaluated. Neither geometrical distortion linked with the system and the patient nor the lack of information on electronic densities represent stumbling obstacles. Distortion remained low even in edge of large field of view on modern machines. The assignment of electronic densities to bone structures and soft tissues in MR images permitted to obtain equivalent dosimetry to that carried out on the original CT, with a good reproducibility and homogeneous distribution within target volume. The assignment of electronic densities could not be carried out using 20 MV photons and suitable ballistics. The development of Image Guided Radiotherapy could facilitate the use of MRI alone in treatment planning. Target volumes and organ at risk delineation is a time consuming task in radiotherapy planning. We took part in the development and evaluated a method of automatic and semi automatic delineation of volumes of interest from MRI images for prostate cancer radiotherapy. For prostate and organ at risk automatic delineation an organ model-based method and a seeded region growing method

Computational text analysis and reading comprehension exam complexity towards automatic text classification

CERN Document Server

Liontou, Trisevgeni

2014-01-01

This book delineates a range of linguistic features that characterise the reading texts used at the B2 (Independent User) and C1 (Proficient User) levels of the Greek State Certificate of English Language Proficiency exams in order to help define text difficulty per level of competence. In addition, it examines whether specific reader variables influence test takers' perceptions of reading comprehension difficulty. The end product is a Text Classification Profile per level of competence and a formula for automatically estimating text difficulty and assigning levels to texts consistently and re

Automatic positioning control device for automatic control rod exchanger

International Nuclear Information System (INIS)

Nasu, Seiji; Sasaki, Masayoshi.

1982-01-01

Purpose: To attain accurate positioning for a control rod exchanger. Constitution: The present position for an automatic control rod exchanger is detected by a synchro generator. An aimed stopping position for the exchanger, a stop instruction range depending on the distantial operation delay in the control system and the inertia-running distance of the mechanical system, and a coincidence confirmation range depending on the required positioning accuracy are previously set. If there is a difference between the present position and the aimed stopping position, the automatic exchanger is caused to run toward the aimed stopping position. A stop instruction is generated upon arrival at the position within said stop instruction range, and a coincidence confirmation signal is generated upon arrival at the position within the coincidence confirmation range. Since uncertain factors such as operation delay in the control system and the inertia-running distance of the mechanical system that influence the positioning accuracy are made definite by the method of actual measurement or the like and the stop instruction range and the coincidence confirmation range are set based on the measured data, the accuracy for the positioning can be improved. (Ikeda, J.)

Towards an Automatic Framework for Urban Settlement Mapping from Satellite Images: Applications of Geo-referenced Social Media and One Class Classification

Science.gov (United States)

Miao, Zelang

2017-04-01

Currently, urban dwellers comprise more than half of the world's population and this percentage is still dramatically increasing. The explosive urban growth over the next two decades poses long-term profound impact on people as well as the environment. Accurate and up-to-date delineation of urban settlements plays a fundamental role in defining planning strategies and in supporting sustainable development of urban settlements. In order to provide adequate data about urban extents and land covers, classifying satellite data has become a common practice, usually with accurate enough results. Indeed, a number of supervised learning methods have proven effective in urban area classification, but they usually depend on a large amount of training samples, whose collection is a time and labor expensive task. This issue becomes particularly serious when classifying large areas at the regional/global level. As an alternative to manual ground truth collection, in this work we use geo-referenced social media data. Cities and densely populated areas are an extremely fertile land for the production of individual geo-referenced data (such as GPS and social network data). Training samples derived from geo-referenced social media have several advantages: they are easy to collect, usually they are freely exploitable; and, finally, data from social media are spatially available in many locations, and with no doubt in most urban areas around the world. Despite these advantages, the selection of training samples from social media meets two challenges: 1) there are many duplicated points; 2) method is required to automatically label them as "urban/non-urban". The objective of this research is to validate automatic sample selection from geo-referenced social media and its applicability in one class classification for urban extent mapping from satellite images. The findings in this study shed new light on social media applications in the field of remote sensing.

Automatic continuous dew point measurement in combustion gases

Energy Technology Data Exchange (ETDEWEB)

Fehler, D.

1986-08-01

Low exhaust temperatures serve to minimize energy consumption in combustion systems. This requires accurate, continuous measurement of exhaust condensation. An automatic dew point meter for continuous operation is described. The principle of measurement, the design of the measuring system, and practical aspects of operation are discussed.

Improved business driveway delineation in urban work zones.

Science.gov (United States)

2015-04-01

This report documents the efforts and results of a two-year research project aimed at improving driveway : delineation in work zones. The first year of the project included a closed-course study to identify the most : promising driveway delineation a...

Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation

Science.gov (United States)

Irshad, Mehreen; Muhammad, Nazeer; Sharif, Muhammad; Yasmeen, Mussarat

2018-04-01

Conventionally, cardiac MR image analysis is done manually. Automatic examination for analyzing images can replace the monotonous tasks of massive amounts of data to analyze the global and regional functions of the cardiac left ventricle (LV). This task is performed using MR images to calculate the analytic cardiac parameter like end-systolic volume, end-diastolic volume, ejection fraction, and myocardial mass, respectively. These analytic parameters depend upon genuine delineation of epicardial, endocardial, papillary muscle, and trabeculations contours. In this paper, we propose an automatic segmentation method using the sum of absolute differences technique to localize the left ventricle. Blind morphological operations are proposed to segment and detect the LV contours of the epicardium and endocardium, automatically. We test the benchmark Sunny Brook dataset for evaluation of the proposed work. Contours of epicardium and endocardium are compared quantitatively to determine contour's accuracy and observe high matching values. Similarity or overlapping of an automatic examination to the given ground truth analysis by an expert are observed with high accuracy as with an index value of 91.30% . The proposed method for automatic segmentation gives better performance relative to existing techniques in terms of accuracy.

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

Directory of Open Access Journals (Sweden)

Charles Gignac

2017-01-01

Full Text Available The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.

Rat brain digital stereotaxic white matter atlas with fine tract delineation in Paxinos space and its automated applications in DTI data analysis.

Science.gov (United States)

Liang, Shengxiang; Wu, Shang; Huang, Qi; Duan, Shaofeng; Liu, Hua; Li, Yuxiao; Zhao, Shujun; Nie, Binbin; Shan, Baoci

2017-11-01

To automatically analyze diffusion tensor images of the rat brain via both voxel-based and ROI-based approaches, we constructed a new white matter atlas of the rat brain with fine tracts delineation in the Paxinos and Watson space. Unlike in previous studies, we constructed a digital atlas image from the latest edition of the Paxinos and Watson. This atlas contains 111 carefully delineated white matter fibers. A white matter network of rat brain based on anatomy was constructed by locating the intersection of all these tracts and recording the nuclei on the pathway of each white matter tract. Moreover, a compatible rat brain template from DTI images was created and standardized into the atlas space. To evaluate the automated application of the atlas in DTI data analysis, a group of rats with right-side middle cerebral artery occlusion (MCAO) and those without were enrolled in this study. The voxel-based analysis result shows that the brain region showing significant declines in signal in the MCAO rats was consistent with the occlusion position. We constructed a stereotaxic white matter atlas of the rat brain with fine tract delineation and a compatible template for the data analysis of DTI images of the rat brain. Copyright © 2017 Elsevier Inc. All rights reserved.

Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance.

Science.gov (United States)

Sjögren, Jane; Ubachs, Joey F A; Engblom, Henrik; Carlsson, Marcus; Arheden, Håkan; Heiberg, Einar

2012-01-31

T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. MaR was 32.9 ± 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p Segment MaR, -2.3 ± 4.9%, R = 0.91 (p Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2

Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering

International Nuclear Information System (INIS)

Acton, P.D.; Pilowsky, L.S.; Kung, H.F.; Ell, P.J.

1999-01-01

The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D 2 /D 3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [ 123 I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r 2 >0.99, P 123 I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r 2 =0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner. (orig.)

Delineating Urban Fringe Area by Land Cover Information Entropy—An Empirical Study of Guangzhou-Foshan Metropolitan Area, China

Directory of Open Access Journals (Sweden)

Junyi Huang

2016-05-01

Full Text Available Rapid urbanization has caused many environmental problems, such as the heat island effect, intensifying air pollution, pollution from runoff, loss of wildlife habitat, etc. Accurate evaluations of these problems demand an accurate delineation of the spatial extent of the urban fringe. Conceptual and analytical ambiguity of the urban fringe and a general lack of consensus among researchers have made its measurement very difficult. This study reports a compound and reliable method to delineate the urban fringe area using a case study. Based on the 'fringe effect' theory in landscape ecology, the existing land cover information entropy model for defining the urban fringe is renewed by incorporating scale theory, cartography and urban geography theory. Results show that the urban fringe area of Guangzhou and Foshan metropolitan area covers an area of 2031 km2, and it occupies over 31% of the total study area. Result evaluation by industry structure data shows satisfactory correspondence with different land cover types. This paper reports the method and outcome of an attempt to provide an objective, repeatable and generally applicable method for mapping its spatial extent from remote sensing imageries, and could be beneficial to relevant urban studies and urban fringe management projects.

System for automatic x-ray-image analysis, measurement, and sorting of laser fusion targets

International Nuclear Information System (INIS)

Singleton, R.M.; Perkins, D.E.; Willenborg, D.L.

1980-01-01

This paper describes the Automatic X-Ray Image Analysis and Sorting (AXIAS) system which is designed to analyze and measure x-ray images of opaque hollow microspheres used as laser fusion targets. The x-ray images are first recorded on a high resolution film plate. The AXIAS system then digitizes and processes the images to accurately measure the target parameters and defects. The primary goals of the AXIAS system are: to provide extremely accurate and rapid measurements, to engineer a practical system for a routine production environment and to furnish the capability of automatically measuring an array of images for sorting and selection

Transit Traffic Analysis Zone Delineating Method Based on Thiessen Polygon

Directory of Open Access Journals (Sweden)

Shuwei Wang

2014-04-01

Full Text Available A green transportation system composed of transit, busses and bicycles could be a significant in alleviating traffic congestion. However, the inaccuracy of current transit ridership forecasting methods is imposing a negative impact on the development of urban transit systems. Traffic Analysis Zone (TAZ delineating is a fundamental and essential step in ridership forecasting, existing delineating method in four-step models have some problems in reflecting the travel characteristics of urban transit. This paper aims to come up with a Transit Traffic Analysis Zone delineation method as supplement of traditional TAZs in transit service analysis. The deficiencies of current TAZ delineating methods were analyzed, and the requirements of Transit Traffic Analysis Zone (TTAZ were summarized. Considering these requirements, Thiessen Polygon was introduced into TTAZ delineating. In order to validate its feasibility, Beijing was then taken as an example to delineate TTAZs, followed by a spatial analysis of office buildings within a TTAZ and transit station departure passengers. Analysis result shows that the TTAZs based on Thiessen polygon could reflect the transit travel characteristic and is of in-depth research value.

Current Trends in Intraoperative Optical Imaging for Functional Brain Mapping and Delineation of Lesions of Language Cortex

Science.gov (United States)

Prakash, Neal; Uhleman, Falk; Sheth, Sameer A.; Bookheimer, Susan; Martin, Neil; Toga, Arthur W.

2009-01-01

Resection of a cerebral arteriovenous malformation (AVM), epileptic focus, or glioma, ideally has a prerequisite of microscopic delineation of the lesion borders in relation to the normal gray and white matter that mediate critical functions. Currently, Wada testing and functional magnetic resonance imaging (fMRI) are used for preoperative mapping of critical function, whereas electrical stimulation mapping (ESM) is used for intraoperative mapping. For lesion delineation, MRI and positron emission tomography (PET) are used preoperatively, whereas microscopy and histological sectioning are used intraoperatively. However, for lesions near eloquent cortex, these imaging techniques may lack sufficient resolution to define the relationship between the lesion and language function, and thus not accurately determine which patients will benefit from neurosurgical resection of the lesion without iatrogenic aphasia. Optical techniques such as intraoperative optical imaging of intrinsic signals (iOIS) show great promise for the precise functional mapping of cortices, as well as delineation of the borders of AVMs, epileptic foci, and gliomas. Here we first review the physiology of neuroimaging, and then progress towards the validation and justification of using intraoperative optical techniques, especially in relation to neurosurgical planning of resection AVMs, epileptic foci, and gliomas near or in eloquent cortex. We conclude with a short description of potential novel intraoperative optical techniques. PMID:18786643

TU-C-17A-04: BEST IN PHYSICS (THERAPY) - A Supervised Framework for Automatic Contour Assessment for Radiotherapy Planning of Head- Neck Cancer

Energy Technology Data Exchange (ETDEWEB)

Chen, H; Kavanaugh, J; Tan, J; Dolly, S; Gay, H; Thorstad, W; Anastasio, M; Altman, M; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States)

2014-06-15

Purpose: Precise contour delineation of tumor targets and critical structures from CT simulations is essential for accurate radiotherapy (RT) treatment planning. However, manual and automatic delineation processes can be error prone due to limitations in imaging techniques and individual anatomic variability. Tedious and laborious manual verification is hence needed. This study develops a general framework for automatically assessing RT contours for head-neck cancer patients using geometric attribute distribution models (GADMs). Methods: Geometric attributes (centroid and volume) were computed from physician-approved RT contours of 29 head-neck patients. Considering anatomical correlation between neighboring structures, the GADM for each attribute was trained to characterize intra- and interpatient structure variations using principal component analysis. Each trained GADM was scalable and deformable, but constrained by the principal attribute variations of the training contours. A new hierarchical model adaptation algorithm was utilized to assess the RT contour correctness for a given patient. Receiver operating characteristic (ROC) curves were employed to evaluate and tune system parameters for the training models. Results: Experiments utilizing training and non-training data sets with simulated contouring errors were conducted to validate the framework performance. Promising assessment results of contour normality/abnormality for the training contour-based data were achieved with excellent accuracy (0.99), precision (0.99), recall (0.83), and F-score (0.97), while corresponding values of 0.84, 0.96, 0.83, and 0.9 were achieved for the non-training data. Furthermore, the areas under the ROC curves were above 0.9, validating the accuracy of this test. Conclusion: The proposed framework can reliably identify contour normality/abnormality based upon intra- and inter-structure constraints derived from clinically-approved contours. It also allows physicians to

CAD-based automatic modeling method for Geant4 geometry model through MCAM

International Nuclear Information System (INIS)

Wang, D.; Nie, F.; Wang, G.; Long, P.; LV, Z.

2013-01-01

The full text of publication follows. Geant4 is a widely used Monte Carlo transport simulation package. Before calculating using Geant4, the calculation model need be established which could be described by using Geometry Description Markup Language (GDML) or C++ language. However, it is time-consuming and error-prone to manually describe the models by GDML. Automatic modeling methods have been developed recently, but there are some problems that exist in most present modeling programs, specially some of them were not accurate or adapted to specifically CAD format. To convert the GDML format models to CAD format accurately, a Geant4 Computer Aided Design (CAD) based modeling method was developed for automatically converting complex CAD geometry model into GDML geometry model. The essence of this method was dealing with CAD model represented with boundary representation (B-REP) and GDML model represented with constructive solid geometry (CSG). At first, CAD model was decomposed to several simple solids which had only one close shell. And then the simple solid was decomposed to convex shell set. Then corresponding GDML convex basic solids were generated by the boundary surfaces getting from the topological characteristic of a convex shell. After the generation of these solids, GDML model was accomplished with series boolean operations. This method was adopted in CAD/Image-based Automatic Modeling Program for Neutronics and Radiation Transport (MCAM), and tested with several models including the examples in Geant4 install package. The results showed that this method could convert standard CAD model accurately, and can be used for Geant4 automatic modeling. (authors)

Automatic control variac system for electronic accelerator

International Nuclear Information System (INIS)

Zhang Shuocheng; Wang Dan; Jing Lan; Qiao Weimin; Ma Yunhai

2006-01-01

An automatic control variac system is designed in order to satisfy the controlling requirement of the electronic accelerator developed by the Institute. Both design and operational principles, structure of the system as well as the software of industrial PC and micro controller unit are described. The interfaces of the control module are RS232 and RS485. A fiber optical interface (FOC) could be set up if an industrial FOC network is necessary, which will extend the filed of its application and make the communication of the system better. It is shown in practice that the system can adjust the variac output voltage automatically and assure the accurate and automatic control of the electronic accelerator. The system is designed in accordance with the general design principles and possesses the merits such as easy operation and maintenance, good expansibility, and low cost, thus it could also be used in other industrial branches. (authors)

Automatic 3D modeling of the urban landscape

NARCIS (Netherlands)

Esteban, I.; Dijk, J.; Groen, F.

2010-01-01

In this paper we present a fully automatic system for building 3D models of urban areas at the street level. We propose a novel approach for the accurate estimation of the scale consistent camera pose given two previous images. We employ a new method for global optimization and use a novel sampling

Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

Science.gov (United States)

Litkey, P.; Nurminen, K.; Honkavaara, E.

2013-05-01

The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control

Directory of Open Access Journals (Sweden)

Yuki Minami

2018-04-01

Full Text Available In recent years, automatic driving control has attracted attention. To achieve a satisfactory driving control performance, the prediction accuracy of the traveling route is important. If a highly accurate prediction method can be used, an accurate traveling route can be obtained. Despite the considerable efforts that have been invested in improving prediction methods, prediction errors do occur in general. Thus, a method to minimize the influence of prediction errors on automatic driving control systems is required. This need motivated us to focus on the design of a mechanism for shaping prediction signals, which is called a prediction governor. In this study, we first extended our previous study to the input-affine nonlinear system case. Then, we analytically derived a solution to an optimal design problem of prediction governors. Finally, we applied the solution to an automatic driving control system, and demonstrated its usefulness through a numerical example and an experiment using a radio controlled car.

AuTom: a novel automatic platform for electron tomography reconstruction

KAUST Repository

Han, Renmin

2017-07-26

We have developed a software package towards automatic electron tomography (ET): Automatic Tomography (AuTom). The presented package has the following characteristics: accurate alignment modules for marker-free datasets containing substantial biological structures; fully automatic alignment modules for datasets with fiducial markers; wide coverage of reconstruction methods including a new iterative method based on the compressed-sensing theory that suppresses the “missing wedge” effect; and multi-platform acceleration solutions that support faster iterative algebraic reconstruction. AuTom aims to achieve fully automatic alignment and reconstruction for electron tomography and has already been successful for a variety of datasets. AuTom also offers user-friendly interface and auxiliary designs for file management and workflow management, in which fiducial marker-based datasets and marker-free datasets are addressed with totally different subprocesses. With all of these features, AuTom can serve as a convenient and effective tool for processing in electron tomography.

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

Science.gov (United States)

Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

2017-03-01

Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

Automatic Segmentation of Dermoscopic Images by Iterative Classification

Directory of Open Access Journals (Sweden)

Maciel Zortea

2011-01-01

Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

Automatic segmentation of equine larynx for diagnosis of laryngeal hemiplegia

Science.gov (United States)

Salehin, Md. Musfequs; Zheng, Lihong; Gao, Junbin

2013-10-01

This paper presents an automatic segmentation method for delineation of the clinically significant contours of the equine larynx from an endoscopic image. These contours are used to diagnose the most common disease of horse larynx laryngeal hemiplegia. In this study, hierarchal structured contour map is obtained by the state-of-the-art segmentation algorithm, gPb-OWT-UCM. The conic-shaped outer boundary of equine larynx is extracted based on Pascal's theorem. Lastly, Hough Transformation method is applied to detect lines related to the edges of vocal folds. The experimental results show that the proposed approach has better performance in extracting the targeted contours of equine larynx than the results of using only the gPb-OWT-UCM method.

Sequential Versus Simultaneous Market Delineation: The Relevant Antitrust Market for Salmon

DEFF Research Database (Denmark)

Haldrup, Niels; Peter, Møllgaard

Delineation of the relevant market forms a pivotal part of most antitrust cases. The standard approach is sequential. First the product market is delineated, then the geographical market is defined. Demand andsupply substitution in both the product dimension and the geographical dimension will no...... and geographical markets. Using a unique data set for prices of Norwegian and Scottish salmon, we propose a methodology for simultaneous market delineation and we demonstrate that compared to a sequential approach conclusions will be reversed.......Delineation of the relevant market forms a pivotal part of most antitrust cases. The standard approach is sequential. First the product market is delineated, then the geographical market is defined. Demand andsupply substitution in both the product dimension and the geographical dimension...

Creation of voxel-based models for paediatric dosimetry from automatic segmentation methods

International Nuclear Information System (INIS)

Acosta, O.; Li, R.; Ourselin, S.; Caon, M.

2006-01-01

Full text: The first computational models representing human anatomy were mathematical phantoms, but still far from accurate representations of human body. These models have been used with radiation transport codes (Monte Carlo) to estimate organ doses from radiological procedures. Although new medical imaging techniques have recently allowed the construction of voxel-based models based on the real anatomy, few children models from individual CT or MRI data have been reported [1,3]. For pediatric dosimetry purposes, a large range of voxel models by ages is required since scaling the anatomy from existing models is not sufficiently accurate. The small number of models available arises from the small number of CT or MRI data sets of children available and the long amount of time required to segment the data sets. The existing models have been constructed by manual segmentation slice by slice and using simple thresholding techniques. In medical image segmentation, considerable difficulties appear when applying classical techniques like thresholding or simple edge detection. Until now, any evidence of more accurate or near-automatic methods used in construction of child voxel models exists. We aim to construct a range of pediatric voxel models, integrating automatic or semi-automatic 3D segmentation techniques. In this paper we present the first stage of this work using pediatric CT data.

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

International Nuclear Information System (INIS)

Kim, J; Han, J; Ailawadi, S; Baker, J; Hsia, A; Xu, Z; Ryu, S

2016-01-01

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

Energy Technology Data Exchange (ETDEWEB)

Kim, J; Han, J; Ailawadi, S; Baker, J; Hsia, A; Xu, Z; Ryu, S [Stony Brook University Hospital, Stony Brook, NY (United States)

2016-06-15

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.

Description and application of capture zone delineation for a wellfield at Hilton Head Island, South Carolina

Science.gov (United States)

Landmeyer, J.E.

1994-01-01

Ground-water capture zone boundaries for individual pumped wells in a confined aquffer were delineated by using groundwater models. Both analytical and numerical (semi-analytical) models that more accurately represent the $round-water-flow system were used. All models delineated 2-dimensional boundaries (capture zones) that represent the areal extent of groundwater contribution to a pumped well. The resultant capture zones were evaluated on the basis of the ability of each model to realistically rapresent the part of the ground-water-flow system that contributed water to the pumped wells. Analytical models used were based on a fixed radius approach, and induded; an arbitrary radius model, a calculated fixed radius model based on the volumetric-flow equation with a time-of-travel criterion, and a calculated fixed radius model derived from modification of the Theis model with a drawdown criterion. Numerical models used induded the 2-dimensional, finite-difference models RESSQC and MWCAP. The arbitrary radius and Theis analytical models delineated capture zone boundaries that compared least favorably with capture zones delineated using the volumetric-flow analytical model and both numerical models. The numerical models produced more hydrologically reasonable capture zones (that were oriented parallel to the regional flow direction) than the volumetric-flow equation. The RESSQC numerical model computed more hydrologically realistic capture zones than the MWCAP numerical model by accounting for changes in the shape of capture zones caused by multiple-well interference. The capture zone boundaries generated by using both analytical and numerical models indicated that the curnmtly used 100-foot radius of protection around a wellhead in South Carolina is an underestimate of the extent of ground-water capture for pumped wetis in this particular wellfield in the Upper Floridan aquifer. The arbitrary fixed radius of 100 feet was shown to underestimate the upgradient

FDG–PET–CT reduces the interobserver variability in rectal tumor delineation

International Nuclear Information System (INIS)

Buijsen, Jeroen; Bogaard, Jørgen van den; Weide, Hiska van der; Engelsman, Stephanie; Stiphout, Ruud van; Janssen, Marco; Beets, Geerard; Beets-Tan, Regina; Lambin, Philippe; Lammering, Guido

2012-01-01

Background and purpose: Previously, we showed a good correlation between pathology and an automatically generated PET-contour in rectal cancer. This study analyzed the effect of the use of PET–CT scan on the interobserver variation in GTV definition in rectal cancer and the influence of PET–CT on treatment volumes. Materials and methods: Forty two patients diagnosed with rectal cancer underwent an FDG–PET–CT for radiotherapy planning. An automatic contour was created on PET-scan using the source-to-background ratio. The GTV was delineated by 5 observers in 3 rounds: using CT and MRI, using CT, MRI and PET and using CT, MRI and PET auto-contour. GTV volumes were compared and concordance indices (CI) were calculated. Since the GTV is only a small portion of the treatment volume in rectal cancer, a separate analysis was performed to evaluate the influence of PET on the definition of the CTV used in daily clinical practice and the caudal extension of the treatment volumes. Results: GTV volumes based on PET were significantly smaller. CIs increased significantly using PET and the best interobserver agreement was observed using PET auto-contours. Furthermore, we found that in up to 29% of patients the CTV based on PET extended outside the CTV used in clinical practice. The caudal border of the treatment volume can be tailored using PET-scan in low seated tumors. Influence of PET on the position of the caudal border was most pronounced in high seated tumors. Conclusion: PET–CT increases the interobserver agreement in the GTV definition in rectal cancer, helps to avoid geographical misses and allows tailoring the caudal border of the treatment volume.

Recommendations for the delineation of organs at risk in ENT radiotherapy; Recommandations de delineation des organes a risque en radiotherapie ORL

Energy Technology Data Exchange (ETDEWEB)

Ali, D.; Halimi, P.; Berges, O.; Deberne, M.; Botti, M.; Giraud, P. [Hopital europeen Georges-Pompidou, Paris (France); Servagi-Vernat, S. [CHUjean-Minjoz, Besancon (France)

2011-10-15

Based on a literature survey, the authors propose recommendations for the delineation of the pharyngeal constrictor muscles, inner ear, larynx, buccal cavity, and temporomandibular joint. These recommendations of delineation of organs at risk are related to the functional anatomy of the considered structures, and correspond to volumes used in published surveys on dose-volume toxicity. They are simple and reproducible. Short communication

Automatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients.

Science.gov (United States)

Wang, Jieqiong; Miao, Wen; Li, Jing; Li, Meng; Zhen, Zonglei; Sabel, Bernhard; Xian, Junfang; He, Huiguang

2015-11-30

The lateral geniculate nucleus (LGN) is a key relay center of the visual system. Because the LGN morphology is affected by different diseases, it is of interest to analyze its morphology by segmentation. However, existing LGN segmentation methods are non-automatic, inefficient and prone to experimenters' bias. To address these problems, we proposed an automatic LGN segmentation algorithm based on T1-weighted imaging. First, the prior information of LGN was used to create a prior mask. Then region growing was applied to delineate LGN. We evaluated this automatic LGN segmentation method by (1) comparison with manually segmented LGN, (2) anatomically locating LGN in the visual system via LGN-based tractography, (3) application to control and glaucoma patients. The similarity coefficients of automatic segmented LGN and manually segmented one are 0.72 (0.06) for the left LGN and 0.77 (0.07) for the right LGN. LGN-based tractography shows the subcortical pathway seeding from LGN passes the optic tract and also reaches V1 through the optic radiation, which is consistent with the LGN location in the visual system. In addition, LGN asymmetry as well as LGN atrophy along with age is observed in normal controls. The investigation of glaucoma effects on LGN volumes demonstrates that the bilateral LGN volumes shrink in patients. The automatic LGN segmentation is objective, efficient, valid and applicable. Experiment results proved the validity and applicability of the algorithm. Our method will speed up the research on visual system and greatly enhance studies of different vision-related diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

A web based semi automatic frame work for astrobiological researches

Directory of Open Access Journals (Sweden)

P.V. Arun

2013-12-01

Full Text Available Astrobiology addresses the possibility of extraterrestrial life and explores measures towards its recognition. Researches in this context are founded upon the premise that indicators of life encountered in space will be recognizable. However, effective recognition can be accomplished through a universal adaptation of life signatures without restricting solely to those attributes that represent local solutions to the challenges of survival. The life indicators should be modelled with reference to temporal and environmental variations specific to each planet and time. In this paper, we investigate a semi-automatic open source frame work for the accurate detection and interpretation of life signatures by facilitating public participation, in a similar way as adopted by SETI@home project. The involvement of public in identifying patterns can bring a thrust to the mission and is implemented using semi-automatic framework. Different advanced intelligent methodologies may augment the integration of this human machine analysis. Automatic and manual evaluations along with dynamic learning strategy have been adopted to provide accurate results. The system also helps to provide a deep public understanding about space agency’s works and facilitate a mass involvement in the astrobiological studies. It will surely help to motivate young eager minds to pursue a career in this field.

Interobserver delineation variation in lung tumour stereotacticbody radiotherapy

DEFF Research Database (Denmark)

Persson, G. F.; Nygaard, D. E.; Hollensen, Christian

2012-01-01

the interobserver delineation variation for stereotactic body radiotherapy (SBRT) of peripheral lung tumours using a cross-sectional study design. Methods 22 consecutive patients with 26 tumours were included. Positron emission tomography/CT scans were acquired for planning of SBRT. Three oncologists and three......-sectional analysis of delineation variation for peripheral lung tumours referred for SBRT, establishing the evidence that interobserver variation is very small for these tumours....

Delineation of gravel-bed clusters via factorial kriging

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Wu, Fu-Chun; Wang, Chi-Kuei; Huang, Guo-Hao

2018-05-01

Gravel-bed clusters are the most prevalent microforms that affect local flows and sediment transport. A growing consensus is that the practice of cluster delineation should be based primarily on bed topography rather than grain sizes. Here we present a novel approach for cluster delineation using patch-scale high-resolution digital elevation models (DEMs). We use a geostatistical interpolation method, i.e., factorial kriging, to decompose the short- and long-range (grain- and microform-scale) DEMs. The required parameters are determined directly from the scales of the nested variograms. The short-range DEM exhibits a flat bed topography, yet individual grains are sharply outlined, making the short-range DEM a useful aid for grain segmentation. The long-range DEM exhibits a smoother topography than the original full DEM, yet groupings of particles emerge as small-scale bedforms, making the contour percentile levels of the long-range DEM a useful tool for cluster identification. Individual clusters are delineated using the segmented grains and identified clusters via a range of contour percentile levels. Our results reveal that the density and total area of delineated clusters decrease with increasing contour percentile level, while the mean grain size of clusters and average size of anchor clast (i.e., the largest particle in a cluster) increase with the contour percentile level. These results support the interpretation that larger particles group as clusters and protrude higher above the bed than other smaller grains. A striking feature of the delineated clusters is that anchor clasts are invariably greater than the D90 of the grain sizes even though a threshold anchor size was not adopted herein. The average areal fractal dimensions (Hausdorff-Besicovich dimensions of the projected areas) of individual clusters, however, demonstrate that clusters delineated with different contour percentile levels exhibit similar planform morphologies. Comparisons with a

Automatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholding

Science.gov (United States)

Ansari, Muhammad Ahsan; Zai, Sammer; Moon, Young Shik

2017-01-01

Manual analysis of the bulk data generated by computed tomography angiography (CTA) is time consuming, and interpretation of such data requires previous knowledge and expertise of the radiologist. Therefore, an automatic method that can isolate the coronary arteries from a given CTA dataset is required. We present an automatic yet effective segmentation method to delineate the coronary arteries from a three-dimensional CTA data cloud. Instead of a region growing process, which is usually time consuming and prone to leakages, the method is based on the optimal thresholding, which is applied globally on the Hessian-based vesselness measure in a localized way (slice by slice) to track the coronaries carefully to their distal ends. Moreover, to make the process automatic, we detect the aorta using the Hough transform technique. The proposed segmentation method is independent of the starting point to initiate its process and is fast in the sense that coronary arteries are obtained without any preprocessing or postprocessing steps. We used 12 real clinical datasets to show the efficiency and accuracy of the presented method. Experimental results reveal that the proposed method achieves 95% average accuracy.

Geomorphic Flood Area (GFA): a QGIS tool for a cost-effective delineation of the floodplains

Science.gov (United States)

Samela, Caterina; Albano, Raffaele; Sole, Aurelia; Manfreda, Salvatore

2017-04-01

The importance of delineating flood hazard and risk areas at a global scale has been highlighted for many years. However, its complete achievement regularly encounters practical difficulties, above all the lack of data and implementation costs. In conditions of scarce data availability (e.g. ungauged basins, large-scale analyses), a fast and cost-effective floodplain delineation can be carried out using geomorphic methods (e.g., Manfreda et al., 2011; 2014). In particular, an automatic DEM-based procedure has been implemented in an open-source QGIS plugin named Geomorphic Flood Area - tool (GFA - tool). This tool performs a linear binary classification based on the recently proposed Geomorphic Flood Index (GFI), which exhibited high classification accuracy and reliability in several test sites located in Europe, United States and Africa (Manfreda et al., 2015; Samela et al., 2016, 2017; Samela, 2016). The GFA - tool is designed to make available to all users the proposed procedure, that includes a number of operations requiring good geomorphic and GIS competences. It allows computing the GFI through terrain analysis, turning it into a binary classifier, and training it on the base of a standard inundation map derived for a portion of the river basin (a minimum of 2% of the river basin's area is suggested) using detailed methods of analysis (e.g. flood hazard maps produced by emergency management agencies or river basin authorities). Finally, GFA - tool allows to extend the classification outside the calibration area to delineate the flood-prone areas across the entire river basin. The full analysis has been implemented in this plugin with a user-friendly interface that should make it easy to all user to apply the approach and produce the desired results. Keywords: flood susceptibility; data scarce environments; geomorphic flood index; linear binary classification; Digital elevation models (DEMs). References Manfreda, S., Di Leo, M., Sole, A., (2011). Detection of

Automatic orbital GTAW welding: Highest quality welds for tomorrow's high-performance systems

Science.gov (United States)

Henon, B. K.

1985-01-01

Automatic orbital gas tungsten arc welding (GTAW) or TIG welding is certain to play an increasingly prominent role in tomorrow's technology. The welds are of the highest quality and the repeatability of automatic weldings is vastly superior to that of manual welding. Since less heat is applied to the weld during automatic welding than manual welding, there is less change in the metallurgical properties of the parent material. The possibility of accurate control and the cleanliness of the automatic GTAW welding process make it highly suitable to the welding of the more exotic and expensive materials which are now widely used in the aerospace and hydrospace industries. Titanium, stainless steel, Inconel, and Incoloy, as well as, aluminum can all be welded to the highest quality specifications automatically. Automatic orbital GTAW equipment is available for the fusion butt welding of tube-to-tube, as well as, tube to autobuttweld fittings. The same equipment can also be used for the fusion butt welding of up to 6 inch pipe with a wall thickness of up to 0.154 inches.

PET-CT-Based Auto-Contouring in Non-Small-Cell Lung Cancer Correlates With Pathology and Reduces Interobserver Variability in the Delineation of the Primary Tumor and Involved Nodal Volumes

International Nuclear Information System (INIS)

Baardwijk, Angela van; Bosmans, Geert; Boersma, Liesbeth; Buijsen, Jeroen; Wanders, Stofferinus; Hochstenbag, Monique; Suylen, Robert-Jan van; Dekker, Andre; Dehing-Oberije, Cary; Houben, Ruud; Bentzen, Soren M.; Kroonenburgh, Marinus van; Lambin, Philippe; Ruysscher, Dirk de

2007-01-01

Purpose: To compare source-to-background ratio (SBR)-based PET-CT auto-delineation with pathology in non-small-cell lung cancer (NSCLC) and to investigate whether auto-delineation reduces the interobserver variability compared with manual PET-CT-based gross tumor volume (GTV) delineation. Methods and Materials: Source-to-background ratio-based auto-delineation was compared with macroscopic tumor dimensions to assess its validity in 23 tumors. Thereafter, GTVs were delineated manually on 33 PET-CT scans by five observers for the primary tumor (GTV-1) and the involved lymph nodes (GTV-2). The delineation was repeated after 6 months with the auto-contour provided. This contour was edited by the observers. For comparison, the concordance index (CI) was calculated, defined as the ratio of intersection and the union of two volumes (A intersection B)/(A union B). Results: The maximal tumor diameter of the SBR-based auto-contour correlated strongly with the macroscopic diameter of primary tumors (correlation coefficient = 0.90) and was shown to be accurate for involved lymph nodes (sensitivity 67%, specificity 95%). The median auto-contour-based target volumes were smaller than those defined by manual delineation for GTV-1 (31.8 and 34.6 cm 3 , respectively; p = 0.001) and GTV-2 (16.3 and 21.8 cm 3 , respectively; p 0.02). The auto-contour-based method showed higher CIs than the manual method for GTV-1 (0.74 and 0.70 cm 3 , respectively; p 3 , respectively; p = 0.11). Conclusion: Source-to-background ratio-based auto-delineation showed a good correlation with pathology, decreased the delineated volumes of the GTVs, and reduced the interobserver variability. Auto-contouring may further improve the quality of target delineation in NSCLC patients

An Approximate Approach to Automatic Kernel Selection.

Science.gov (United States)

Ding, Lizhong; Liao, Shizhong

2016-02-02

Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

DEFF Research Database (Denmark)

Saygin, Z M; Kliemann, D; Iglesias, J. E.

2017-01-01

The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high...... resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently...... developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE...

Automatic Shadow Detection and Removal from a Single Image.

Science.gov (United States)

Khan, Salman H; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto

2016-03-01

We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.

A novel algorithm for delineating wetland depressions and ...

Science.gov (United States)

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features that are seldom fully filled with water. For instance, wetland depressions in the Prairie Pothole Region (PPR) are seasonally to permanently flooded wetlands characterized by nested hierarchical structures with dynamic filling- spilling-merging surface-water hydrological processes. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution LiDAR data and aerial imagery. We proposed a novel algorithm delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost path algorithm. The resulting flow network delineated putative temporary or seasonal flow paths connecting wetland depressions to each other or to the river network at scales finer than available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow modeling and hydrologic connectivity analysis. Presentation at AWRA Spring Specialty Conference in Sn

Delineating SPTAN1 associated phenotypes

DEFF Research Database (Denmark)

Syrbe, Steffen; Harms, Frederike L; Parrini, Elena

2017-01-01

De novo in-frame deletions and duplications in the SPTAN1 gene, encoding the non-erythrocyte αII spectrin, have been associated with severe West syndrome with hypomyelination and pontocerebellar atrophy. We aimed at comprehensively delineating the phenotypic spectrum associated with SPTAN1 mutati...

Automatic modulation recognition of communication signals

CERN Document Server

Azzouz, Elsayed Elsayed

1996-01-01

Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a cr...

Barcoding against a paradox? Combined molecular species delineations reveal multiple cryptic lineages in elusive meiofaunal sea slugs

Directory of Open Access Journals (Sweden)

Jörger Katharina M

2012-12-01

Full Text Available Abstract Background Many marine meiofaunal species are reported to have wide distributions, which creates a paradox considering their hypothesized low dispersal abilities. Correlated with this paradox is an especially high taxonomic deficit for meiofauna, partly related to a lower taxonomic effort and partly to a high number of putative cryptic species. Molecular-based species delineation and barcoding approaches have been advocated for meiofaunal biodiversity assessments to speed up description processes and uncover cryptic lineages. However, these approaches show sensitivity to sampling coverage (taxonomic and geographic and the success rate has never been explored on mesopsammic Mollusca. Results We collected the meiofaunal sea-slug Pontohedyle (Acochlidia, Heterobranchia from 28 localities worldwide. With a traditional morphological approach, all specimens fall into two morphospecies. However, with a multi-marker genetic approach, we reveal multiple lineages that are reciprocally monophyletic on single and concatenated gene trees in phylogenetic analyses. These lineages are largely concordant with geographical and oceanographic parameters, leading to our primary species hypothesis (PSH. In parallel, we apply four independent methods of molecular based species delineation: General Mixed Yule Coalescent model (GMYC, statistical parsimony, Bayesian Species Delineation (BPP and Automatic Barcode Gap Discovery (ABGD. The secondary species hypothesis (SSH is gained by relying only on uncontradicted results of the different approaches (‘minimum consensus approach’, resulting in the discovery of a radiation of (at least 12 mainly cryptic species, 9 of them new to science, some sympatric and some allopatric with respect to ocean boundaries. However, the meiofaunal paradox still persists in some Pontohedyle species identified here with wide coastal and trans-archipelago distributions. Conclusions Our study confirms extensive, morphologically

Design of an automatic sample changer for the measurement of neutron flux by gamma spectrometry

International Nuclear Information System (INIS)

Gago, Javier; Bruna, Ruben; Baltuano, Oscar; Montoya, Eduardo; Descreaux, Killian

2014-01-01

This paper presents calculus, selection and components design for the construction of an automatic system in order to measure neutron flux in a working nuclear reactor by the gamma spectrometry technique using samples irradiated on the RP-10 nucleus. This system will perform the measurement of interchanging 100 samples in a programed and automatic way, reducing operation time by the user and obtaining more accurate measures. (authors).

MRI target delineation may reduce long-term toxicity after prostate radiotherapy.

Science.gov (United States)

Sander, Lotte; Langkilde, Niels Christian; Holmberg, Mats; Carl, Jesper

2014-06-01

Aiming for minimal toxicity after radical prostate cancer (PC) radiotherapy (RT), magnetic resonance imaging (MRI) target delineation could be a possible benefit knowing that clinical target volumes (CTV) are up to 30% smaller, when CTV delineation on MRI is compared to standard computed tomography (CT). This study compares long-term toxicity using CT or MRI delineation before PC RT. Urinary and rectal toxicity assessments 36 months after image-guided RT (78 Gy) using CTC-AE scores in two groups of PC patients. Peak symptom score values were registered. One group of patients (n=72) had standard CT target delineation and gold markers as fiducials. Another group of patients (n=73) had MRI target delineation and a nickel-titanium stent as fiducial. At 36 months no difference in overall survival (92% in both groups, p=0.29) or in PSA-relapse free survival was found between the groups (MRI=89% and CT=94%, p=0.67). A significantly smaller CTV was found in the MRI group (p=0.02). Urinary retention and frequency were significantly reduced in the MRI group (p=0.03 in the matter of both). The overall urinary and rectal toxicity did not differ between the two groups. MRI delineation leads to a significantly reduced CTV. Significantly lower urinary frequency and urinary retention toxicity scores were observed following MRI delineation. The study did not find significant differences in overall urinary or rectal toxicity between the two groups. PSA-relapse survival did not differ between the two groups at 36 months.

Automatic radiation dose monitoring for CT of trauma patients with different protocols: feasibility and accuracy

International Nuclear Information System (INIS)

Higashigaito, K.; Becker, A.S.; Sprengel, K.; Simmen, H.-P.; Wanner, G.; Alkadhi, H.

2016-01-01

Aim: To demonstrate the feasibility and accuracy of automatic radiation dose monitoring software for computed tomography (CT) of trauma patients in a clinical setting over time, and to evaluate the potential of radiation dose reduction using iterative reconstruction (IR). Materials and methods: In a time period of 18 months, data from 378 consecutive thoraco-abdominal CT examinations of trauma patients were extracted using automatic radiation dose monitoring software, and patients were split into three cohorts: cohort 1, 64-section CT with filtered back projection, 200 mAs tube current–time product; cohort 2, 128-section CT with IR and identical imaging protocol; cohort 3, 128-section CT with IR, 150 mAs tube current–time product. Radiation dose parameters from the software were compared with the individual patient protocols. Image noise was measured and image quality was semi-quantitatively determined. Results: Automatic extraction of radiation dose metrics was feasible and accurate in all (100%) patients. All CT examinations were of diagnostic quality. There were no differences between cohorts 1 and 2 regarding volume CT dose index (CTDI_v_o_l; p=0.62), dose–length product (DLP), and effective dose (ED, both p=0.95), while noise was significantly lower (chest and abdomen, both −38%, p<0.017). Compared to cohort 1, CTDI_v_o_l, DLP, and ED in cohort 3 were significantly lower (all −25%, p<0.017), similar to the noise in the chest (–32%) and abdomen (–27%, both p<0.017). Compared to cohort 2, CTDI_v_o_l (–28%), DLP, and ED (both –26%) in cohort 3 was significantly lower (all, p<0.017), while noise in the chest (+9%) and abdomen (+18%) was significantly higher (all, p<0.017). Conclusion: Automatic radiation dose monitoring software is feasible and accurate, and can be implemented in a clinical setting for evaluating the effects of lowering radiation doses of CT protocols over time. - Highlights: • Automatic dose monitoring software can be

Application of image recognition-based automatic hyphae detection in fungal keratitis.

Science.gov (United States)

Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

2018-03-01

microscope corneal images, of being accurate, stable and does not rely on human expertise. It was the most useful to the medical experts who are not familiar with fungal keratitis. The technology of automatic hyphae detection based on image recognition can quantify the hyphae density and grade this property. Being noninvasive, it can provide an evaluation criterion to fungal keratitis in a timely, accurate, objective and quantitative manner.

Recent advances in probabilistic species pool delineations

Directory of Open Access Journals (Sweden)

Dirk Nikolaus Karger

2016-07-01

Full Text Available A species pool is the set of species that could potentially colonize and establish within a community. It has been a commonly used concept in biogeography since the early days of MacArthur and Wilson’s work on Island Biogeography. Despite their simple and appealing definition, an operational application of species pools is bundled with a multitude of problems, which have often resulted in arbitrary decisions and workarounds when defining species pools. Two recently published papers address the operational problems of species pool delineations, and show ways of delineating them in a probabilistic fashion. In both papers, species pools were delineated using a process-based, mechanistical approach, which opens the door for a multitude of new applications in biogeography. Such applications include detecting the hidden signature of biotic interactions, disentangling the geographical structure of community assembly processes, and incorporating a temporal extent into species pools. Although similar in their conclusions, both ‘probabilistic approaches’ differ in their implementation and definitions. Here I give a brief overview of the differences and similarities of both approaches, and identify the challenges and advantages in their application.

A semi-automatic annotation tool for cooking video

Science.gov (United States)

Bianco, Simone; Ciocca, Gianluigi; Napoletano, Paolo; Schettini, Raimondo; Margherita, Roberto; Marini, Gianluca; Gianforme, Giorgio; Pantaleo, Giuseppe

2013-03-01

In order to create a cooking assistant application to guide the users in the preparation of the dishes relevant to their profile diets and food preferences, it is necessary to accurately annotate the video recipes, identifying and tracking the foods of the cook. These videos present particular annotation challenges such as frequent occlusions, food appearance changes, etc. Manually annotate the videos is a time-consuming, tedious and error-prone task. Fully automatic tools that integrate computer vision algorithms to extract and identify the elements of interest are not error free, and false positive and false negative detections need to be corrected in a post-processing stage. We present an interactive, semi-automatic tool for the annotation of cooking videos that integrates computer vision techniques under the supervision of the user. The annotation accuracy is increased with respect to completely automatic tools and the human effort is reduced with respect to completely manual ones. The performance and usability of the proposed tool are evaluated on the basis of the time and effort required to annotate the same video sequences.

Automatic classification of blank substrate defects

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Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

2014-10-01

Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma

International Nuclear Information System (INIS)

Liberman, Gilad; Louzoun, Yoram; Aizenstein, Orna; Blumenthal, Deborah T.; Bokstein, Felix; Palmon, Mika; Corn, Benjamin W.; Ben Bashat, Dafna

2013-01-01

Background: Current methods for evaluation of treatment response in glioblastoma are inaccurate, limited and time-consuming. This study aimed to develop a multi-modal MRI automatic classification method to improve accuracy and efficiency of treatment response assessment in patients with recurrent glioblastoma (GB). Materials and methods: A modification of the k-Nearest-Neighbors (kNN) classification method was developed and applied to 59 longitudinal MR data sets of 13 patients with recurrent GB undergoing bevacizumab (anti-angiogenic) therapy. Changes in the enhancing tumor volume were assessed using the proposed method and compared with Macdonald's criteria and with manual volumetric measurements. The edema-like area was further subclassified into peri- and non-peri-tumoral edema, using both the kNN method and an unsupervised method, to monitor longitudinal changes. Results: Automatic classification using the modified kNN method was applicable in all scans, even when the tumors were infiltrative with unclear borders. The enhancing tumor volume obtained using the automatic method was highly correlated with manual measurements (N = 33, r = 0.96, p < 0.0001), while standard radiographic assessment based on Macdonald's criteria matched manual delineation and automatic results in only 68% of cases. A graded pattern of tumor infiltration within the edema-like area was revealed by both automatic methods, showing high agreement. All classification results were confirmed by a senior neuro-radiologist and validated using MR spectroscopy. Conclusion: This study emphasizes the important role of automatic tools based on a multi-modal view of the tissue in monitoring therapy response in patients with high grade gliomas specifically under anti-angiogenic therapy

Delineation of contaminant plume for an inorganic contaminated site using electrical resistivity tomography: comparison with direct-push technique.

Science.gov (United States)

Liao, Qing; Deng, Yaping; Shi, Xiaoqing; Sun, Yuanyuan; Duan, Weidong; Wu, Jichun

2018-03-03

Precise delineation of contaminant plume distribution is essential for effective remediation of contaminated sites. Traditional in situ investigation methods like direct-push (DP) sampling are accurate, but are usually intrusive and costly. Electrical resistivity tomography (ERT) method, as a non-invasive geophysical technique to map spatiotemporal changes in resistivity of the subsurface, is becoming increasingly popular in environmental science. However, the resolution of ERT for delineation of contaminant plumes still remains controversial. In this study, ERT and DP technique were both conducted at a real inorganic contaminated site. The reliability of the ERT method was validated by the direct comparisons of their investigation results that the resistivity acquired by ERT method is in accordance with the total dissolved solid concentration in groundwater and the overall variation of the total iron content in soil obtained by DP technique. After testifying the applicability of ERT method for contaminant identification, the extension of contaminant plume at the study site was revealed by supplementary ERT surveys conducted subsequently in the surrounding area of the contaminant source zone.

Evaluation of an automatic MR-based gold fiducial marker localisation method for MR-only prostate radiotherapy

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Maspero, Matteo; van den Berg, Cornelis A. T.; Zijlstra, Frank; Sikkes, Gonda G.; de Boer, Hans C. J.; Meijer, Gert J.; Kerkmeijer, Linda G. W.; Viergever, Max A.; Lagendijk, Jan J. W.; Seevinck, Peter R.

2017-10-01

An MR-only radiotherapy planning (RTP) workflow would reduce the cost, radiation exposure and uncertainties introduced by CT-MRI registrations. In the case of prostate treatment, one of the remaining challenges currently holding back the implementation of an RTP workflow is the MR-based localisation of intraprostatic gold fiducial markers (FMs), which is crucial for accurate patient positioning. Currently, MR-based FM localisation is clinically performed manually. This is sub-optimal, as manual interaction increases the workload. Attempts to perform automatic FM detection often rely on being able to detect signal voids induced by the FMs in magnitude images. However, signal voids may not always be sufficiently specific, hampering accurate and robust automatic FM localisation. Here, we present an approach that aims at automatic MR-based FM localisation. This method is based on template matching using a library of simulated complex-valued templates, and exploiting the behaviour of the complex MR signal in the vicinity of the FM. Clinical evaluation was performed on seventeen prostate cancer patients undergoing external beam radiotherapy treatment. Automatic MR-based FM localisation was compared to manual MR-based and semi-automatic CT-based localisation (the current gold standard) in terms of detection rate and the spatial accuracy and precision of localisation. The proposed method correctly detected all three FMs in 15/17 patients. The spatial accuracy (mean) and precision (STD) were 0.9 mm and 0.5 mm respectively, which is below the voxel size of 1.1 × 1.1 × 1.2 mm3 and comparable to MR-based manual localisation. FM localisation failed (3/51 FMs) in the presence of bleeding or calcifications in the direct vicinity of the FM. The method was found to be spatially accurate and precise, which is essential for clinical use. To overcome any missed detection, we envision the use of the proposed method along with verification by an observer. This will result in a

Evaluation of an automatic MR-based gold fiducial marker localisation method for MR-only prostate radiotherapy.

Science.gov (United States)

Maspero, Matteo; van den Berg, Cornelis A T; Zijlstra, Frank; Sikkes, Gonda G; de Boer, Hans C J; Meijer, Gert J; Kerkmeijer, Linda G W; Viergever, Max A; Lagendijk, Jan J W; Seevinck, Peter R

2017-10-03

An MR-only radiotherapy planning (RTP) workflow would reduce the cost, radiation exposure and uncertainties introduced by CT-MRI registrations. In the case of prostate treatment, one of the remaining challenges currently holding back the implementation of an RTP workflow is the MR-based localisation of intraprostatic gold fiducial markers (FMs), which is crucial for accurate patient positioning. Currently, MR-based FM localisation is clinically performed manually. This is sub-optimal, as manual interaction increases the workload. Attempts to perform automatic FM detection often rely on being able to detect signal voids induced by the FMs in magnitude images. However, signal voids may not always be sufficiently specific, hampering accurate and robust automatic FM localisation. Here, we present an approach that aims at automatic MR-based FM localisation. This method is based on template matching using a library of simulated complex-valued templates, and exploiting the behaviour of the complex MR signal in the vicinity of the FM. Clinical evaluation was performed on seventeen prostate cancer patients undergoing external beam radiotherapy treatment. Automatic MR-based FM localisation was compared to manual MR-based and semi-automatic CT-based localisation (the current gold standard) in terms of detection rate and the spatial accuracy and precision of localisation. The proposed method correctly detected all three FMs in 15/17 patients. The spatial accuracy (mean) and precision (STD) were 0.9 mm and 0.5 mm respectively, which is below the voxel size of [Formula: see text] mm 3 and comparable to MR-based manual localisation. FM localisation failed (3/51 FMs) in the presence of bleeding or calcifications in the direct vicinity of the FM. The method was found to be spatially accurate and precise, which is essential for clinical use. To overcome any missed detection, we envision the use of the proposed method along with verification by an observer. This will result in a

[Development of automatic urine monitoring system].

Science.gov (United States)

Wei, Liang; Li, Yongqin; Chen, Bihua

2014-03-01

An automatic urine monitoring system is presented to replace manual operation. The system is composed of the flow sensor, MSP430f149 single chip microcomputer, human-computer interaction module, LCD module, clock module and memory module. The signal of urine volume is captured when the urine flows through the flow sensor and then displayed on the LCD after data processing. The experiment results suggest that the design of the monitor provides a high stability, accurate measurement and good real-time, and meets the demand of the clinical application.

Feasibility of geometric-intensity-based semi-automated delineation of the tentorium cerebelli from MRI scans.

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Penumetcha, Neeraja; Kabadi, Suraj; Jedynak, Bruno; Walcutt, Charles; Gado, Mokhtar H; Wang, Lei; Ratnanather, J Tilak

2011-04-01

This paper describes a feasibility study of a method for delineating the tentorium cerebelli in magnetic resonance imaging (MRI) brain scans. The tentorium cerebelli is a thin sheet of dura matter covering the cerebellum and separating it from the posterior part of the temporal lobe and the occipital lobe of the cerebral hemispheres. Cortical structures such as the parahippocampal gyrus can be indistinguishable from tentorium in magnetized prepared rapid gradient echo and T1-weighted MRI scans. Similar intensities in these neighboring regions make it difficult to perform accurate cortical analysis in neuroimaging studies of schizophrenia and Alzheimer's disease. A semi-automated, geometric, intensity-based procedure for delineating the tentorium from a whole-brain scan is described. Initial and final curves are traced within the tentorium. A cost function, based on intensity and Euclidean distance, is computed between the two curves using the Fast Marching method. The initial curve is then evolved to the final curve based on the gradient of the computed costs, generating a series of intermediate curves. These curves are then used to generate a triangulated surface of the tentorium. For 3 scans, surfaces were found to be within 2 voxels from hand segmentations. Copyright © 2009 by the American Society of Neuroimaging.

Automatic coding method of the ACR Code

International Nuclear Information System (INIS)

Park, Kwi Ae; Ihm, Jong Sool; Ahn, Woo Hyun; Baik, Seung Kook; Choi, Han Yong; Kim, Bong Gi

1993-01-01

The authors developed a computer program for automatic coding of ACR(American College of Radiology) code. The automatic coding of the ACR code is essential for computerization of the data in the department of radiology. This program was written in foxbase language and has been used for automatic coding of diagnosis in the Department of Radiology, Wallace Memorial Baptist since May 1992. The ACR dictionary files consisted of 11 files, one for the organ code and the others for the pathology code. The organ code was obtained by typing organ name or code number itself among the upper and lower level codes of the selected one that were simultaneous displayed on the screen. According to the first number of the selected organ code, the corresponding pathology code file was chosen automatically. By the similar fashion of organ code selection, the proper pathologic dode was obtained. An example of obtained ACR code is '131.3661'. This procedure was reproducible regardless of the number of fields of data. Because this program was written in 'User's Defined Function' from, decoding of the stored ACR code was achieved by this same program and incorporation of this program into program in to another data processing was possible. This program had merits of simple operation, accurate and detail coding, and easy adjustment for another program. Therefore, this program can be used for automation of routine work in the department of radiology

Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information.

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Tao, Qian; Milles, Julien; Zeppenfeld, Katja; Lamb, Hildo J; Bax, Jeroen J; Reiber, Johan H C; van der Geest, Rob J

2010-08-01

Accurate assessment of the size and distribution of a myocardial infarction (MI) from late gadolinium enhancement (LGE) MRI is of significant prognostic value for postinfarction patients. In this paper, an automatic MI identification method combining both intensity and spatial information is presented in a clear framework of (i) initialization, (ii) false acceptance removal, and (iii) false rejection removal. The method was validated on LGE MR images of 20 chronic postinfarction patients, using manually traced MI contours from two independent observers as reference. Good agreement was observed between automatic and manual MI identification. Validation results showed that the average Dice indices, which describe the percentage of overlap between two regions, were 0.83 +/- 0.07 and 0.79 +/- 0.08 between the automatic identification and the manual tracing from observer 1 and observer 2, and the errors in estimated infarct percentage were 0.0 +/- 1.9% and 3.8 +/- 4.7% compared with observer 1 and observer 2. The difference between the automatic method and manual tracing is in the order of interobserver variation. In conclusion, the developed automatic method is accurate and robust in MI delineation, providing an objective tool for quantitative assessment of MI in LGE MR imaging.

Automatic segmentation of mandible in panoramic x-ray

OpenAIRE

Abdi, Amir Hossein; Kasaei, Shohreh; Mehdizadeh, Mojdeh

2015-01-01

As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of t...

Aspen Delineation - Inyo National Forest [ds366

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California Natural Resource Agency — The database represents delineations of known aspen stands where aspen assessments were collected in the Inyo National Forest, Inyo County, California. The Inyo...

Automatic labeling of MR brain images through extensible learning and atlas forests.

Science.gov (United States)

Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

2017-12-01

Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic

Automatic Chessboard Detection for Intrinsic and Extrinsic Camera Parameter Calibration

Directory of Open Access Journals (Sweden)

Jose María Armingol

2010-03-01

Full Text Available There are increasing applications that require precise calibration of cameras to perform accurate measurements on objects located within images, and an automatic algorithm would reduce this time consuming calibration procedure. The method proposed in this article uses a pattern similar to that of a chess board, which is found automatically in each image, when no information regarding the number of rows or columns is supplied to aid its detection. This is carried out by means of a combined analysis of two Hough transforms, image corners and invariant properties of the perspective transformation. Comparative analysis with more commonly used algorithms demonstrate the viability of the algorithm proposed, as a valuable tool for camera calibration.

The Curve Number Concept as a Driver for Delineating Hydrological Response Units

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Eleni Savvidou

2018-02-01

Full Text Available In this paper, a new methodology for delineating Hydrological Response Units (HRUs, based on the Curve Number (CN concept, is presented. Initially, a semi-automatic procedure in a GIS environment is used to produce basin maps of distributed CN values as the product of the three classified layers, soil permeability, land use/land cover characteristics and drainage capacity. The map of CN values is used in the context of model parameterization, in order to identify the essential number and spatial extent of HRUs and, consequently, the number of control variables of the calibration problem. The new approach aims at reducing the subjectivity introduced by the definition of HRUs and providing parsimonious modelling schemes. In particular, the CN-based parameterization (1 allows the user to assign as many parameters as can be supported by the available hydrological information, (2 associates the model parameters with anticipated basin responses, as quantified in terms of CN classes across HRUs, and (3 reduces the effort for model calibration, simultaneously ensuring good predictive capacity. The advantages of the proposed approach are demonstrated in the hydrological simulation of the Nedontas River Basin, Greece, where parameterizations of different complexities are employed in a recently improved version of the HYDROGEIOS model. A modelling experiment with a varying number of HRUs, where the parameter estimation problem was handled through automatic optimization, showed that the parameterization with three HRUs, i.e., equal to the number of flow records, ensured the optimal performance. Similarly, tests with alternative HRU configurations confirmed that the optimal scores, both in calibration and validation, were achieved by the CN-based approach, also resulting in parameters values across the HRUs that were in agreement with their physical interpretation.

Aspen Delineation - Sierra State Parks [ds380

Data.gov (United States)

California Natural Resource Agency — The database represents delineations of aspen stands associated with stand assessment data (SIERRA_SP_PTS) collected in aspen stands on lands administered by the...

Aspen Delineation - Sequoia National Forest [ds378

Data.gov (United States)

California Natural Resource Agency — The database represents delineations of aspen stands associated with stand assessment data (SEQUOIA_NF_PTS) collected in aspen stands in the Cannell Meadows Ranger...

Automatic setting of the distance between sample and detector in gamma-ray spectroscopy

International Nuclear Information System (INIS)

Andeweg, A.H.

1980-01-01

An apparatus has been developed that automatically sets the distance from the sample to the detector according to the radioactivity of the sample. The distance-setting unit works in conjuction with an automatic sample changer, and is interconnected with other components so that the counting head automatically moves to the optimum distance for the analysis of a particular sample. The distance, which is indicated digitally in increments of 0,01 mm, can be set between 18 and 995 mm at count rates that can be preset between 1000 and 10 000 counts per second. On being tested, the instrument performed well within the desired range and accuracy. Under routine conditions, the spectra were much more accurate than before, especially when samples of different radioactivity were counted

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

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Ramamurthy Bhagavatula

2014-01-01

Full Text Available We propose a methodology for the design of features mimicking the visual cues used by pathologists when identifying tissues in hematoxylin and eosin (H&E-stained samples. Background: H&E staining is the gold standard in clinical histology; it is cheap and universally used, producing a vast number of histopathological samples. While pathologists accurately and consistently identify tissues and their pathologies, it is a time-consuming and expensive task, establishing the need for automated algorithms for improved throughput and robustness. Methods: We use an iterative feedback process to design a histopathology vocabulary (HV, a concise set of features that mimic the visual cues used by pathologists, e.g. "cytoplasm color" or "nucleus density." These features are based in histology and understood by both pathologists and engineers. We compare our HV to several generic texture-feature sets in a pixel-level classification algorithm. Results: Results on delineating and identifying tissues in teratoma tumor samples validate our expert knowledge-based approach. Conclusions: The HV can be an effective tool for identifying and delineating teratoma components from images of H&E-stained tissue samples.

Consensus Guidelines for Delineation of Clinical Target Volume for Intensity-Modulated Pelvic Radiotherapy for the Definitive Treatment of Cervix Cancer

International Nuclear Information System (INIS)

Lim, Karen; Small, William; Portelance, Lorraine; Creutzberg, Carien; Juergenliemk-Schulz, Ina M.; Mundt, Arno; Mell, Loren K.; Mayr, Nina; Viswanathan, Akila; Jhingran, Anuja; Erickson, Beth; De Los Santos, Jennifer; Gaffney, David; Yashar, Catheryn; Beriwal, Sushil; Wolfson, Aaron

2011-01-01

Purpose: Accurate target definition is vitally important for definitive treatment of cervix cancer with intensity-modulated radiotherapy (IMRT), yet a definition of clinical target volume (CTV) remains variable within the literature. The aim of this study was to develop a consensus CTV definition in preparation for a Phase 2 clinical trial being planned by the Radiation Therapy Oncology Group. Methods and Materials: A guidelines consensus working group meeting was convened in June 2008 for the purposes of developing target definition guidelines for IMRT for the intact cervix. A draft document of recommendations for CTV definition was created and used to aid in contouring a clinical case. The clinical case was then analyzed for consistency and clarity of target delineation using an expectation maximization algorithm for simultaneous truth and performance level estimation (STAPLE), with kappa statistics as a measure of agreement between participants. Results: Nineteen experts in gynecological radiation oncology generated contours on axial magnetic resonance images of the pelvis. Substantial STAPLE agreement sensitivity and specificity values were seen for gross tumor volume (GTV) delineation (0.84 and 0.96, respectively) with a kappa statistic of 0.68 (p < 0.0001). Agreement for delineation of cervix, uterus, vagina, and parametria was moderate. Conclusions: This report provides guidelines for CTV definition in the definitive cervix cancer setting for the purposes of IMRT, building on previously published guidelines for IMRT in the postoperative setting.

Towards automatic music transcription: note extraction based on independent subspace analysis

Science.gov (United States)

Wellhausen, Jens; Hoynck, Michael

2005-01-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

AISLE: an automatic volumetric segmentation method for the study of lung allometry.

Science.gov (United States)

Ren, Hongliang; Kazanzides, Peter

2011-01-01

We developed a fully automatic segmentation method for volumetric CT (computer tomography) datasets to support construction of a statistical atlas for the study of allometric laws of the lung. The proposed segmentation method, AISLE (Automated ITK-Snap based on Level-set), is based on the level-set implementation from an existing semi-automatic segmentation program, ITK-Snap. AISLE can segment the lung field without human interaction and provide intermediate graphical results as desired. The preliminary experimental results show that the proposed method can achieve accurate segmentation, in terms of volumetric overlap metric, by comparing with the ground-truth segmentation performed by a radiologist.

Aspen Delineation - Lassen National Forest [ds372

Data.gov (United States)

California Natural Resource Agency — The database represents delineations of aspen stands associated with stand assessment data (LASSEN_NF_EAGLELAKE_PTS) collected in aspen stands in the in the Eagle...

Automatic evidence retrieval for systematic reviews.

Science.gov (United States)

Choong, Miew Keen; Galgani, Filippo; Dunn, Adam G; Tsafnat, Guy

2014-10-01

Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing's effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Our goal was to evaluate an automatic method for citation snowballing's capacity to identify and retrieve the full text and/or abstracts of cited articles. Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews.

Automatic Volumetry of the Cerebrospinal Fluid Space in Idiopathic Normal Pressure Hydrocephalus

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Kazunari Ishii

2013-12-01

Full Text Available Objectives: To measure the cerebrospinal fluid (CSF space volume in idiopathic normal pressure hydrocephalus (INPH, we developed a software that allows us to automatically measure the regional CSF space and compared the volumes of the ventricle systems (VS, Sylvian fissures (SF and sulci at high convexity and midline (SHM among INPH patients, Alzheimer's disease (AD patients and healthy volunteers (HVs. Methods: Fifteen INPH patients, 15 AD patients and 15 HVs were retrospectively selected for this study. 3D-T1 MR images were obtained. We improved upon an automatic gray matter volume system to measure CSF spaces, adopting new regions for the template of INPH-characteristic CSF spaces and measured them. The VS, SF and SHM volumes were calculated relative to the intracranial volume. Results: The relative SHM volume of the INPH group (0.0237 ± 0.0064 was the smallest among the 3 groups (AD: 0.0477 ± 0.0109, HV: 0.0542 ± 0.0045. The VS (0.0499 ± 0.0135 and SF (0.0187 ± 0.0037 volumes of the INPH group were significantly larger than those of the AD (VS: 0.0311 ± 0.0075, SF: 0.0146 ± 0.0026 and HV groups (VS: 0.0167 ± 0.0065, SF: 0.0111 ± 0.017. Conclusion: Automatic volume measurement can be used to delineate the characteristic changes in CSF space in patients with INPH and is useful in the diagnosis of INPH.

Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

Science.gov (United States)

Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Automatic noninvasive measurement of systolic blood pressure using photoplethysmography

Directory of Open Access Journals (Sweden)

Glik Zehava

2009-10-01

Full Text Available Abstract Background Automatic measurement of arterial blood pressure is important, but the available commercial automatic blood pressure meters, mostly based on oscillometry, are of low accuracy. Methods In this study, we present a cuff-based technique for automatic measurement of systolic blood pressure, based on photoplethysmographic signals measured simultaneously in fingers of both hands. After inflating the pressure cuff to a level above systolic blood pressure in a relatively slow rate, it is slowly deflated. The cuff pressure for which the photoplethysmographic signal reappeared during the deflation of the pressure-cuff was taken as the systolic blood pressure. The algorithm for the detection of the photoplethysmographic signal involves: (1 determination of the time-segments in which the photoplethysmographic signal distal to the cuff is expected to appear, utilizing the photoplethysmographic signal in the free hand, and (2 discrimination between random fluctuations and photoplethysmographic pattern. The detected pulses in the time-segments were identified as photoplethysmographic pulses if they met two criteria, based on the pulse waveform and on the correlation between the signal in each segment and the signal in the two neighboring segments. Results Comparison of the photoplethysmographic-based automatic technique to sphygmomanometry, the reference standard, shows that the standard deviation of their differences was 3.7 mmHg. For subjects with systolic blood pressure above 130 mmHg the standard deviation was even lower, 2.9 mmHg. These values are much lower than the 8 mmHg value imposed by AAMI standard for automatic blood pressure meters. Conclusion The photoplethysmographic-based technique for automatic measurement of systolic blood pressure, and the algorithm which was presented in this study, seems to be accurate.

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

Science.gov (United States)

Osmanoglu, B.; Ozkan, C.; Sunar, F.

2013-10-01

After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

Feature-based automatic color calibration for networked camera system

Science.gov (United States)

Yamamoto, Shoji; Taki, Keisuke; Tsumura, Norimichi; Nakaguchi, Toshiya; Miyake, Yoichi

2011-01-01

In this paper, we have developed a feature-based automatic color calibration by using an area-based detection and adaptive nonlinear regression method. Simple color matching of chartless is achieved by using the characteristic of overlapping image area with each camera. Accurate detection of common object is achieved by the area-based detection that combines MSER with SIFT. Adaptive color calibration by using the color of detected object is calculated by nonlinear regression method. This method can indicate the contribution of object's color for color calibration, and automatic selection notification for user is performed by this function. Experimental result show that the accuracy of the calibration improves gradually. It is clear that this method can endure practical use of multi-camera color calibration if an enough sample is obtained.

Steam jet ejectors are examined automatically

International Nuclear Information System (INIS)

Lardiere, C.

2013-01-01

Steam jet ejectors are used in the nuclear industry particularly for the transfer of radioactive fluids. Their working is based on the Venturi effect and the conservation of energy. A steam ejector can be considered as a thermodynamical pump without mobile parts. The Descote enterprise manufactures a broad range of steam jet ejectors and the characterization and testing of the steam ejectors was made manually and empirically so far. A new test bench has been designed, the tests are led automatically and allow a more accurate characterization and optimization of the steam jet ejectors. (A.C.)

Using analytic element models to delineate drinking water source protection areas.

Science.gov (United States)

Raymond, Heather A; Bondoc, Michael; McGinnis, John; Metropulos, Kathy; Heider, Pat; Reed, Allison; Saines, Steve

2006-01-01

Since 1999, Ohio EPA hydrogeologists have used two analytic element models (AEMs), the proprietary software GFLOW and U.S. EPA's WhAEM, to delineate protection areas for 535 public water systems. Both models now use the GFLOW2001 solution engine, integrate well with Geographic Information System (GIS) technology, have a user-friendly graphical interface, are capable of simulating a variety of complex hydrogeologic settings, and do not rely upon a model grid. These features simplify the modeling process and enable AEMs to bridge the gap between existing simplistic delineation methods and more complex numerical models. Ohio EPA hydrogeologists demonstrated that WhAEM2000 and GFLOW2000 were capable of producing capture zones similar to more widely accepted models by applying the AEMs to eight sites that had been previously delineated using other methods. After the Ohio EPA delineated protection areas using AEMs, more simplistic delineation methods used by other states (volumetric equation and arbitrary fixed radii) were applied to the same water systems to compare the differences between various methods. GIS software and two-tailed paired t-tests were used to quantify the differences in protection areas and analyze the data. The results of this analysis demonstrate that AEMs typically produce significantly different protection areas than the most simplistic delineation methods, in terms of total area and shape. If the volumetric equation had been used instead of AEMs, Ohio would not have protected 265 km2 of critical upgradient area and would have overprotected 269 km2 of primarily downgradient land. Since an increasing number of land-use restrictions are being tied to drinking water protection areas, this analysis has broad policy implications.

Implementation Of Automatic Wiper Speed Control And Headlight Modes Control Systems Using Fuzzy Logic

Directory of Open Access Journals (Sweden)

ThetKoKo

2015-07-01

Full Text Available Abstract This research paper describes the design and simulation of the automatic wiper speed and headlight modes controllers using fuzzy logic. This proposed system consists of a fuzzy logic controller to control a cars wiper speed and headlight modes. The automatic wiper system detects the rain and its intensity. And according to the rain intensity the wiper speed is automatically controlled. Headlight modes automatically changes either from low beam mode to high beam mode or form high beam mode to low beam mode depending on the light intensity from the other vehicle coming from the opposite direction. The system comprises of PIC impedance sensor piezoelectric vibration sensor LDR headlamps and a DC motor to accurate the windshield wiper. Piezoelectric sensor is used to detect the rain intensity which is based on the piezoelectric effect. MATLAB software is used to achieve the designed goal.

FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION

Directory of Open Access Journals (Sweden)

R. J. L. Argamosa

2016-06-01

Full Text Available The generation of high resolution canopy height model (CHM from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope. The local maxima are obtained by taking the maximum raster value in a 3 m x 3 m cell. These values are assumed as tree tops and therefore considered as individual trees. Based on the assumptions, thiessen polygons were generated to serve as buffers for the canopy extent. The negative profile curvature is then measured from the slope of the CHM. The results show that the aggregated points from a negative profile curvature raster provide the most realistic crown shape. The absence of field data regarding tree crown dimensions require accurate visual assessment after the appended delineated tree crown polygon was superimposed to the hill shaded CHM.

Automatic Imitation

Science.gov (United States)

Heyes, Cecilia

2011-01-01

"Automatic imitation" is a type of stimulus-response compatibility effect in which the topographical features of task-irrelevant action stimuli facilitate similar, and interfere with dissimilar, responses. This article reviews behavioral, neurophysiological, and neuroimaging research on automatic imitation, asking in what sense it is "automatic"…

Atlas-based segmentation technique incorporating inter-observer delineation uncertainty for whole breast

International Nuclear Information System (INIS)

Bell, L R; Pogson, E M; Metcalfe, P; Holloway, L; Dowling, J A

2017-01-01

Accurate, efficient auto-segmentation methods are essential for the clinical efficacy of adaptive radiotherapy delivered with highly conformal techniques. Current atlas based auto-segmentation techniques are adequate in this respect, however fail to account for inter-observer variation. An atlas-based segmentation method that incorporates inter-observer variation is proposed. This method is validated for a whole breast radiotherapy cohort containing 28 CT datasets with CTVs delineated by eight observers. To optimise atlas accuracy, the cohort was divided into categories by mean body mass index and laterality, with atlas’ generated for each in a leave-one-out approach. Observer CTVs were merged and thresholded to generate an auto-segmentation model representing both inter-observer and inter-patient differences. For each category, the atlas was registered to the left-out dataset to enable propagation of the auto-segmentation from atlas space. Auto-segmentation time was recorded. The segmentation was compared to the gold-standard contour using the dice similarity coefficient (DSC) and mean absolute surface distance (MASD). Comparison with the smallest and largest CTV was also made. This atlas-based auto-segmentation method incorporating inter-observer variation was shown to be efficient (<4min) and accurate for whole breast radiotherapy, with good agreement (DSC>0.7, MASD <9.3mm) between the auto-segmented contours and CTV volumes. (paper)

Automatic Extraction and Size Distribution of Landslides in Kurdistan Region, NE Iraq

Directory of Open Access Journals (Sweden)

Arsalan A. Othman

2013-05-01

Full Text Available This study aims to assess the localization and size distribution of landslides using automatic remote sensing techniques in (semi- arid, non-vegetated, mountainous environments. The study area is located in the Kurdistan region (NE Iraq, within the Zagros orogenic belt, which is characterized by the High Folded Zone (HFZ, the Imbricated Zone and the Zagros Suture Zone (ZSZ. The available reference inventory includes 3,190 landslides mapped from sixty QuickBird scenes using manual delineation. The landslide types involve rock falls, translational slides and slumps, which occurred in different lithological units. Two hundred and ninety of these landslides lie within the ZSZ, representing a cumulated surface of 32 km2. The HFZ implicates 2,900 landslides with an overall coverage of about 26 km2. We first analyzed cumulative landslide number-size distributions using the inventory map. We then proposed a very simple and robust algorithm for automatic landslide extraction using specific band ratios selected upon the spectral signatures of bare surfaces as well as posteriori slope and the normalized difference vegetation index (NDVI thresholds. The index is based on the contrast between landslides and their background, whereas the landslides have high reflections in the green and red bands. We applied the slope threshold map to remove low slope areas, which have high reflectance in red and green bands. The algorithm was able to detect ~96% of the recent landslides known from the reference inventory on a test site. The cumulative landslide number-size distribution of automatically extracted landslide is very similar to the one based on visual mapping. The automatic extraction is therefore adapted for the quantitative analysis of landslides and thus can contribute to the assessment of hazards in similar regions.

Handheld optical coherence tomography-reflectance confocal microscopy probe for detection of basal cell carcinoma and delineation of margins

Science.gov (United States)

Iftimia, Nicusor; Yélamos, Oriol; Chen, Chih-Shan J.; Maguluri, Gopi; Cordova, Miguel A.; Sahu, Aditi; Park, Jesung; Fox, William; Alessi-Fox, Christi; Rajadhyaksha, Milind

2017-07-01

We present a hand-held implementation and preliminary evaluation of a combined optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) probe for detecting and delineating the margins of basal cell carcinomas (BCCs) in human skin in vivo. A standard OCT approach (spectrometer-based) with a central wavelength of 1310 nm and 0.11 numerical aperture (NA) was combined with a standard RCM approach (830-nm wavelength and 0.9 NA) into a common path hand-held probe. Cross-sectional OCT images and enface RCM images are simultaneously displayed, allowing for three-dimensional microscopic assessment of tumor morphology in real time. Depending on the subtype and depth of the BCC tumor and surrounding skin conditions, OCT and RCM imaging are able to complement each other, the strengths of each helping overcome the limitations of the other. Four representative cases are summarized, out of the 15 investigated in a preliminary pilot study, demonstrating how OCT and RCM imaging may be synergistically combined to more accurately detect BCCs and more completely delineate margins. Our preliminary results highlight the potential benefits of combining the two technologies within a single probe to potentially guide diagnosis as well as treatment of BCCs.

Delineating organs at risk in radiation therapy

CERN Document Server

Ausili Cèfaro, Giampiero; Perez, Carlos A

2014-01-01

This book offers an invaluable guide to the delineation of organs at risk of toxicity in patients undergoing radiotherapy. It details the radiological anatomy of organs at risk as seen on typical radiotherapy planning CT scans.

Variability Among Breast Radiation Oncologists in Delineation of the Postsurgical Lumpectomy Cavity

International Nuclear Information System (INIS)

Landis, Daniel M.; Luo Weixiu; Song Jun; Bellon, Jennifer R.; Punglia, Rinaa S.; Wong, Julia S.; Killoran, Joseph H.; Gelman, Rebecca; Harris, Jay R.

2007-01-01

Purpose: Partial breast irradiation (PBI) is becoming more widely used. Accurate determination of the surgical lumpectomy cavity volume is more critical with PBI than with whole breast radiation therapy. We examined the interobserver variability in delineation of the lumpectomy cavity among four academic radiation oncologists who specialize in the treatment of breast cancer. Methods and Materials: Thirty-four lumpectomy cavities in 33 consecutive patients were evaluated. Each physician contoured the cavity and a 1.5-cm margin was added to define the planning target volume (PTV). A cavity visualization score (CVS) was assigned (1-5). To eliminate bias, the physician of record was eliminated from the analysis in all cases. Three measures of variability of the PTV were developed: average shift of the center of mass (COM), average percent overlap between the PTV of two physicians (PVO), and standard deviation of the PTV. Results: Of variables examined, pathologic resection volume was significantly correlated with CVS, with larger volumes more easily visualized. Shift of the COM decreased and PVO increased significantly as CVS increased. For CVS 4 and 5 cases, the average COM shift was 3 mm and 2 mm, respectively, and PVO was 77% and 87%, respectively. In multiple linear regression, pathologic diameter >4 cm and CVS ≥3 were significantly associated with smaller COM shift. When CVS was omitted from analysis, PVO was significantly larger with pathologic diameter ≥5 cm, days to planning <36, and older age. Conclusions: Even among radiation oncologists who specialize in breast radiotherapy, there can be substantial differences in delineation of the postsurgical radiotherapy target volume. Large treatment margins may be prudent if the cavity is not clearly defined

Organs at risk in the brain and their dose-constraints in adults and in children: A radiation oncologist’s guide for delineation in everyday practice

International Nuclear Information System (INIS)

Scoccianti, Silvia; Detti, Beatrice; Gadda, Davide; Greto, Daniela; Furfaro, Ilaria; Meacci, Fiammetta; Simontacchi, Gabriele; Di Brina, Lucia; Bonomo, Pierluigi; Giacomelli, Irene; Meattini, Icro; Mangoni, Monica; Cappelli, Sabrina; Cassani, Sara; Talamonti, Cinzia; Bordi, Lorenzo; Livi, Lorenzo

2015-01-01

Purpose: Accurate organs at risk definition is essential for radiation treatment of brain tumors. The aim of this study is to provide a stepwise and simplified contouring guide to delineate the OARs in the brain as it would be done in the everyday practice of planning radiotherapy for brain cancer treatment. Methods: Anatomical descriptions and neuroimaging atlases of the brain were studied. The dosimetric constraints used in literature were reviewed. Results: A Computed Tomography and Magnetic Resonance Imaging based detailed atlas was developed jointly by radiation oncologists, a neuroradiologist and a neurosurgeon. For each organ brief anatomical notion, main radiological reference points and useful considerations are provided. Recommended dose-constraints both for adult and pediatric patients were also provided. Conclusions: This report provides guidelines for OARs delineation and their dose-constraints for the treatment planning of patients with brain tumors

SU-E-T-373: A Motorized Stage for Fast and Accurate QA of Machine Isocenter

International Nuclear Information System (INIS)

Moore, J; Velarde, E; Wong, J

2014-01-01

Purpose: Precision delivery of radiation dose relies on accurate knowledge of the machine isocenter under a variety of machine motions. This is typically determined by performing a Winston-Lutz test consisting of imaging a known object at multiple gantry/collimator/table angles and ensuring that the maximum offset is within specified tolerance. The first step in the Winston-Lutz test is careful placement of a ball bearing at the machine isocenter as determined by repeated imaging and shifting until accurate placement has been determined. Conventionally this is performed by adjusting a stage manually using vernier scales which carry the limitation that each adjustment must be done inside the treatment room with the risks of inaccurate adjustment of the scale and physical bumping of the table. It is proposed to use a motorized system controlled outside of the room to improve the required time and accuracy of these tests. Methods: The three dimensional vernier scales are replaced by three motors with accuracy of 1 micron and a range of 25.4mm connected via USB to a computer in the control room. Software is designed which automatically detects the motors and assigns them to proper axes and allows for small shifts to be entered and performed. Input values match calculated offsets in magnitude and sign to reduce conversion errors. Speed of setup, number of iterations to setup, and accuracy of final placement are assessed. Results: Automatic BB placement required 2.25 iterations and 13 minutes on average while manual placement required 3.76 iterations and 37.5 minutes. The average final XYZ offsets is 0.02cm, 0.01cm, 0.04cm for automatic setup and 0.04cm, 0.02cm, 0.04cm for manual setup. Conclusion: Automatic placement decreased time and repeat iterations for setup while improving placement accuracy. Automatic placement greatly reduces the time required to perform QA

Markov random field based automatic image alignment for electron tomography.

Science.gov (United States)

Amat, Fernando; Moussavi, Farshid; Comolli, Luis R; Elidan, Gal; Downing, Kenneth H; Horowitz, Mark

2008-03-01

We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy

NARCIS (Netherlands)

A. Ramkumar (Anjana); J. Dolz (Jose); H.A. Kirisli (Hortense); S. Adebahr (Sonja); T. Schimek-Jasch (Tanja); U. Nestle (Ursula); L. Massoptier (Laurent); E. Varga (Edit); P.J. Stappers (P.); W.J. Niessen (Wiro); Y. Song (Yu)

2016-01-01

textabstractAccurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently

Aspen Delineation - Klamath National Forest, EUI [ds368

Data.gov (United States)

California Natural Resource Agency — The database represents delineations of known aspen stands where aspen assessments were collected in the Klamath National Forest, Siskiyou County, California. The...

Target volume delineation variation in radiotherapy for early stage rectal cancer in the Netherlands

International Nuclear Information System (INIS)

Nijkamp, Jasper; Haas-Kock, Danielle F.M. de; Beukema, Jannet C.; Neelis, Karen J.; Woutersen, Dankert; Ceha, Heleen; Rozema, Tom; Slot, Annerie; Vos-Westerman, Hanneke; Intven, Martijn; Spruit, Patty H.; Linden, Yvette van der; Geijsen, Debby; Verschueren, Karijn; Herk, Marcel B. van; Marijnen, Corrie A.M.

2012-01-01

Purpose: The aim of this study was to measure and improve the quality of target volume delineation by means of national consensus on target volume definition in early-stage rectal cancer. Methods and materials: The CTV’s for eight patients were delineated by 11 radiation oncologists in 10 institutes according to local guidelines (phase 1). After observer variation analysis a workshop was organized to establish delineation guidelines and a digital atlas, with which the same observers re-delineated the dataset (phase 2). Variation in volume, most caudal and cranial slice and local surface distance variation were analyzed. Results: The average delineated CTV volume decreased from 620 to 460 cc (p < 0.001) in phase 2. Variation in the caudal CTV border was reduced significantly from 1.8 to 1.2 cm SD (p = 0.01), while it remained 0.7 cm SD for the cranial border. The local surface distance variation (cm SD) reduced from 1.02 to 0.74 for anterior, 0.63 to 0.54 for lateral, 0.33 to 0.25 for posterior and 1.22 to 0.46 for the sphincter region, respectively. Conclusions: The large variation in target volume delineation could significantly be reduced by use of consensus guidelines and a digital delineation atlas. Despite the significant reduction there is still a need for further improvement.

Assessing the Agreement Between Eo-Based Semi-Automated Landslide Maps with Fuzzy Manual Landslide Delineation

Science.gov (United States)

Albrecht, F.; Hölbling, D.; Friedl, B.

2017-09-01

Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

Aspen Delineation - Plumas National Forest, FRRD [ds376

Data.gov (United States)

California Natural Resource Agency — The database represents delineations of aspen stands associated with stand assessment data (PLUMAS_NF_FEATHERRIVER_PTS) collected in aspen stands in the Plumas...

Development of a setup to enable stable and accurate flow conditions for membrane biofouling studies

KAUST Repository

Bucs, Szilard

2015-07-10

Systematic laboratory studies on membrane biofouling require experimental conditions that are well defined and representative for practice. Hydrodynamics and flow rate variations affect biofilm formation, morphology, and detachment and impacts on membrane performance parameters such as feed channel pressure drop. There is a suite of available monitors to study biofouling, but systems to operate monitors have not been well designed to achieve an accurate, constant water flow required for a reliable determination of biomass accumulation and feed channel pressure drop increase. Studies were done with membrane fouling simulators operated in parallel with manual and automated flow control, with and without dosage of a biodegradable substrate to the feedwater to enhance biofouling rate. High flow rate variations were observed for the manual water flow system (up to ≈9%) compared to the automatic flow control system (<1%). The flow rate variation in the manual system was strongly increased by biofilm accumulation, while the automatic system maintained an accurate and constant water flow in the monitor. The flow rate influences the biofilm accumulation and the impact of accumulated biofilm on membrane performance. The effect of the same amount of accumulated biomass on the pressure drop increase was related to the linear flow velocity. Stable and accurate feedwater flow rates are essential for biofouling studies in well-defined conditions in membrane systems. © 2015 Balaban Desalination Publications. All rights reserved.

HOW MANY HIPPOS (HOMHIP: ALGORITHM FOR AUTOMATIC COUNTS OF ANIMALS WITH INFRA-RED THERMAL IMAGERY FROM UAV

Directory of Open Access Journals (Sweden)

S. Lhoest

2015-08-01

Full Text Available The common hippopotamus (Hippopotamus amphibius L. is part of the animal species endangered because of multiple human pressures. Monitoring of species for conservation is then essential, and the development of census protocols has to be chased. UAV technology is considering as one of the new perspectives for wildlife survey. Indeed, this technique has many advantages but its main drawback is the generation of a huge amount of data to handle. This study aims at developing an algorithm for automatic count of hippos, by exploiting thermal infrared aerial images acquired from UAV. This attempt is the first known for automatic detection of this species. Images taken at several flight heights can be used as inputs of the algorithm, ranging from 38 to 155 meters above ground level. A Graphical User Interface has been created in order to facilitate the use of the application. Three categories of animals have been defined following their position in water. The mean error of automatic counts compared with manual delineations is +2.3% and shows that the estimation is unbiased. Those results show great perspectives for the use of the algorithm in populations monitoring after some technical improvements and the elaboration of statistically robust inventories protocols.

Recommendations for the delineation of organs at risk in ENT radiotherapy

International Nuclear Information System (INIS)

Ali, D.; Halimi, P.; Berges, O.; Deberne, M.; Botti, M.; Giraud, P.; Servagi-Vernat, S.

2011-01-01

Based on a literature survey, the authors propose recommendations for the delineation of the pharyngeal constrictor muscles, inner ear, larynx, buccal cavity, and temporomandibular joint. These recommendations of delineation of organs at risk are related to the functional anatomy of the considered structures, and correspond to volumes used in published surveys on dose-volume toxicity. They are simple and reproducible. Short communication

Automatic fringe enhancement with novel bidimensional sinusoids-assisted empirical mode decomposition.

Science.gov (United States)

Wang, Chenxing; Kemao, Qian; Da, Feipeng

2017-10-02

Fringe-based optical measurement techniques require reliable fringe analysis methods, where empirical mode decomposition (EMD) is an outstanding one due to its ability of analyzing complex signals and the merit of being data-driven. However, two challenging issues hinder the application of EMD in practical measurement. One is the tricky mode mixing problem (MMP), making the decomposed intrinsic mode functions (IMFs) have equivocal physical meaning; the other is the automatic and accurate extraction of the sinusoidal fringe from the IMFs when unpredictable and unavoidable background and noise exist in real measurements. Accordingly, in this paper, a novel bidimensional sinusoids-assisted EMD (BSEMD) is proposed to decompose a fringe pattern into mono-component bidimensional IMFs (BIMFs), with the MMP solved; properties of the resulted BIMFs are then analyzed to recognize and enhance the useful fringe component. The decomposition and the fringe recognition are integrated and the latter provides a feedback to the former, helping to automatically stop the decomposition to make the algorithm simpler and more reliable. A series of experiments show that the proposed method is accurate, efficient and robust to various fringe patterns even with poor quality, rendering it a potential tool for practical use.

Experimental pavement delineation treatments

Science.gov (United States)

Bryden, J. E.; Lorini, R. A.

1981-06-01

Visibility and durability of materials used to delineate shoulders and medians adjacent to asphalt pavements were evaluated. Materials evaluated were polysulfide and coal tar epoxies, one and two component polyesters, portland cement, acrylic paints, modified-alkyd traffic paint, preformed plastic tape, and thermoplastic markings. Neat applications, sand mortars, and surface treatments were installed in several geometric patterns including cross hatches, solid median treatments, and various widths of edge lines. Thermoplastic pavement markings generally performed very well, providing good visibility under adverse viewing conditions for at least 4 years. Thermoplastic 4 in. wide edge lines appear to provide adequate visibility for most conditions.

Experimental Study for Automatic Colony Counting System Based Onimage Processing

Science.gov (United States)

Fang, Junlong; Li, Wenzhe; Wang, Guoxin

Colony counting in many colony experiments is detected by manual method at present, therefore it is difficult for man to execute the method quickly and accurately .A new automatic colony counting system was developed. Making use of image-processing technology, a study was made on the feasibility of distinguishing objectively white bacterial colonies from clear plates according to the RGB color theory. An optimal chromatic value was obtained based upon a lot of experiments on the distribution of the chromatic value. It has been proved that the method greatly improves the accuracy and efficiency of the colony counting and the counting result is not affected by using inoculation, shape or size of the colony. It is revealed that automatic detection of colony quantity using image-processing technology could be an effective way.

Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation

DEFF Research Database (Denmark)

Mangado Lopez, Nerea; Ceresa, Mario; Duchateau, Nicolas

2016-01-01

. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical μCT images. Then, by fitting the statistical model to a patient......'s CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns......Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging...

All-in-One Wafer-Level Solution for MMIC Automatic Testing

Directory of Open Access Journals (Sweden)

Xu Ding

2018-04-01

Full Text Available In this paper, we present an all-in-one wafer-level solution for MMIC (monolithic microwave integrated circuit automatic testing. The OSL (open short load two tier de-embedding, the calibration verification model, the accurate PAE (power added efficiency testing, and the optimized vector cold source NF (noise figure measurement techniques are integrated in this solution to improve the measurement accuracy. A dual-core topology formed by an IPC (industrial personal computer and a VNA (vector network analyzer, and an automatic test software based on a three-level driver architecture, are applied to enhance the test efficiency. The benefit from this solution is that all the data of a MMIC can be achieved in only one contact, which shows state-of-the-art accuracy and efficiency.

User Interaction in Semi-Automatic Segmentation of Organs at Risk : A Case Study in Radiotherapy

NARCIS (Netherlands)

Ramkumar, A.; Dolz, J.; Kirisli, H.A.; Adebahr, S.; Schimek-Jasch, T.; Nestle, U.; Massoptier, L.; Varga, E.; Stappers, P.J.; Niessen, W.J.; Song, Y.

2015-01-01

Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to

Accurate, fully-automated NMR spectral profiling for metabolomics.

Directory of Open Access Journals (Sweden)

Siamak Ravanbakhsh

Full Text Available Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid, BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF, defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error, in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of

Automatic system for 3D reconstruction of the chick eye based on digital photographs.

Science.gov (United States)

Wong, Alexander; Genest, Reno; Chandrashekar, Naveen; Choh, Vivian; Irving, Elizabeth L

2012-01-01

The geometry of anatomical specimens is very complex and accurate 3D reconstruction is important for morphological studies, finite element analysis (FEA) and rapid prototyping. Although magnetic resonance imaging, computed tomography and laser scanners can be used for reconstructing biological structures, the cost of the equipment is fairly high and specialised technicians are required to operate the equipment, making such approaches limiting in terms of accessibility. In this paper, a novel automatic system for 3D surface reconstruction of the chick eye from digital photographs of a serially sectioned specimen is presented as a potential cost-effective and practical alternative. The system is designed to allow for automatic detection of the external surface of the chick eye. Automatic alignment of the photographs is performed using a combination of coloured markers and an algorithm based on complex phase order likelihood that is robust to noise and illumination variations. Automatic segmentation of the external boundaries of the eye from the aligned photographs is performed using a novel level-set segmentation approach based on a complex phase order energy functional. The extracted boundaries are sampled to construct a 3D point cloud, and a combination of Delaunay triangulation and subdivision surfaces is employed to construct the final triangular mesh. Experimental results using digital photographs of the chick eye show that the proposed system is capable of producing accurate 3D reconstructions of the external surface of the eye. The 3D model geometry is similar to a real chick eye and could be used for morphological studies and FEA.

Automatization of hydrodynamic modelling in a Floreon+ system

Science.gov (United States)

Ronovsky, Ales; Kuchar, Stepan; Podhoranyi, Michal; Vojtek, David

2017-07-01

The paper describes fully automatized hydrodynamic modelling as a part of the Floreon+ system. The main purpose of hydrodynamic modelling in the disaster management is to provide an accurate overview of the hydrological situation in a given river catchment. Automatization of the process as a web service could provide us with immediate data based on extreme weather conditions, such as heavy rainfall, without the intervention of an expert. Such a service can be used by non scientific users such as fire-fighter operators or representatives of a military service organizing evacuation during floods or river dam breaks. The paper describes the whole process beginning with a definition of a schematization necessary for hydrodynamic model, gathering of necessary data and its processing for a simulation, the model itself and post processing of a result and visualization on a web service. The process is demonstrated on a real data collected during floods in our Moravian-Silesian region in 2010.

Emission computed tomography with technetium-99m pyrophosphate for delineating location and size of acute myocardial infarction in man

Energy Technology Data Exchange (ETDEWEB)

Tamaki, S; Kadota, K; Kambara, H; Suzuki, Y; Nohara, R; Murakami, T; Kawai, C; Tamaki, N; Torizuka, K [Kyoto Univ. (Japan). Faculty of Medicine

1984-07-01

Emission computed tomography with technetium-99m pyrophosphate was used to delineate the location and estimate the size of myocardial infarcts in 20 patients with documented acute myocardial infarction. Tomography was performed after planar imaging within 2-5 days after the onset of infarction. Infarct volume was measured from the tomographic images by computerised planimetry and compared with the cumulative release of creatine kinase MB isoenzyme. The planar images showed discrete myocardial uptake in 13 of the 20 patients and diffuse uptake throughout the cardiac region in the remaining seven. In contrast, the tomographic images clearly delineated myocardial uptake by avoiding confusion of myocardial activity with that of surrounding structures, particularly bones, in all patients. For the 10 patients whose infarct size was assessed by analysis of the creatine kinase MB curve there was a close correlation between infarct volume estimated by tomography and by cumulative creatine kinase MB release. Thus emission computed tomography can provide a three dimensional map of technetium-99m pyrophosphate distribution within the heart and is thus able accurately to localise and estimate the size of myocardial infarcts in man.

Emission computed tomography with technetium-99m pyrophosphate for delineating location and size of acute myocardial infarction in man

International Nuclear Information System (INIS)

Tamaki, S.; Kadota, K.; Kambara, H.; Suzuki, Y.; Nohara, R.; Murakami, T.; Kawai, C.; Tamaki, N.; Torizuka, K.

1984-01-01

Emission computed tomography with technetium-99m pyrophosphate was used to delineate the location and estimate the size of myocardial infarcts in 20 patients with documented acute myocardial infarction. Tomography was performed after planar imaging within 2-5 days after the onset of infarction. Infarct volume was measured from the tomographic images by computerised planimetry and compared with the cumulative release of creatine kinase MB isoenzyme. The planar images showed discrete myocardial uptake in 13 of the 20 patients and diffuse uptake throughout the cardiac region in the remaining seven. In contrast, the tomographic images clearly delineated myocardial uptake by avoiding confusion of myocardial activity with that of surrounding structures, particularly bones, in all patients. For the 10 patients whose infarct size was assessed by analysis of the creatine kinase MB curve there was a close correlation between infarct volume estimated by tomography and by cumulative creatine kinase MB release. Thus emission computed tomography can provide a three dimensional map of technetium-99m pyrophosphate distribution within the heart and is thus able accurately to localise and estimate the size of myocardial infarcts in man. (author)

Automatic 3d Building Model Generations with Airborne LiDAR Data

Science.gov (United States)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D

AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA

Directory of Open Access Journals (Sweden)

N. Yastikli

2017-11-01

Full Text Available LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified

DNA Probe Pooling for Rapid Delineation of Chromosomal Breakpoints

Energy Technology Data Exchange (ETDEWEB)

Lu, Chun-Mei; Kwan, Johnson; Baumgartner, Adolf; Weier, Jingly F.; Wang, Mei; Escudero, Tomas; Munne' , Santiago; Zitzelsberger, Horst F.; Weier, Heinz-Ulrich

2009-01-30

Structural chromosome aberrations are hallmarks of many human genetic diseases. The precise mapping of translocation breakpoints in tumors is important for identification of genes with altered levels of expression, prediction of tumor progression, therapy response, or length of disease-free survival as well as the preparation of probes for detection of tumor cells in peripheral blood. Similarly, in vitro fertilization (IVF) and preimplantation genetic diagnosis (PGD) for carriers of balanced, reciprocal translocations benefit from accurate breakpoint maps in the preparation of patient-specific DNA probes followed by a selection of normal or balanced oocytes or embryos. We expedited the process of breakpoint mapping and preparation of case-specific probes by utilizing physically mapped bacterial artificial chromosome (BAC) clones. Historically, breakpoint mapping is based on the definition of the smallest interval between proximal and distal probes. Thus, many of the DNA probes prepared for multi-clone and multi-color mapping experiments do not generate additional information. Our pooling protocol described here with examples from thyroid cancer research and PGD accelerates the delineation of translocation breakpoints without sacrificing resolution. The turnaround time from clone selection to mapping results using tumor or IVF patient samples can be as short as three to four days.

Automatic generation of statistical pose and shape models for articulated joints.

Science.gov (United States)

Xin Chen; Graham, Jim; Hutchinson, Charles; Muir, Lindsay

2014-02-01

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

ASSESSING THE AGREEMENT BETWEEN EO-BASED SEMI-AUTOMATED LANDSLIDE MAPS WITH FUZZY MANUAL LANDSLIDE DELINEATION

Directory of Open Access Journals (Sweden)

F. Albrecht

2017-09-01

Full Text Available Landslide mapping benefits from the ever increasing availability of Earth Observation (EO data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

Target volume delineation in external beam partial breast irradiation: less inter-observer variation with preoperative- compared to postoperative delineation

NARCIS (Netherlands)

Leij, F. van der; Elkhuizen, P.H.M.; Janssen, T.M.; Poortmans, P.M.P.; Sangen, M. van der; Scholten, A.N.; Vliet-Vroegindeweij, C. van; Boersma, L.J.

2014-01-01

The challenge of adequate target volume definition in external beam partial breast irradiation (PBI) could be overcome with preoperative irradiation, due to less inter-observer variation. We compared the target volume delineation for external beam PBI on preoperative versus postoperative CT scans of

A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

Science.gov (United States)

Deng, Zhipeng; Lei, Lin; Zhou, Shilin

2015-10-01

Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

Accurate halo-galaxy mocks from automatic bias estimation and particle mesh gravity solvers

Science.gov (United States)

Vakili, Mohammadjavad; Kitaura, Francisco-Shu; Feng, Yu; Yepes, Gustavo; Zhao, Cheng; Chuang, Chia-Hsun; Hahn, ChangHoon

2017-12-01

Reliable extraction of cosmological information from clustering measurements of galaxy surveys requires estimation of the error covariance matrices of observables. The accuracy of covariance matrices is limited by our ability to generate sufficiently large number of independent mock catalogues that can describe the physics of galaxy clustering across a wide range of scales. Furthermore, galaxy mock catalogues are required to study systematics in galaxy surveys and to test analysis tools. In this investigation, we present a fast and accurate approach for generation of mock catalogues for the upcoming galaxy surveys. Our method relies on low-resolution approximate gravity solvers to simulate the large-scale dark matter field, which we then populate with haloes according to a flexible non-linear and stochastic bias model. In particular, we extend the PATCHY code with an efficient particle mesh algorithm to simulate the dark matter field (the FASTPM code), and with a robust MCMC method relying on the EMCEE code for constraining the parameters of the bias model. Using the haloes in the BigMultiDark high-resolution N-body simulation as a reference catalogue, we demonstrate that our technique can model the bivariate probability distribution function (counts-in-cells), power spectrum and bispectrum of haloes in the reference catalogue. Specifically, we show that the new ingredients permit us to reach percentage accuracy in the power spectrum up to k ∼ 0.4 h Mpc-1 (within 5 per cent up to k ∼ 0.6 h Mpc-1) with accurate bispectra improving previous results based on Lagrangian perturbation theory.

Pancreas and cyst segmentation

Science.gov (United States)

Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

2016-03-01

Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

Towards Automatic Music Transcription: Extraction of MIDI-Data out of Polyphonic Piano Music

Directory of Open Access Journals (Sweden)

Jens Wellhausen

2005-06-01

Full Text Available Driven by the increasing amount of music available electronically the need of automatic search and retrieval systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications and music analysis. The first part of the algorithm performs a note accurate temporal audio segmentation. The resulting segments are examined to extract the notes played in the second part. An algorithm for chord separation based on Independent Subspace Analysis is presented. Finally, the results are used to build a MIDI file.

MRI to delineate the gross tumor volume of nasopharyngeal cancers: which sequences and planes should be used?

Science.gov (United States)

Popovtzer, Aron; Ibrahim, Mohannad; Tatro, Daniel; Feng, Felix Y; Ten Haken, Randall K; Eisbruch, Avraham

2014-09-01

Magnetic resonance imaging (MRI) has been found to be better than computed tomography for defining the extent of primary gross tumor volume (GTV) in advanced nasopharyngeal cancer. It is routinely applied for target delineation in planning radiotherapy. However, the specific MRI sequences/planes that should be used are unknown. Twelve patients with nasopharyngeal cancer underwent primary GTV evaluation with gadolinium-enhanced axial T1 weighted image (T1) and T2 weighted image (T2), coronal T1, and sagittal T1 sequences. Each sequence was registered with the planning computed tomography scans. Planning target volumes (PTVs) were derived by uniform expansions of the GTVs. The volumes encompassed by the various sequences/planes, and the volumes common to all sequences/planes, were compared quantitatively and anatomically to the volume delineated by the commonly used axial T1-based dataset. Addition of the axial T2 sequence increased the axial T1-based GTV by 12% on average (p = 0.004), and composite evaluations that included the coronal T1 and sagittal T1 planes increased the axial T1-based GTVs by 30% on average (p = 0.003). The axial T1-based PTVs were increased by 20% by the additional sequences (p = 0.04). Each sequence/plane added unique volume extensions. The GTVs common to all the T1 planes accounted for 38% of the total volumes of all the T1 planes. Anatomically, addition of the coronal and sagittal-based GTVs extended the axial T1-based GTV caudally and cranially, notably to the base of the skull. Adding MRI planes and sequences to the traditional axial T1 sequence yields significant quantitative and anatomically important extensions of the GTVs and PTVs. For accurate target delineation in nasopharyngeal cancer, we recommend that GTVs be outlined in all MRI sequences/planes and registered with the planning computed tomography scans.

SU-F-I-51: CT/MR Image Deformation: The Clinical Assessment QA in Target Delineation

Energy Technology Data Exchange (ETDEWEB)

Yang, C; Chen, Y [Monmouth Medical Center, Long Branch, NJ (United States)

2016-06-15

Purpose: To study the deformation effects in CT/MR image registration of head and neck (HN) cancers. We present a clinical indication in guiding and simplifying registration procedures of this process while CT images possessed artifacts. Methods: CT/MR image fusion provides better soft tissue contrast in intracranial GTV definition with artifacts. However, whether the fusion process should include the deformation process is questionable and not recommended. We performed CT/MR image registration of a HN patient with tonsil GTV and nodes delineation on Varian Velocity™ system. Both rigid transformation and deformable registration of the same CT/MR imaging data were processed separately. Physician’s selection of target delineation was implemented to identify the variations. Transformation matrix was shown with visual identification, as well as the deformation QA numbers and figures were assessed. Results: The deformable CT/MR images were traced with the calculated matrix, both translation and rotational parameters were summarized. In deformable quality QA, the calculated Jacobian matrix was analyzed, which the min/mean/max of 0.73/0/99/1.37, respectively. Jacobian matrix of right neck node was 0.84/1.13/1.41, which present dis-similarity of the nodal area. If Jacobian = 1, the deformation is at the optimum situation. In this case, the deformation results have shown better target delineation for CT/MR deformation than rigid transformation. Though the root-mean-square vector difference is 1.48 mm, with similar rotational components, the cord and vertebrae position were aligned much better in the deformable MR images than the rigid transformation. Conclusion: CT/MR with/without image deformation presents similar image registration matrix; there were significant differentiate the anatomical structures in the region of interest by deformable process. Though vendor suggested only rigid transformation between CT/MR assuming the geometry remain similar, our findings

Tumor delineation: The weakest link in the search for accuracy in radiotherapy

Directory of Open Access Journals (Sweden)

Njeh C

2008-01-01

Full Text Available Radiotherapy is one of the most effective modalities for the treatment of cancer. However, there is a high degree of uncertainty associated with the target volume of most cancer sites. The sources of these uncertainties include, but are not limited to, the motion of the target, patient setup errors, patient movements, and the delineation of the target volume. Recently, many imaging techniques have been introduced to track the motion of tumors. The treatment delivery using these techniques is collectively called image-guided radiation therapy (IGRT. Ultimately, IGRT is only as good as the accuracy with which the target is known. There are reports of interobserver variability in tumor delineation across anatomical sites, but the widest ranges of variations have been reported for the delineation of head and neck tumors as well as esophageal and lung carcinomas. Significant interobserver variability in target delineation can be attributed to many factors including the impact of imaging and the influence of the observer (specialty, training, and personal bias. The visibility of the target can be greatly improved with the use of multimodality imaging by co-registration of CT with a second modality such as magnetic resonance imaging (MRI and/or positron emission tomography. Also, continuous education, training, and cross-collaboration of the radiation oncologist with other specialties can reduce the degree of variability in tumor delineation.

Integrating respiratory-gated PET-based target volume delineation in liver SBRT planning, a pilot study

International Nuclear Information System (INIS)

Riou, Olivier; Thariat, Juliette; Serrano, Benjamin; Azria, David; Paulmier, Benoit; Villeneuve, Remy; Fenoglietto, Pascal; Artenie, Antonella; Ortholan, Cécile; Faraggi, Marc

2014-01-01

To assess the feasibility and benefit of integrating four-dimensional (4D) Positron Emission Tomography (PET) – computed tomography (CT) for liver stereotactic body radiation therapy (SBRT) planning. 8 patients with 14 metastases were accrued in the study. They all underwent a non-gated PET and a 4D PET centered on the liver. The same CT scan was used for attenuation correction, registration, and considered the planning CT for SBRT planning. Six PET phases were reconstructed for each 4D PET. By applying an individualized threshold to the 4D PET, a Biological Internal Target Volume (BITV) was generated for each lesion. A gated Planning Target Volume (PTVg) was created by adding 3 mm to account for set-up margins. This volume was compared to a manual Planning Target Volume (PTV) delineated with the help of a semi-automatic Biological Target Volume (BTV) obtained from the non-gated exam. A 5 mm radial and a 10 mm craniocaudal margins were applied to account for tumor motion and set-up margins to create the PTV. One undiagnosed liver metastasis was discovered thanks to the 4D PET. The semi-automatic BTV were significantly smaller than the BITV (p = 0.0031). However, after applying adapted margins, 4D PET allowed a statistically significant decrease in the PTVg as compared to the PTV (p = 0.0052). In comparison to non-gated PET, 4D PET may better define the respiratory movements of liver targets and improve SBRT planning for liver metastases. Furthermore, non respiratory-gated PET exams can both misdiagnose liver metastases and underestimate the real internal target volumes

Evaluating automatic attentional capture by self-relevant information.

Science.gov (United States)

Ocampo, Brenda; Kahan, Todd A

2016-01-01

Our everyday decisions and memories are inadvertently influenced by self-relevant information. For example, we are faster and more accurate at making perceptual judgments about stimuli associated with ourselves, such as our own face or name, as compared with familiar non-self-relevant stimuli. Humphreys and Sui propose a "self-attention network" to account for these effects, wherein self-relevant stimuli automatically capture our attention and subsequently enhance the perceptual processing of self-relevant information. We propose that the masked priming paradigm and continuous flash suppression represent two ways to experimentally examine these controversial claims.

Nicolaides-Baraitser Syndrome: Delineation of the Phenotype

NARCIS (Netherlands)

Sousa, Sérgio B.; Abdul-Rahman, Omar A.; Bottani, Armand; Cormier-Daire, Valérie; Fryer, Alan; Gillessen-Kaesbach, Gabriele; Horn, Denise; Josifova, Dragana; Kuechler, Alma; Lees, Melissa; Macdermot, Kay; Magee, Alex; Morice-Picard, Fanny; Rosser, Elizabeth; Sarkar, Ajoy; Shannon, Nora; Stolte-Dijkstra, Irene; Verloes, Alain; Wakeling, Emma; Wilson, Louise; Hennekam, Raoul C. M.

2009-01-01

Nicolaides-Baraitser syndrome (NBS) is an infrequently described condition, thus far reported in five cases. In order to delineate the phenotype and its natural history in more detail, we gathered data on 18 hitherto unreported patients through a multi-center collaborative study, and follow-up data

Nicolaides-Baraitser Syndrome : Delineation of the Phenotype

NARCIS (Netherlands)

Sousa, Sergio B.; Abdul-Rahman, Omar A.; Bottani, Armand; Cormier-Daire, Valerie; Fryer, Alan; Gillessen-Kaesbach, Gabriele; Horn, Denise; Josifova, Dragana; Kuechler, Alma; Lees, Melissa; MacDermot, Kay; Magee, Alex; Morice-Picard, Fanny; Rosser, Elizabeth; Sarkar, Ajoy; Shannon, Nora; Stolte-Dijkstra, Irene; Verloes, Alain; Wakeling, Emma; Wilson, Louise; Hennekam, Raoul C. M.

Nicolaides-Baraitser syndrome (NBS) is an infrequently described condition, thus far reported in five cases. In order to delineate the phenotype and its natural history in more detail, we gathered data on 18 hitherto unreported patients through a multi-center collaborative study, and follow-up data

Automatic Extraction of Urban Built-Up Area Based on Object-Oriented Method and Remote Sensing Data

Science.gov (United States)

Li, L.; Zhou, H.; Wen, Q.; Chen, T.; Guan, F.; Ren, B.; Yu, H.; Wang, Z.

2018-04-01

Built-up area marks the use of city construction land in the different periods of the development, the accurate extraction is the key to the studies of the changes of urban expansion. This paper studies the technology of automatic extraction of urban built-up area based on object-oriented method and remote sensing data, and realizes the automatic extraction of the main built-up area of the city, which saves the manpower cost greatly. First, the extraction of construction land based on object-oriented method, the main technical steps include: (1) Multi-resolution segmentation; (2) Feature Construction and Selection; (3) Information Extraction of Construction Land Based on Rule Set, The characteristic parameters used in the rule set mainly include the mean of the red band (Mean R), Normalized Difference Vegetation Index (NDVI), Ratio of residential index (RRI), Blue band mean (Mean B), Through the combination of the above characteristic parameters, the construction site information can be extracted. Based on the degree of adaptability, distance and area of the object domain, the urban built-up area can be quickly and accurately defined from the construction land information without depending on other data and expert knowledge to achieve the automatic extraction of the urban built-up area. In this paper, Beijing city as an experimental area for the technical methods of the experiment, the results show that: the city built-up area to achieve automatic extraction, boundary accuracy of 2359.65 m to meet the requirements. The automatic extraction of urban built-up area has strong practicality and can be applied to the monitoring of the change of the main built-up area of city.

Automated delineation and characterization of drumlins using a localized contour tree approach

Science.gov (United States)

Wang, Shujie; Wu, Qiusheng; Ward, Dylan

2017-10-01

Drumlins are ubiquitous landforms in previously glaciated regions, formed through a series of complex subglacial processes operating underneath the paleo-ice sheets. Accurate delineation and characterization of drumlins are essential for understanding the formation mechanism of drumlins as well as the flow behaviors and basal conditions of paleo-ice sheets. Automated mapping of drumlins is particularly important for examining the distribution patterns of drumlins across large spatial scales. This paper presents an automated vector-based approach to mapping drumlins from high-resolution light detection and ranging (LiDAR) data. The rationale is to extract a set of concentric contours by building localized contour trees and establishing topological relationships. This automated method can overcome the shortcomings of previously manual and automated methods for mapping drumlins, for instance, the azimuthal biases during the generation of shaded relief images. A case study was carried out over a portion of the New York Drumlin Field. Overall 1181 drumlins were identified from the LiDAR-derived DEM across the study region, which had been underestimated in previous literature. The delineation results were visually and statistically compared to the manual digitization results. The morphology of drumlins was characterized by quantifying the length, width, elongation ratio, height, area, and volume. Statistical and spatial analyses were conducted to examine the distribution pattern and spatial variability of drumlin size and form. The drumlins and the morphologic characteristics exhibit significant spatial clustering rather than randomly distributed patterns. The form of drumlins varies from ovoid to spindle shapes towards the downstream direction of paleo ice flows, along with the decrease in width, area, and volume. This observation is in line with previous studies, which may be explained by the variations in sediment thickness and/or the velocity increases of ice flows

A multi-standard active-RC filter with accurate tuning system

International Nuclear Information System (INIS)

Ma Heping; Yuan Fang; Shi Yin; Dai, F F

2009-01-01

A low-power, highly linear, multi-standard, active-RC filter with an accurate and novel tuning architecture is presented. It exhibits IEEE 802.11 a/b/g (9.5 MHz) and DVB-H (3 MHz, 4 MHz) application. The filter exploits digitally-controlled polysilicon resistor banks and a phase lock loop type automatic tuning system. The novel and complex automatic frequency calibration scheme provides better than 4 corner frequency accuracy, and it can be powered down after calibration to save power and avoid digital signal interference. The filter achieves OIP3 of 26 dBm and the measured group delay variation of the receiver filter is 50 ns (WLAN mode). Its dissipation is 3.4 mA in RX mode and 2.3 mA (only for one path) in TX mode from a 2.85 V supply. The dissipation of calibration consumes 2 mA. The circuit has been fabricated in a 0.35 μm 47 GHz SiGe BiCMOS technology; the receiver and transmitter filter occupy 0.21 mm 2 and 0.11 mm 2 (calibration circuit excluded), respectively.

Delineating Concept Meanings: The Case of Terrorism.

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Kleg, Milton; Mahlios, Marc

1990-01-01

Presents a teacher-initiated model for reaching class consensus on the meaning of confusing or interchangeable concepts in social studies classrooms. Illustrates the model by delineating terrorism. Shows procedural steps that involve students in self and small group interviews where definitions are clarified until consensus is reached. Suggests…

Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.

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Veeraraghavan, Harini; Dashevsky, Brittany Z; Onishi, Natsuko; Sadinski, Meredith; Morris, Elizabeth; Deasy, Joseph O; Sutton, Elizabeth J

2018-03-19

We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). GCGMM's segmentations and the texture features computed from those segmentations were the most reproducible compared with manual delineations and other analyzed segmentation methods. Finally, random forest (RF) classifier trained with leave-one-out cross-validation using features extracted from GCGMM segmentation resulted in the best accuracy for ER-HER2+ vs. ERPR+/TN (GCGMM 0.95, expert 0.95, GC 0.90, FCM 0.92) and for ERPR + HER2- vs. TN (GCGMM 0.92, expert 0.91, GC 0.77, FCM 0.83).

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.

Science.gov (United States)

Olesen, Alexander Neergaard; Christensen, Julie A E; Sorensen, Helge B D; Jennum, Poul J

2016-08-01

Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen's kappa of 0.74 indicating substantial agreement between automatic and manual scoring.

Automatic measurement system for congenital hip dislocation using a computed radiography

International Nuclear Information System (INIS)

Komori, M.; Minato, K.; Hirakawa, A.; Kuwahara, M.

1988-01-01

Acetabular angle which is a diagnostic parameter of congenital hip dislocation has been measured manually in conventional X-ray film system. Using digital image directly provided from a computed radiography, an automatic measurement system was developed for this parameter. The process of the measurement was completed within a reasonable time, and accurate enough. The system was combined with an image database, so that it would be a measurement tool of PACS

Target volume delineation in external beam partial breast irradiation: Less inter-observer variation with preoperative- compared to postoperative delineation

International Nuclear Information System (INIS)

Leij, Femke van der; Elkhuizen, Paula H.M.; Janssen, Tomas M.; Poortmans, Philip; Sangen, Maurice van der; Scholten, Astrid N.; Vliet-Vroegindeweij, Corine van; Boersma, Liesbeth J.

2014-01-01

The challenge of adequate target volume definition in external beam partial breast irradiation (PBI) could be overcome with preoperative irradiation, due to less inter-observer variation. We compared the target volume delineation for external beam PBI on preoperative versus postoperative CT scans of twenty-four breast cancer patients

Automatic physical inference with information maximizing neural networks

Science.gov (United States)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

THE ACCURACY OF AUTOMATIC PHOTOGRAMMETRIC TECHNIQUES ON ULTRA-LIGHT UAV IMAGERY

Directory of Open Access Journals (Sweden)

O. Küng

2012-09-01

Full Text Available This paper presents an affordable, fully automated and accurate mapping solutions based on ultra-light UAV imagery. Several datasets are analysed and their accuracy is estimated. We show that the accuracy highly depends on the ground resolution (flying height of the input imagery. When chosen appropriately this mapping solution can compete with traditional mapping solutions that capture fewer high-resolution images from airplanes and that rely on highly accurate orientation and positioning sensors on board. Due to the careful integration with recent computer vision techniques, the post processing is robust and fully automatic and can deal with inaccurate position and orientation information which are typically problematic with traditional techniques.

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

Energy Technology Data Exchange (ETDEWEB)

Xiangfei, Chai; Hulshof, Maarten; Bel, Arjan [Department of Radiotherapy, Academic medical Center, University of Amsterdam, 1105 AZ, Amsterdam (Netherlands); Van Herk, Marcel; Betgen, Anja [Department of Radiotherapy, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, 1066 CX, Amsterdam (Netherlands)

2012-06-21

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

International Nuclear Information System (INIS)

Chai Xiangfei; Hulshof, Maarten; Bel, Arjan; Van Herk, Marcel; Betgen, Anja

2012-01-01

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation

Automatic characterization of loose parts impact damage risk parameters

International Nuclear Information System (INIS)

Glass, S.W.; Phillips, J.M.

1985-01-01

Loose parts caught in the high-velocity flows of the reactor coolant fluid strike against nuclear steam supply system (NSSS) components and can cause significant damage. Loose parts monitor systems (LPMS) have been available for years to detect metal-to-metal impacts. Once detected, however, an assessment of the damage risk potential for leaving the part in the system versus shutting it down and removing the part must be made. The principal parameters used in the damage risk assessment are time delays between the first and subsequent sensor indications (used to assess the impact location) and a correlation between the waveform and the impact energy of the part (how hard the part impacted). These parameters are not well suited to simple automatic techniques. The task has historically been performed by loose parts diagnostic experts who base much of their evaluation on experience and subjective interpretation of impact data waveforms. Three of the principal goals in developing the Babcock and Wilcox (B and W) LPMS-III were (a) to develop an accurate automatic assessment for the time delays, (b) to develop an automatic estimate of the impact energy, and (c) to present the data in a meaningful manner to the operator

Identification of mycobacterium tuberculosis in sputum smear slide using automatic scanning microscope

Science.gov (United States)

Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

2015-04-01

Sputum smear observation has an important role in tuberculosis (TB) disease diagnosis, because it needs accurate identification to avoid high errors diagnosis. In development countries, sputum smear slide observation is commonly done with conventional light microscope from Ziehl-Neelsen stained tissue and it doesn't need high cost to maintain the microscope. The clinicians do manual screening process for sputum smear slide which is time consuming and needs highly training to detect the presence of TB bacilli (mycobacterium tuberculosis) accurately, especially for negative slide and slide with less number of TB bacilli. For helping the clinicians, we propose automatic scanning microscope with automatic identification of TB bacilli. The designed system modified the field movement of light microscope with stepper motor which was controlled by microcontroller. Every sputum smear field was captured by camera. After that some image processing techniques were done for the sputum smear images. The color threshold was used for background subtraction with hue canal in HSV color space. Sobel edge detection algorithm was used for TB bacilli image segmentation. We used feature extraction based on shape for bacilli analyzing and then neural network classified TB bacilli or not. The results indicated identification of TB bacilli that we have done worked well and detected TB bacilli accurately in sputum smear slide with normal staining, but not worked well in over staining and less staining tissue slide. However, overall the designed system can help the clinicians in sputum smear observation becomes more easily.

Value of 18F-FDG PET-CT in nasopharyngeal carcinoma target delineation and radiotherapy boost

International Nuclear Information System (INIS)

Wang Ying; Feng Yanlin

2011-01-01

18 F-FDG PET-CT has widely used in nasopharyngeal carcinoma diagnosis and staging in recent years, it's effecten target volume delineation has received great attention. The article lays stress on the clinical research progress of 18 F-FDG PET-CT in the radiotherapy of nasopharyngeal carcinoma improve the accuracy of target delineation, reduce the difference of target delineation, guide the dose painting and boost. (authors)

A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease.

Science.gov (United States)

Nestor, Sean M; Gibson, Erin; Gao, Fu-Qiang; Kiss, Alex; Black, Sandra E

2013-02-01

Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer's disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multi-template fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity - particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of (Haller et al., 1997; Killiany et al., 1993; Malykhin et al., 2007; Pantel et al., 2000; Pruessner et al., 2000), and a single operator performed all manual tracings to generate de facto "ground truth" labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more posterior

SU-E-J-129: Atlas Development for Cardiac Automatic Contouring Using Multi-Atlas Segmentation

International Nuclear Information System (INIS)

Zhou, R; Yang, J; Pan, T; Milgrom, S; Pinnix, C; Shi, A; Yang, J; Liu, Y; Nguyen, Q; Gomez, D; Dabaja, B; Balter, P; Court, L; Liao, Z

2015-01-01

Purpose: To develop a set of atlases for automatic contouring of cardiac structures to determine heart radiation dose and the associated toxicity. Methods: Six thoracic cancer patients with both contrast and non-contrast CT images were acquired for this study. Eight radiation oncologists manually and independently delineated cardiac contours on the non-contrast CT by referring to the fused contrast CT and following the RTOG 1106 atlas contouring guideline. Fifteen regions of interest (ROIs) were delineated, including heart, four chambers, four coronary arteries, pulmonary artery and vein, inferior and superior vena cava, and ascending and descending aorta. Individual expert contours were fused using the simultaneous truth and performance level estimation (STAPLE) algorithm for each ROI and each patient. The fused contours became atlases for an in-house multi-atlas segmentation. Using leave-one-out test, we generated auto-segmented contours for each ROI and each patient. The auto-segmented contours were compared with the fused contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: Inter-observer variability was not obvious for heart, chambers, and aorta but was large for other structures that were not clearly distinguishable on CT image. The average DSC between individual expert contours and the fused contours were less than 50% for coronary arteries and pulmonary vein, and the average MSD were greater than 4.0 mm. The largest MSD of expert contours deviating from the fused contours was 2.5 cm. The mean DSC and MSD of auto-segmented contours were within one standard deviation of expert contouring variability except the right coronary artery. The coronary arteries, vena cava, and pulmonary vein had DSC<70% and MSD>3.0 mm. Conclusion: A set of cardiac atlases was created for cardiac automatic contouring, the accuracy of which was comparable to the variability in expert contouring. However, substantial modification may need

Microsoft excel's automatic data processing and diagram drawing of RIA internal quality control parameters

International Nuclear Information System (INIS)

Zeng Pingfan; Liu Guoqiang

2006-01-01

We did automatic data processing and diagram drawing of various parameters of RIA' s internal quality control (IQC)by the use of Microsoft Excel (ME). By use of AVERAGE and STDEV of ME, we got x-bar, s and CV%. With pearson, we got the serum quality control coefficients (r). Inputing the original data to diagram's self-definition item, the diagram was drawn automatically. By the use of logic judging, we got the quality control judging results with the status, timing and data of various quality control parameters. For the past four years, the ME data processing and diagram drawing as well as quality control judging have been showed to be accurate, convenient and correct. It was quick and easy to manage and the automatic computer processing of RIA's IQC was realized. Conclusion: the method is applicable to all types of RIA' s IQC. (authors)

Phantom study on three-dimensional target volume delineation by PET/CT-based auto-contouring

International Nuclear Information System (INIS)

Zhang, Tiejiao; Sakaguchi, Yuichi; Mitsumoto, Katsuhiko; Mitsumoto, Tatsuya; Sasaki, Masayuki; Tachiya, Yosuke; Ohya, Nobuyoshi

2010-01-01

The aim of this study was to determine an appropriate threshold value for delineation of the target volume in positron emission tomography (PET)/CT and to investigate whether we could delineate a target volume by phantom studies. A phantom consisted of six spheres (φ10-37 mm) filled with 18 F solution. Data acquisition was performed PET/CT in non-motion and motion status with high 18 F solution and in non-motion status with low 18 F solution. In non-motion phantom experiments, we determined two types of threshold value, an absolute SUV (T SUV ) and a percentage of the maximum SUV (T % ). Delineation using threshold values was applied for all spheres and for selected large spheres (a diameter of 22 mm or larger). In motion phantom experiments, data acquisition was performed in a static mode (sPET) and a gated mode (gPET). CT scanning was performed with helical CT (HCT) and 4-dimentional CT (4DCT). The appropriate threshold values were aT % =27% and aT SUV =2.4 for all spheres, and sT % =30% and sT SUV =4.3 for selected spheres. For all spheres in sPET/HCT in motion, the delineated volumes were 84%-129% by the aT % and 34%-127% by the aT SUV . In gPET/4DCT in motion, the delineated volumes were 94-103% by the aT % and 51-131% by the aT SUV . For low radioactivity spheres, the delineated volumes were all underestimated. A threshold value of T % =27% was proposed for auto-contouring of lung tumors. Our results also suggested that the respiratory gated data acquisition should be performed in both PET and CT for target volume delineation. (author)

Observer variation in target volume delineation of lung cancer related to radiation oncologist-computer interaction: A 'Big Brother' evaluation

International Nuclear Information System (INIS)

Steenbakkers, Roel J.H.M.; Duppen, Joop C.; Fitton, Isabelle; Deurloo, Kirsten E.I.; Zijp, Lambert; Uitterhoeve, Apollonia L.J.; Rodrigus, Patrick T.R.; Kramer, Gijsbert W.P.; Bussink, Johan; Jaeger, Katrien De; Belderbos, Jose S.A.; Hart, Augustinus A.M.; Nowak, Peter J.C.M.; Herk, Marcel van; Rasch, Coen R.N.

2005-01-01

Background and purpose: To evaluate the process of target volume delineation in lung cancer for optimization of imaging, delineation protocol and delineation software. Patients and methods: Eleven radiation oncologists (observers) from five different institutions delineated the Gross Tumor Volume (GTV) including positive lymph nodes of 22 lung cancer patients (stages I-IIIB) on CT only. All radiation oncologist-computer interactions were recorded with a tool called 'Big Brother'. For each radiation oncologist and patient the following issues were analyzed: delineation time, number of delineated points and corrections, zoom levels, level and window (L/W) settings, CT slice changes, use of side windows (coronal and sagittal) and software button use. Results: The mean delineation time per GTV was 16 min (SD 10 min). The mean delineation time for lymph node positive patients was on average 3 min larger (P=0.02) than for lymph node negative patients. Many corrections (55%) were due to L/W change (e.g. delineating in mediastinum L/W and then correcting in lung L/W). For the lymph node region, a relatively large number of corrections was found (3.7 corr/cm 2 ), indicating that it was difficult to delineate lymph nodes. For the tumor-atelectasis region, a relative small number of corrections was found (1.0 corr/cm 2 ), indicating that including or excluding atelectasis into the GTV was a clinical decision. Inappropriate use of L/W settings was frequently found (e.g. 46% of all delineated points in the tumor-lung region were delineated in mediastinum L/W settings). Despite a large observer variation in cranial and caudal direction of 0.72 cm (1 SD), the coronal and sagittal side windows were not used in 45 and 60% of the cases, respectively. For the more difficult cases, observer variation was smaller when the coronal and sagittal side windows were used. Conclusions: With the 'Big Brother' tool a method was developed to trace the delineation process. The differences between

Automatic X-ray inspection for the HTR-PM spherical fuel elements

Energy Technology Data Exchange (ETDEWEB)

Yi, DU, E-mail: duyi11@mails.tsinghua.edu.cn [Institute of Nuclear and New Energy Technology (INET), Tsinghua University, Energy Science Building A309, Haidian District, Beijing 100084 (China); Xiangang, WANG, E-mail: wangxiangang@tsinghua.edu.cn [Institute of Nuclear and New Energy Technology (INET), Tsinghua University, Energy Science Building A309, Haidian District, Beijing 100084 (China); Xincheng, XIANG, E-mail: inetxxc@tsinghua.edu.cn [Institute of Nuclear and New Energy Technology (INET), Tsinghua University, Energy Science Building, Haidian District, Beijing 100084 (China); Bing, LIU, E-mail: bingliu@tsinghua.edu.cn [Institute of Nuclear and New Energy Technology (INET), Tsinghua University, Energy Science Building, Haidian District, Beijing 100084 (China)

2014-12-15

Highlights: • An automatic X-ray inspection method is established to characterize HTR pebbles. • The method provides physical characterization and the inner structure of pebbles. • The method can be conducted non-destructively, quickly and automatically. • Sample pebbles were measured with this AXI method for validation. • The method shows the potential to be applied in situ. - Abstract: Inefficient quality assessment and control (QA and C) of spherical fuel elements for high temperature reactor-pebblebed modules (HTR-PM) has been a long-term problem, since conventional methods are labor intensive and cannot reveal the inside information nondestructively. Herein, we proposed a nondestructive, automated X-ray inspection (AXI) method to characterize spherical fuel elements including their inner structures based on X-ray digital radiography (DR). Briefly, DR images at different angles are first obtained and then the chosen important parameters such as spherical diameters, geometric and mass centers, can be automatically extracted and calculated via image processing techniques. Via evaluating sample spherical fuel elements, we proved that this AXI method can be conducted non-destructively, quickly and automatically. This method not only provides accurate physical characterization of spherical fuel elements but also reveals their inner structure with good resolution, showing great potentials to facilitate fast QA and C in HTM-PM spherical fuel element development and production.

Automatic X-ray inspection for the HTR-PM spherical fuel elements

International Nuclear Information System (INIS)

Yi, DU; Xiangang, WANG; Xincheng, XIANG; Bing, LIU

2014-01-01

Highlights: • An automatic X-ray inspection method is established to characterize HTR pebbles. • The method provides physical characterization and the inner structure of pebbles. • The method can be conducted non-destructively, quickly and automatically. • Sample pebbles were measured with this AXI method for validation. • The method shows the potential to be applied in situ. - Abstract: Inefficient quality assessment and control (QA and C) of spherical fuel elements for high temperature reactor-pebblebed modules (HTR-PM) has been a long-term problem, since conventional methods are labor intensive and cannot reveal the inside information nondestructively. Herein, we proposed a nondestructive, automated X-ray inspection (AXI) method to characterize spherical fuel elements including their inner structures based on X-ray digital radiography (DR). Briefly, DR images at different angles are first obtained and then the chosen important parameters such as spherical diameters, geometric and mass centers, can be automatically extracted and calculated via image processing techniques. Via evaluating sample spherical fuel elements, we proved that this AXI method can be conducted non-destructively, quickly and automatically. This method not only provides accurate physical characterization of spherical fuel elements but also reveals their inner structure with good resolution, showing great potentials to facilitate fast QA and C in HTM-PM spherical fuel element development and production

Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer

Energy Technology Data Exchange (ETDEWEB)

Hoang Duc, Albert K., E-mail: albert.hoangduc.ucl@gmail.com; McClelland, Jamie; Modat, Marc; Cardoso, M. Jorge; Mendelson, Alex F. [Center for Medical Image Computing, University College London, London WC1E 6BT (United Kingdom); Eminowicz, Gemma; Mendes, Ruheena; Wong, Swee-Ling; D’Souza, Derek [Radiotherapy Department, University College London Hospitals, 235 Euston Road, London NW1 2BU (United Kingdom); Veiga, Catarina [Department of Medical Physics and Bioengineering, University College London, London WC1E 6BT (United Kingdom); Kadir, Timor [Mirada Medical UK, Oxford Center for Innovation, New Road, Oxford OX1 1BY (United Kingdom); Ourselin, Sebastien [Centre for Medical Image Computing, University College London, London WC1E 6BT (United Kingdom)

2015-09-15

Purpose: The aim of this study was to assess whether clinically acceptable segmentations of organs at risk (OARs) in head and neck cancer can be obtained automatically and efficiently using the novel “similarity and truth estimation for propagated segmentations” (STEPS) compared to the traditional “simultaneous truth and performance level estimation” (STAPLE) algorithm. Methods: First, 6 OARs were contoured by 2 radiation oncologists in a dataset of 100 patients with head and neck cancer on planning computed tomography images. Each image in the dataset was then automatically segmented with STAPLE and STEPS using those manual contours. Dice similarity coefficient (DSC) was then used to compare the accuracy of these automatic methods. Second, in a blind experiment, three separate and distinct trained physicians graded manual and automatic segmentations into one of the following three grades: clinically acceptable as determined by universal delineation guidelines (grade A), reasonably acceptable for clinical practice upon manual editing (grade B), and not acceptable (grade C). Finally, STEPS segmentations graded B were selected and one of the physicians manually edited them to grade A. Editing time was recorded. Results: Significant improvements in DSC can be seen when using the STEPS algorithm on large structures such as the brainstem, spinal canal, and left/right parotid compared to the STAPLE algorithm (all p < 0.001). In addition, across all three trained physicians, manual and STEPS segmentation grades were not significantly different for the brainstem, spinal canal, parotid (right/left), and optic chiasm (all p > 0.100). In contrast, STEPS segmentation grades were lower for the eyes (p < 0.001). Across all OARs and all physicians, STEPS produced segmentations graded as well as manual contouring at a rate of 83%, giving a lower bound on this rate of 80% with 95% confidence. Reduction in manual interaction time was on average 61% and 93% when automatic

Using aquatic invertebrates to delineate seasonal and temporary wetlands in the Prairie Pothole Region of North America

Science.gov (United States)

Euliss, Ned H.; Mushet, David M.; Johnson, Douglas H.

2002-01-01

Tillage can destroy or greatly disturb indicators of hydric soils and hydrophytic vegetation, making delineation of tilled wetlands difficult. The remains of aquatic invertebrates (e.g., shells, drought-resistant eggs, and trichopteran cases) are easily identifiable and persist in wetland substrates even when wetlands are dry. Additionally, these remains are not easily destroyed by mechanical tillage. To test the feasibility of using invertebrate remains to delineate wetlands, we used two methods to identify the wetland edge of ten seasonal and ten temporary wetlands, evenly divided between grassland and cropland landscapes. First, we identified the wetland edge using hydric soil and vegetation indicators along six evenly spaced transects in each wetland (our “standard” delineation). We then identified the wetland edge along the same transects using aquatic invertebrate remains as our indicator. In grassland landscapes, delineations of the wetland edge made using invertebrate remains were consistently at the same location or closer to the wetland center as the standard delineations for both seasonal and temporary wetlands. In cropland landscapes, however, many of our invertebrate delineations of seasonal and temporary wetlands were on the upland side of our standard delineations. We attribute the differences to movement of remains during tillage, increased maximum pool levels in cropland wetlands, and disturbance of hydric soils and plants. We found that the elevations of the wetland edge indicated by invertebrate remains were more consistent within a wetland than elevations determined by standard delineations. Aquatic invertebrate remains can be useful in delineating wetlands when other indicators have been destroyed or severely disturbed by tillage.

Delineating Rearrangements in Single Yeast Artificial Chromosomes by Quantitative DNA Fiber Mapping

Energy Technology Data Exchange (ETDEWEB)

Weier, Heinz-Ulrich G.; Greulich-Bode, Karin M.; Wu, Jenny; Duell, Thomas

2009-09-18

Cloning of large chunks of human genomic DNA in recombinant systems such as yeast or bacterial artificial chromosomes has greatly facilitated the construction of physical maps, the positional cloning of disease genes or the preparation of patient-specific DNA probes for diagnostic purposes. For this process to work efficiently, the DNA cloning process and subsequent clone propagation need to maintain stable inserts that are neither deleted nor otherwise rearranged. Some regions of the human genome; however, appear to have a higher propensity than others to rearrange in any host system. Thus, techniques to detect and accurately characterize such rearrangements need to be developed. We developed a technique termed 'Quantitative DNA Fiber Mapping (QDFM)' that allows accurate tagging of sequence elements of interest with near kilobase accuracy and optimized it for delineation of rearrangements in recombinant DNA clones. This paper demonstrates the power of this microscopic approach by investigating YAC rearrangements. In our examples, high-resolution physical maps for regions within the immunoglobulin lambda variant gene cluster were constructed for three different YAC clones carrying deletions of 95 kb and more. Rearrangements within YACs could be demonstrated unambiguously by pairwise mapping of cosmids along YAC DNA molecules. When coverage by YAC clones was not available, distances between cosmid clones were estimated by hybridization of cosmids onto DNA fibers prepared from human genomic DNA. In addition, the QDFM technology provides essential information about clone stability facilitating closure of the maps of the human genome as well as those of model organisms.

Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement

Science.gov (United States)

Matsuda, Noboru; Furukawa, Tadanobu; Bier, Norman; Faloutsos, Christos

2015-01-01

How can we automatically determine which skills must be mastered for the successful completion of an online course? Large-scale online courses (e.g., MOOCs) often contain a broad range of contents frequently intended to be a semester's worth of materials; this breadth often makes it difficult to articulate an accurate set of skills and knowledge…

Full automatic fiducial marker detection on coil arrays for accurate instrumentation placement during MRI guided breast interventions

Science.gov (United States)

Filippatos, Konstantinos; Boehler, Tobias; Geisler, Benjamin; Zachmann, Harald; Twellmann, Thorsten

2010-02-01

With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this purpose have to be manually or semi-automatically detected by the user. In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of 0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that was appointed from a radiologist was 0.94mm.

Long Baseline Stereovision for Automatic Detection and Ranging of Moving Objects in the Night Sky

Directory of Open Access Journals (Sweden)

Vlad Turcu

2012-09-01

Full Text Available As the number of objects in Earth’s atmosphere and in low Earth orbit is continuously increasing; accurate surveillance of these objects has become important. This paper presents a generic, low cost sky surveillance system based on stereovision. Two cameras are placed 37 km apart and synchronized by a GPS-controlled external signal. The intrinsic camera parameters are calibrated before setup in the observation position, the translation vectors are determined from the GPS coordinates and the rotation matrices are continuously estimated using an original automatic calibration methodology based on following known stars. The moving objects in the sky are recognized as line segments in the long exposure images, using an automatic detection and classification algorithm based on image processing. The stereo correspondence is based on the epipolar geometry and is performed automatically using the image detection results. The resulting experimental system is able to automatically detect moving objects such as planes, meteors and Low Earth Orbit satellites, and measure their 3D position in an Earth-bound coordinate system.

High-order space charge effects using automatic differentiation

International Nuclear Information System (INIS)

Reusch, Michael F.; Bruhwiler, David L.

1997-01-01

The Northrop Grumman Topkark code has been upgraded to Fortran 90, making use of operator overloading, so the same code can be used to either track an array of particles or construct a Taylor map representation of the accelerator lattice. We review beam optics and beam dynamics simulations conducted with TOPKARK in the past and we present a new method for modeling space charge forces to high-order with automatic differentiation. This method generates an accurate, high-order, 6-D Taylor map of the phase space variable trajectories for a bunched, high-current beam. The spatial distribution is modeled as the product of a Taylor Series times a Gaussian. The variables in the argument of the Gaussian are normalized to the respective second moments of the distribution. This form allows for accurate representation of a wide range of realistic distributions, including any asymmetries, and allows for rapid calculation of the space charge fields with free space boundary conditions. An example problem is presented to illustrate our approach

Delineation of wetland areas from high resolution WorldView-2 data by object-based method

International Nuclear Information System (INIS)

Hassan, N; Hamid, J R A; Adnan, N A; Jaafar, M

2014-01-01

Various classification methods are available that can be used to delineate land cover types. Object-based is one of such methods for delineating the land cover from satellite imageries. This paper focuses on the digital image processing aspects of discriminating wetland areas via object-based method using high resolution satellite multispectral WorldView-2 image data taken over part of Penang Island region. This research is an attempt to improve the wetland area delineation in conjunction with a range of classification techniques which can be applied to satellite data with high spatial and spectral resolution such as World View 2. The intent is to determine a suitable approach to delineate and map these wetland areas more appropriately. There are common parameters to take into account that are pivotal in object-based method which are the spatial resolution and the range of spectral channels of the imaging sensor system. The preliminary results of the study showed object-based analysis is capable of delineating wetland region of interest with an accuracy that is acceptable to the required tolerance for land cover classification

Software and hardware platform for testing of Automatic Generation Control algorithms

Directory of Open Access Journals (Sweden)

Vasiliev Alexey

2017-01-01

Full Text Available Development and implementation of new Automatic Generation Control (AGC algorithms requires testing them on a model that adequately simulates primary energetic, information and control processes. In this article an implementation of a test platform based on HRTSim (Hybrid Real Time Simulator and SCADA CK-2007 (which is widely used by the System Operator of Russia is proposed. Testing of AGC algorithms on the test platform based on the same SCADA system that is used in operation allows to exclude errors associated with the transfer of AGC algorithms and settings from the test platform to a real power system. A power system including relay protection, automatic control systems and emergency control automatics can be accurately simulated on HRTSim. Besides the information commonly used by conventional AGC systems HRTSim is able to provide a resemblance of Phasor Measurement Unit (PMU measurements (information about rotor angles, magnitudes and phase angles of currents and voltages etc.. The additional information significantly expands the number of possible AGC algorithms so the test platform is useful in modern AGC system developing. The obtained test results confirm that the proposed system is applicable for the tasks mentioned above.

A study of prostate delineation referenced against a gold standard created from the visible human data

International Nuclear Information System (INIS)

Gao Zhanrong; Wilkins, David; Eapen, Libni; Morash, Christopher; Wassef, Youssef; Gerig, Lee

2007-01-01

Purpose: To measure inter- and intra-observer variation and systematic error in CT based prostate delineation, where individual delineations are referenced against a gold standard produced from photographic anatomical images from the Visible Human Project (VHP). Materials and methods: The CT and anatomical images of the VHP male form the basic data set for this study. The gold standard was established based on 1 mm thick anatomical photographic images. These were registered against the 3 mm thick CT images that were used for target delineation. A total of 120 organ delineations were performed by six radiation oncologists. Results: The physician delineated prostate volume was on average 30% larger than the 'true' prostate volume, but on average included only 84% of the gold standard volume. Our study found a systematic delineation error such that posterior portions of the prostate were always missed while anteriorly some normal tissue was always defined as target. Conclusions: Our data suggest that radiation oncologists are more concerned with the unintentional inclusion of rectal tissue than they are in missing prostate volume. In contrast, they are likely to overextend the anterior boundary of the prostate to encompass normal tissue such as the bladder

A multi-standard active-RC filter with accurate tuning system

Energy Technology Data Exchange (ETDEWEB)

Ma Heping; Yuan Fang; Shi Yin [Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083 (China); Dai, F F, E-mail: hpma@semi.ac.c [Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849-5201 (United States)

2009-09-15

A low-power, highly linear, multi-standard, active-RC filter with an accurate and novel tuning architecture is presented. It exhibits IEEE 802.11 a/b/g (9.5 MHz) and DVB-H (3 MHz, 4 MHz) application. The filter exploits digitally-controlled polysilicon resistor banks and a phase lock loop type automatic tuning system. The novel and complex automatic frequency calibration scheme provides better than 4 corner frequency accuracy, and it can be powered down after calibration to save power and avoid digital signal interference. The filter achieves OIP3 of 26 dBm and the measured group delay variation of the receiver filter is 50 ns (WLAN mode). Its dissipation is 3.4 mA in RX mode and 2.3 mA (only for one path) in TX mode from a 2.85 V supply. The dissipation of calibration consumes 2 mA. The circuit has been fabricated in a 0.35 {mu}m 47 GHz SiGe BiCMOS technology; the receiver and transmitter filter occupy 0.21 mm{sup 2} and 0.11 mm{sup 2} (calibration circuit excluded), respectively.

Automatic Fiscal Stabilizers

Directory of Open Access Journals (Sweden)

Narcis Eduard Mitu

2013-11-01

Full Text Available Policies or institutions (built into an economic system that automatically tend to dampen economic cycle fluctuations in income, employment, etc., without direct government intervention. For example, in boom times, progressive income tax automatically reduces money supply as incomes and spendings rise. Similarly, in recessionary times, payment of unemployment benefits injects more money in the system and stimulates demand. Also called automatic stabilizers or built-in stabilizers.

Geometrical Comparison Measures for Tumor Delineation, what do they mean for the Actual Dosis Plan?

DEFF Research Database (Denmark)

Hollensen, Christian; Persson, G.; Højgaard, L.

2012-01-01

Purpose/Objective: Gross tumour volume (GTV) delineation is central for radiotherapy planning. It provides the basis of the clinical target volume and finally the planning target volume (PTV) which is used for dose optimization. GTV delineations are prone to intermethod and inter......observer variation. In clinical studies this variation is commonly represented by geometrical volume comparison measures (GVCMs) as volume assessment, centre of mass and overlap. The correlation between these measures and the radiotherapy plan are however unclear. The aim of the present study is to investigate...... the correlation between GVCMs and the radiotherapy plans of patients with peripheral lung tumours. Materials and Methods: Peripheral lung tumours of 10 patients referred for stereotactic body radiotherapy in 2008 were delineated by 3 radiologists and 3 oncologists. From these GTV delineations 6 different...

Differences in delineation guidelines for head and neck cancer result in inconsistent reported dose and corresponding NTCP

International Nuclear Information System (INIS)

Brouwer, Charlotte L.; Steenbakkers, Roel J.H.M.; Gort, Elske; Kamphuis, Marije E.; Laan, Hans Paul van der; Veld, Aart A. van’t; Sijtsema, Nanna M.; Langendijk, Johannes A.

2014-01-01

Purpose: To test the hypothesis that delineation of swallowing organs at risk (SWOARs) based on different guidelines results in differences in dose–volume parameters and subsequent normal tissue complication probability (NTCP) values for dysphagia-related endpoints. Materials and methods: Nine different SWOARs were delineated according to five different delineation guidelines in 29 patients. Reference delineation was performed according to the guidelines and NTCP-models of Christianen et al. Concordance Index (CI), dosimetric consequences, as well as differences in the subsequent NTCPs were calculated. Results: The median CI of the different delineation guidelines with the reference guidelines was 0.54 for the pharyngeal constrictor muscles, 0.56 for the laryngeal structures and 0.07 for the cricopharyngeal muscle and esophageal inlet muscle. The average difference in mean dose to the SWOARs between the guidelines with the largest difference (maxΔD) was 3.5 ± 3.2 Gy. A mean ΔNTCP of 2.3 ± 2.7% was found. For two patients, ΔNTCP exceeded 10%. Conclusions: The majority of the patients showed little differences in NTCPs between the different delineation guidelines. However, large NTCP differences >10% were found in 7% of the patients. For correct use of NTCP models in individual patients, uniform delineation guidelines are of great importance

Automatic anatomy recognition in whole-body PET/CT images

Energy Technology Data Exchange (ETDEWEB)

Wang, Huiqian [College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China and Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey; Tong, Yubing; Torigian, Drew A. [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Zhao, Liming [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China)

2016-01-15

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

Automatic anatomy recognition in whole-body PET/CT images

International Nuclear Information System (INIS)

Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.; Zhao, Liming

2016-01-01

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

A novel device for automatic withdrawal and accurate calibration of 99m-technetium radiopharmaceuticals to minimise radiation exposure to nuclear medicine staff and patient

International Nuclear Information System (INIS)

Nazififard, M.; Mahdizadeh, S.; Meigooni, A. S.; Alavi, M.; Suh, K. Y.

2012-01-01

A Joint Automatic Dispenser Equipment (JADE) has been designed and fabricated for automatic withdrawal and calibration of radiopharmaceutical materials. The thermoluminescent dosemeter procedures have shown a reduction in dose to the technician's hand with this novel dose dispenser system JADE when compared with the manual withdrawal of 99m Tc. This system helps to increase the precision of calibration and to minimise the radiation dose to the hands and body of the workers. This paper describes the structure of this device, its function and user-friendliness, and its efficacy. The efficacy of this device was determined by measuring the radiation dose delivered to the hands of the nuclear medicine laboratory technician. The user-friendliness of JADE has been examined. The automatic withdrawal and calibration offered by this system reduces the dose to the technician's hand to a level below the maximum permissible dose stipulated by the international protocols. This research will serve as a backbone for future study about the safe use of ionising radiation in medicine. (authors)

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Directory of Open Access Journals (Sweden)

Syed Ali Naqi Gilani

2016-03-01

Full Text Available The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object’s size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m2, building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian in contrast to the ISPRS benchmark, where it does better or equal to the counterparts.

Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.

Science.gov (United States)

Almeida, Diogo F; Ruben, Rui B; Folgado, João; Fernandes, Paulo R; Audenaert, Emmanuel; Verhegghe, Benedict; De Beule, Matthieu

2016-12-01

Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans. Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach. With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1mm. For the low resolution image group the results are also accurate and the average error is less than 1.5mm. The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Delineating social network data anonymization via random edge perturbation

KAUST Repository

Xue, Mingqiang; Karras, Panagiotis; Raï ssi, Chedy; Kalnis, Panos; Pung, Hungkeng

2012-01-01

study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments

Development and application of an automatic system for measuring the laser camera

International Nuclear Information System (INIS)

Feng Shuli; Peng Mingchen; Li Kuncheng

2004-01-01

Objective: To provide an automatic system for measuring imaging quality of laser camera, and to make an automatic measurement and analysis system. Methods: On the special imaging workstation (SGI 540), the procedure was written by using Matlab language. An automatic measurement and analysis system of imaging quality for laser camera was developed and made according to the imaging quality measurement standard of laser camera of International Engineer Commission (IEC). The measurement system used the theories of digital signal processing, and was based on the characteristics of digital images, as well as put the automatic measurement and analysis of laser camera into practice by the affiliated sample pictures of the laser camera. Results: All the parameters of imaging quality of laser camera, including H-D and MTF curve, low and middle and high resolution of optical density, all kinds of geometry distort, maximum and minimum density, as well as the dynamic range of gray scale, could be measured by this system. The system was applied for measuring the laser cameras in 20 hospitals in Beijing. The measuring results showed that the system could provide objective and quantitative data, and could accurately evaluate the imaging quality of laser camera, as well as correct the results made by manual measurement based on the affiliated sample pictures of the laser camera. Conclusion: The automatic measuring system of laser camera is an effective and objective tool for testing the quality of the laser camera, and the system makes a foundation for the future research

ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer

International Nuclear Information System (INIS)

Offersen, Birgitte V.; Boersma, Liesbeth J.; Kirkove, Carine; Hol, Sandra; Aznar, Marianne C.; Biete Sola, Albert; Kirova, Youlia M.; Pignol, Jean-Philippe; Remouchamps, Vincent; Verhoeven, Karolien; Weltens, Caroline; Arenas, Meritxell; Gabrys, Dorota; Kopek, Neil; Krause, Mechthild; Lundstedt, Dan; Marinko, Tanja

2015-01-01

Background and purpose: Delineation of clinical target volumes (CTVs) is a weak link in radiation therapy (RT), and large inter-observer variation is seen in breast cancer patients. Several guidelines have been proposed, but most result in larger CTVs than based on conventional simulator-based RT. The aim was to develop a delineation guideline obtained by consensus between a broad European group of radiation oncologists. Material and methods: During ESTRO teaching courses on breast cancer, teachers sought consensus on delineation of CTV through dialogue based on cases. One teacher delineated CTV on CT scans of 2 patients, followed by discussion and adaptation of the delineation. The consensus established between teachers was sent to other teams working in the same field, both locally and on a national level, for their input. This was followed by developing a broad consensus based on discussions. Results: Borders of the CTV encompassing a 5 mm margin around the large veins, running through the regional lymph node levels were agreed, and for the breast/thoracic wall other vessels were pointed out to guide delineation, with comments on margins for patients with advanced breast cancer. Conclusion: The ESTRO consensus on CTV for elective RT of breast cancer, endorsed by a broad base of the radiation oncology community, is presented to improve consistency

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

Science.gov (United States)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Automatic management software for large-scale cluster system

International Nuclear Information System (INIS)

Weng Yunjian; Chinese Academy of Sciences, Beijing; Sun Gongxing

2007-01-01

At present, the large-scale cluster system faces to the difficult management. For example the manager has large work load. It needs to cost much time on the management and the maintenance of large-scale cluster system. The nodes in large-scale cluster system are very easy to be chaotic. Thousands of nodes are put in big rooms so that some managers are very easy to make the confusion with machines. How do effectively carry on accurate management under the large-scale cluster system? The article introduces ELFms in the large-scale cluster system. Furthermore, it is proposed to realize the large-scale cluster system automatic management. (authors)

Iterative Otsu's method for OCT improved delineation in the aorta wall

Science.gov (United States)

Alonso, Daniel; Real, Eusebio; Val-Bernal, José F.; Revuelta, José M.; Pontón, Alejandro; Calvo Díez, Marta; Mayorga, Marta; López-Higuera, José M.; Conde, Olga M.

2015-07-01

Degradation of human ascending thoracic aorta has been visualized with Optical Coherence Tomography (OCT). OCT images of the vessel wall exhibit structural degradation in the media layer of the artery, being this disorder the final trigger of the pathology. The degeneration in the vessel wall appears as low-reflectivity areas due to different optical properties of acidic polysaccharides and mucopolysaccharides in contrast with typical ordered structure of smooth muscle cells, elastin and collagen fibers. An OCT dimension indicator of wall degradation can be generated upon the spatial quantification of the extension of degraded areas in a similar way as conventional histopathology. This proposed OCT marker can offer in the future a real-time clinical perception of the vessel status to help cardiovascular surgeons in vessel repair interventions. However, the delineation of degraded areas on the B-scan image from OCT is sometimes difficult due to presence of speckle noise, variable signal to noise ratio (SNR) conditions on the measurement process, etc. Degraded areas can be delimited by basic thresholding techniques taking advantage of disorders evidences in B-scan images, but this delineation is not optimum in the aorta samples and requires complex additional processing stages. This work proposes an optimized delineation of degraded areas within the aorta wall, robust to noisy environments, based on the iterative application of Otsu's thresholding method. Results improve the delineation of wall anomalies compared with the simple application of the algorithm. Achievements could be also transferred to other clinical scenarios: carotid arteries, aorto-iliac or ilio-femoral sections, intracranial, etc.

Optimising delineation accuracy of tumours in PET for radiotherapy planning using blind deconvolution

International Nuclear Information System (INIS)

Guvenis, A.; Koc, A.

2015-01-01

Positron emission tomography (PET) imaging has been proven to be useful in radiotherapy planning for the determination of the metabolically active regions of tumours. Delineation of tumours, however, is a difficult task in part due to high noise levels and the partial volume effects originating mainly from the low camera resolution. The goal of this work is to study the effect of blind deconvolution on tumour volume estimation accuracy for different computer-aided contouring methods. The blind deconvolution estimates the point spread function (PSF) of the imaging system in an iterative manner in a way that the likelihood of the given image being the convolution output is maximised. In this way, the PSF of the imaging system does not need to be known. Data were obtained from a NEMA NU-2 IQ-based phantom with a GE DSTE-16 PET/CT scanner. The artificial tumour diameters were 13, 17, 22, 28 and 37 mm with a target/background ratio of 4:1. The tumours were delineated before and after blind deconvolution. Student's two-tailed paired t-test showed a significant decrease in volume estimation error ( p < 0.001) when blind deconvolution was used in conjunction with computer-aided delineation methods. A manual delineation confirmation demonstrated an improvement from 26 to 16 % for the artificial tumour of size 37 mm while an improvement from 57 to 15 % was noted for the small tumour of 13 mm. Therefore, it can be concluded that blind deconvolution of reconstructed PET images may be used to increase tumour delineation accuracy. (authors)

Automatic limb identification and sleeping parameters assessment for pressure ulcer prevention.

Science.gov (United States)

Baran Pouyan, Maziyar; Birjandtalab, Javad; Nourani, Mehrdad; Matthew Pompeo, M D

2016-08-01

Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

Perioperative Clinical Nurse Specialist Role Delineation: A Systematic Review

National Research Council Canada - National Science Library

Cole, Lisa M; Walker, Theodore J; Nader, Kelly C; Glover, Dennis E; Newkirk, Laura E

2006-01-01

A clearly defined role of the Perioperative Clinical Nurse Specialist (PONS) is not identified. The purpose of this study was to provide recommendations for a delineated role of the PONS that will provide role clarity and practice guidance...

Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils

International Nuclear Information System (INIS)

Juang, Kai-Wei; Chen, Yue-Shin; Lee, Dar-Yuan

2004-01-01

Mapping the spatial distribution of soil pollutants is essential for delineating contaminated areas. Currently, geostatistical interpolation, kriging, is increasingly used to estimate pollutant concentrations in soils. The kriging-based approach, indicator kriging (IK), may be used to model the uncertainty of mapping. However, a smoothing effect is usually produced when using kriging in pollutant mapping. The detailed spatial patterns of pollutants could, therefore, be lost. The local uncertainty of mapping pollutants derived by the IK technique is referred to as the conditional cumulative distribution function (ccdf) for one specific location (i.e. single-location uncertainty). The local uncertainty information obtained by IK is not sufficient as the uncertainty of mapping at several locations simultaneously (i.e. multi-location uncertainty or spatial uncertainty) is required to assess the reliability of the delineation of contaminated areas. The simulation approach, sequential indicator simulation (SIS), which has the ability to model not only single, but also multi-location uncertainties, was used, in this study, to assess the uncertainty of the delineation of heavy metal contaminated soils. To illustrate this, a data set of Cu concentrations in soil from Taiwan was used. The results show that contour maps of Cu concentrations generated by the SIS realizations exhausted all the spatial patterns of Cu concentrations without the smoothing effect found when using the kriging method. Based on the SIS realizations, the local uncertainty of Cu concentrations at a specific location of x', refers to the probability of the Cu concentration z(x') being higher than the defined threshold level of contamination (z c ). This can be written as Prob SIS [z(x')>z c ], representing the probability of contamination. The probability map of Prob SIS [z(x')>z c ] can then be used for delineating contaminated areas. In addition, the multi-location uncertainty of an area A,delineated

Delineating social network data anonymization via random edge perturbation

KAUST Repository

Xue, Mingqiang

2012-01-01

Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations. © 2012 ACM.

Automatic Segmentation and Quantification of Filamentous Structures in Electron Tomography.

Science.gov (United States)

Loss, Leandro A; Bebis, George; Chang, Hang; Auer, Manfred; Sarkar, Purbasha; Parvin, Bahram

2012-10-01

Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides.

A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

Science.gov (United States)

Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

2014-12-01

Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

Automatic respiration tracking for radiotherapy using optical 3D camera

Science.gov (United States)

Li, Tuotuo; Geng, Jason; Li, Shidong

2013-03-01

Rapid optical three-dimensional (O3D) imaging systems provide accurate digitized 3D surface data in real-time, with no patient contact nor radiation. The accurate 3D surface images offer crucial information in image-guided radiation therapy (IGRT) treatments for accurate patient repositioning and respiration management. However, applications of O3D imaging techniques to image-guided radiotherapy have been clinically challenged by body deformation, pathological and anatomical variations among individual patients, extremely high dimensionality of the 3D surface data, and irregular respiration motion. In existing clinical radiation therapy (RT) procedures target displacements are caused by (1) inter-fractional anatomy changes due to weight, swell, food/water intake; (2) intra-fractional variations from anatomy changes within any treatment session due to voluntary/involuntary physiologic processes (e.g. respiration, muscle relaxation); (3) patient setup misalignment in daily reposition due to user errors; and (4) changes of marker or positioning device, etc. Presently, viable solution is lacking for in-vivo tracking of target motion and anatomy changes during the beam-on time without exposing patient with additional ionized radiation or high magnet field. Current O3D-guided radiotherapy systems relay on selected points or areas in the 3D surface to track surface motion. The configuration of the marks or areas may change with time that makes it inconsistent in quantifying and interpreting the respiration patterns. To meet the challenge of performing real-time respiration tracking using O3D imaging technology in IGRT, we propose a new approach to automatic respiration motion analysis based on linear dimensionality reduction technique based on PCA (principle component analysis). Optical 3D image sequence is decomposed with principle component analysis into a limited number of independent (orthogonal) motion patterns (a low dimension eigen-space span by eigen-vectors). New

Delineating psychomotor slowing from reduced processing speed in schizophrenia

NARCIS (Netherlands)

Morrens, M.; Hulstijn, W.; Matton, C.; Madani, Y.; Bouwel, L. van; Peuskens, J.; Sabbe, B.G.C.

2008-01-01

Introduction. Psychomotor slowing is an intrinsic feature of schizophrenia that is poorly delineated from generally reduced processing speed. Although the Symbol Digit Substitution Test (SDST) is widely used to assess psychomotor speed, the task also taps several higher-order cognitive processes.

Automatic alignment device for focal spot measurements in the center of the field for mammography

International Nuclear Information System (INIS)

Vieira, Marcelo A.C.; Watanabe, Alex O.; Oliveira Junior, Paulo D.; Schiabel, Homero

2010-01-01

Some quality control procedures used for mammography, such as focal spot evaluation, requires previous alignment of the measurement equipment with the X-ray central beam. However, alignment procedures are, in general, the most difficult task and the one that needs more time to be performed. Moreover, the operator sometimes is exposed to radiation during this procedure. This work presents an automatic alignment system for mammographic equipment that allows locating the central ray of the radiation beam and, immediately, aligns with it by dislocating itself automatically along the field. The system consists on a bidirectional moving device, connected to a CCD sensor for digital radiographic image acquisition. A computational analysis of a radiographic image, acquired at any position on the field, is performed in order to determine its positioning under the X-ray beam. Finally, a mechanical system for two moving directions, electronically controlled by a microcontroller under USB communication, makes the system to align automatically with the radiation beam central ray. The alignment process is fully automatic, fast and accurate, with no operator exposure to radiation, which allows a considerable time saving for quality control procedures achievement for mammography. (author)

Automatic mesh refinement and local multigrid methods for contact problems: application to the Pellet-Cladding mechanical Interaction

International Nuclear Information System (INIS)

Liu, Hao

2016-01-01

This Ph.D. work takes place within the framework of studies on Pellet-Cladding mechanical Interaction (PCI) which occurs in the fuel rods of pressurized water reactor. This manuscript focuses on automatic mesh refinement to simulate more accurately this phenomena while maintaining acceptable computational time and memory space for industrial calculations. An automatic mesh refinement strategy based on the combination of the Local Defect Correction multigrid method (LDC) with the Zienkiewicz and Zhu a posteriori error estimator is proposed. The estimated error is used to detect the zones to be refined, where the local sub-grids of the LDC method are generated. Several stopping criteria are studied to end the refinement process when the solution is accurate enough or when the refinement does not improve the global solution accuracy anymore. Numerical results for elastic 2D test cases with pressure discontinuity show the efficiency of the proposed strategy. The automatic mesh refinement in case of unilateral contact problems is then considered. The strategy previously introduced can be easily adapted to the multi-body refinement by estimating solution error on each body separately. Post-processing is often necessary to ensure the conformity of the refined areas regarding the contact boundaries. A variety of numerical experiments with elastic contact (with or without friction, with or without an initial gap) confirms the efficiency and adaptability of the proposed strategy. (author) [fr

Estimating patient dose from CT exams that use automatic exposure control: Development and validation of methods to accurately estimate tube current values.

Science.gov (United States)

McMillan, Kyle; Bostani, Maryam; Cagnon, Christopher H; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H; McNitt-Gray, Michael F

2017-08-01

The vast majority of body CT exams are performed with automatic exposure control (AEC), which adapts the mean tube current to the patient size and modulates the tube current either angularly, longitudinally or both. However, most radiation dose estimation tools are based on fixed tube current scans. Accurate estimates of patient dose from AEC scans require knowledge of the tube current values, which is usually unavailable. The purpose of this work was to develop and validate methods to accurately estimate the tube current values prescribed by one manufacturer's AEC system to enable accurate estimates of patient dose. Methods were developed that took into account available patient attenuation information, user selected image quality reference parameters and x-ray system limits to estimate tube current values for patient scans. Methods consistent with AAPM Report 220 were developed that used patient attenuation data that were: (a) supplied by the manufacturer in the CT localizer radiograph and (b) based on a simulated CT localizer radiograph derived from image data. For comparison, actual tube current values were extracted from the projection data of each patient. Validation of each approach was based on data collected from 40 pediatric and adult patients who received clinically indicated chest (n = 20) and abdomen/pelvis (n = 20) scans on a 64 slice multidetector row CT (Sensation 64, Siemens Healthcare, Forchheim, Germany). For each patient dataset, the following were collected with Institutional Review Board (IRB) approval: (a) projection data containing actual tube current values at each projection view, (b) CT localizer radiograph (topogram) and (c) reconstructed image data. Tube current values were estimated based on the actual topogram (actual-topo) as well as the simulated topogram based on image data (sim-topo). Each of these was compared to the actual tube current values from the patient scan. In addition, to assess the accuracy of each method in estimating

Finding weak points automatically

International Nuclear Information System (INIS)

Archinger, P.; Wassenberg, M.

1999-01-01

Operators of nuclear power stations have to carry out material tests at selected components by regular intervalls. Therefore a full automaticated test, which achieves a clearly higher reproducibility, compared to part automaticated variations, would provide a solution. In addition the full automaticated test reduces the dose of radiation for the test person. (orig.) [de

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

Directory of Open Access Journals (Sweden)

Q. Wu

2017-07-01

Full Text Available In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling–spilling–merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

Automatic tissue image segmentation based on image processing and deep learning

Science.gov (United States)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint.

Science.gov (United States)

Liukkonen, Mimmi K; Mononen, Mika E; Tanska, Petri; Saarakkala, Simo; Nieminen, Miika T; Korhonen, Rami K

2017-10-01

Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

Automatic lung segmentation in the presence of alveolar collapse

Directory of Open Access Journals (Sweden)

Noshadi Areg

2017-09-01

Full Text Available Lung ventilation and perfusion analyses using chest imaging methods require a correct segmentation of the lung to offer anatomical landmarks for the physiological data. An automatic segmentation approach simplifies and accelerates the analysis. However, the segmentation of the lungs has shown to be difficult if collapsed areas are present that tend to share similar gray values with surrounding non-pulmonary tissue. Our goal was to develop an automatic segmentation algorithm that is able to approximate dorsal lung boundaries even if alveolar collapse is present in the dependent lung areas adjacent to the pleura. Computed tomography data acquired in five supine pigs with injured lungs were used for this purpose. First, healthy lung tissue was segmented using a standard 3D region growing algorithm. Further, the bones in the chest wall surrounding the lungs were segmented to find the contact points of ribs and pleura. Artificial boundaries of the dorsal lung were set by spline interpolation through these contact points. Segmentation masks of the entire lung including the collapsed regions were created by combining the splines with the segmentation masks of the healthy lung tissue through multiple morphological operations. The automatically segmented images were then evaluated by comparing them to manual segmentations and determining the Dice similarity coefficients (DSC as a similarity measure. The developed method was able to accurately segment the lungs including the collapsed regions (DSCs over 0.96.

Preliminary study on computer automatic quantification of brain atrophy

International Nuclear Information System (INIS)

Li Chuanfu; Zhou Kangyuan

2006-01-01

Objective: To study the variability of normal brain volume with the sex and age, and put forward an objective standard for computer automatic quantification of brain atrophy. Methods: The cranial volume, brain volume and brain parenchymal fraction (BPF) of 487 cases of brain atrophy (310 males, 177 females) and 1901 cases of normal subjects (993 males, 908 females) were calculated with the newly developed algorithm of automatic quantification for brain atrophy. With the technique of polynomial curve fitting, the mathematical relationship of BPF with age in normal subjects was analyzed. Results: The cranial volume, brain volume and BPF of normal subjects were (1 271 322 ± 128 699) mm 3 , (1 211 725 ± 122 077) mm 3 and (95.3471 ± 2.3453)%, respectively, and those of atrophy subjects were (1 276 900 ± 125 180) mm 3 , (1 203 400 ± 117 760) mm 3 and BPF(91.8115 ± 2.3035)% respectively. The difference of BPF between the two groups was extremely significant (P 0.05). The expression P(x)=-0.0008x 2 + 0.0193x + 96.9999 could accurately describe the mathematical relationship between BPF and age in normal subject (lower limit of 95% CI y=-0.0008x 2 +0.0184x+95.1090). Conclusion: The lower limit of 95% confidence interval mathematical relationship between BPF and age could be used as an objective criteria for automatic quantification of brain atrophy with computer. (authors)

The automatic component of habit in health behavior: habit as cue-contingent automaticity.

Science.gov (United States)

Orbell, Sheina; Verplanken, Bas

2010-07-01

Habit might be usefully characterized as a form of automaticity that involves the association of a cue and a response. Three studies examined habitual automaticity in regard to different aspects of the cue-response relationship characteristic of unhealthy and healthy habits. In each study, habitual automaticity was assessed by the Self-Report Habit Index (SRHI). In Study 1 SRHI scores correlated with attentional bias to smoking cues in a Stroop task. Study 2 examined the ability of a habit cue to elicit an unwanted habit response. In a prospective field study, habitual automaticity in relation to smoking when drinking alcohol in a licensed public house (pub) predicted the likelihood of cigarette-related action slips 2 months later after smoking in pubs had become illegal. In Study 3 experimental group participants formed an implementation intention to floss in response to a specified situational cue. Habitual automaticity of dental flossing was rapidly enhanced compared to controls. The studies provided three different demonstrations of the importance of cues in the automatic operation of habits. Habitual automaticity assessed by the SRHI captured aspects of a habit that go beyond mere frequency or consistency of the behavior. PsycINFO Database Record (c) 2010 APA, all rights reserved.

High-order space charge effects using automatic differentiation

International Nuclear Information System (INIS)

Reusch, M.F.; Bruhwiler, D.L.; Computer Accelerator Physics Conference Williamsburg, Virginia 1996)

1997-01-01

The Northrop Grumman Topkark code has been upgraded to Fortran 90, making use of operator overloading, so the same code can be used to either track an array of particles or construct a Taylor map representation of the accelerator lattice. We review beam optics and beam dynamics simulations conducted with TOPKARK in the past and we present a new method for modeling space charge forces to high-order with automatic differentiation. This method generates an accurate, high-order, 6-D Taylor map of the phase space variable trajectories for a bunched, high-current beam. The spatial distribution is modeled as the product of a Taylor Series times a Gaussian. The variables in the argument of the Gaussian are normalized to the respective second moments of the distribution. This form allows for accurate representation of a wide range of realistic distributions, including any asymmetries, and allows for rapid calculation of the space charge fields with free space boundary conditions. An example problem is presented to illustrate our approach. copyright 1997 American Institute of Physics

TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

International Nuclear Information System (INIS)

Rios Velazquez, E; Meier, R; Dunn, W; Gutman, D; Alexander, B; Wiest, R; Reyes, M; Bauer, S; Aerts, H

2015-01-01

Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showed high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research

TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

Energy Technology Data Exchange (ETDEWEB)

Rios Velazquez, E [Dana-Farber Cancer Institute | Harvard Medical School, Boston, MA (United States); Meier, R [Institute for Surgical Technology and Biomechanics, Bern, NA (Switzerland); Dunn, W; Gutman, D [Emory University School of Medicine, Atlanta, GA (United States); Alexander, B [Dana- Farber Cancer Institute, Brigham and Womens Hospital, Harvard Medic, Boston, MA (United States); Wiest, R; Reyes, M [Institute for Surgical Technology and Biomechanics, University of Bern, Bern, NA (Switzerland); Bauer, S [Institute for Surgical Technology and Biomechanics, Support Center for Adva, Bern, NA (Switzerland); Aerts, H [Dana-Farber/Brigham Womens Cancer Center, Boston, MA (United States)

2015-06-15

Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showed high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research.

Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.

Science.gov (United States)

Chiu, Stephanie J; Izatt, Joseph A; O'Connell, Rachelle V; Winter, Katrina P; Toth, Cynthia A; Farsiu, Sina

2012-01-05

To automatically segment retinal spectral domain optical coherence tomography (SD-OCT) images of eyes with age-related macular degeneration (AMD) and various levels of image quality to advance the study of retinal pigment epithelium (RPE)+drusen complex (RPEDC) volume changes indicative of AMD progression. A general segmentation framework based on graph theory and dynamic programming was used to segment three retinal boundaries in SD-OCT images of eyes with drusen and geographic atrophy (GA). A validation study for eyes with nonneovascular AMD was conducted, forming subgroups based on scan quality and presence of GA. To test for accuracy, the layer thickness results from two certified graders were compared against automatic segmentation results for 220 B-scans across 20 patients. For reproducibility, automatic layer volumes were compared that were generated from 0° versus 90° scans in five volumes with drusen. The mean differences in the measured thicknesses of the total retina and RPEDC layers were 4.2 ± 2.8 and 3.2 ± 2.6 μm for automatic versus manual segmentation. When the 0° and 90° datasets were compared, the mean differences in the calculated total retina and RPEDC volumes were 0.28% ± 0.28% and 1.60% ± 1.57%, respectively. The average segmentation time per image was 1.7 seconds automatically versus 3.5 minutes manually. The automatic algorithm accurately and reproducibly segmented three retinal boundaries in images containing drusen and GA. This automatic approach can reduce time and labor costs and yield objective measurements that potentially reveal quantitative RPE changes in longitudinal clinical AMD studies. (ClinicalTrials.gov number, NCT00734487.).

Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

International Nuclear Information System (INIS)

Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sebastien

2007-01-01

The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis

Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

Energy Technology Data Exchange (ETDEWEB)

Fripp, Jurgen [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia); Crozier, Stuart [School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD 4072 (Australia); Warfield, Simon K [Computational Radiology Laboratory, Harvard Medical School, Children' s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 (United States); Ourselin, Sebastien [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia)

2007-03-21

The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

Automatic temperature control method of shipping can

International Nuclear Information System (INIS)

Nishikawa, Kaoru.

1992-01-01

The present invention provides a method of rapidly and accurately controlling the temperature of a shipping can, which is used upon shipping inspection for a nuclear fuel assembly. That is, a measured temperature value of the shipping can is converted to a gas pressure setting value in a jacket of the shipping can by conducting a predetermined logic calculation by using a fuzzy logic. A gas pressure control section compares the pressure setting value of a fuzzy estimation section and the measured value of the gas pressure in the jacket of the shipping can, and conducts air supply or exhaustion of the jacket gas so as to adjust the measured value with the setting value. These fuzzy estimation section and gas pressure control section control the gas pressure in the jacket of the shipping can to control the water level in the jacket. As a result, the temperature of the shipping can is controlled. With such procedures, since the water level in the jacket can be controlled directly and finely, temperature of the shipping can is automatically controlled rapidly and accurately compared with a conventional case. (I.S.)

Target volume delineation for head and neck cancer intensity-modulated radiotherapy; Delineation des volumes cibles des cancers des voies aerodigestives superieures en radiotherapie conformationnelle avec modulation d'intensite

Energy Technology Data Exchange (ETDEWEB)

Lapeyre, M.; Toledano, I.; Bourry, N. [Departement de radiotherapie, centre Jean-Perrin, 58, rue Montalembert, BP 5026, 63011 Clermont-Ferrand cedex 1 (France); Bailly, C. [Unite de radiodiagnostic, centre Jean-Perrin, 58, rue Montalembert, BP 5026, 63011 Clermont-Ferrand cedex 1 (France); Cachin, F. [Unite de medecine nucleaire, centre Jean-Perrin, 58, rue Montalembert, BP 5026, 63011 Clermont-Ferrand cedex 1 (France)

2011-10-15

This article describes the determination and the delineation of the target volumes for head-and-neck cancers treated with intensity-modulated radiotherapy (IMRT). The delineation of the clinical target volumes (CTV) on the computerized tomography scanner (CT scan) requires a rigorous methodology due to the complexity of head-and-neck anatomy. The clinical examination with a sketch of pretreatment tumour extension, the surgical and pathological reports and the adequate images (CT scan, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography) are necessary for the delineation. The target volumes depend on the overall strategy: sequential IMRT or simultaneous integrated boost-IMRT (SIB-IMRT). The concept of selectivity of the potential subclinical disease near the primary tumor and the selection of neck nodal targets are described according to the recommendations and the literature. The planing target volume (PTV), mainly reflecting setup errors (random and systematic), results from a uniform 4-5 mm expansion around the CTV. We propose the successive delineation of: (1) the gross volume tumour (GTV); (2) the 'high risk' CTV1 around the GTV or including the postoperative tumour bed in case of positive margins or nodal extra-capsular spread (65-70 Gy in 30-35 fractions); (3) the CTV2 'intermediate risk' around the CTV1 for SIB-IMRT (59-63 Gy in 30-35 fractions); (4) the 'low-risk' CTV3 (54-56 Gy in 30-35 fractions); (5) the PTVs. (authors)

Automatic and manual segmentation of healthy retinas using high-definition optical coherence tomography.

Science.gov (United States)

Golbaz, Isabelle; Ahlers, Christian; Goesseringer, Nina; Stock, Geraldine; Geitzenauer, Wolfgang; Prünte, Christian; Schmidt-Erfurth, Ursula Margarethe

2011-03-01

This study compared automatic- and manual segmentation modalities in the retina of healthy eyes using high-definition optical coherence tomography (HD-OCT). Twenty retinas in 20 healthy individuals were examined using an HD-OCT system (Carl Zeiss Meditec, Inc.). Three-dimensional imaging was performed with an axial resolution of 6 μm at a maximum scanning speed of 25,000 A-scans/second. Volumes of 6 × 6 × 2 mm were scanned. Scans were analysed using a matlab-based algorithm and a manual segmentation software system (3D-Doctor). The volume values calculated by the two methods were compared. Statistical analysis revealed a high correlation between automatic and manual modes of segmentation. The automatic mode of measuring retinal volume and the corresponding three-dimensional images provided similar results to the manual segmentation procedure. Both methods were able to visualize retinal and subretinal features accurately. This study compared two methods of assessing retinal volume using HD-OCT scans in healthy retinas. Both methods were able to provide realistic volumetric data when applied to raster scan sets. Manual segmentation methods represent an adequate tool with which to control automated processes and to identify clinically relevant structures, whereas automatic procedures will be needed to obtain data in larger patient populations. © 2009 The Authors. Journal compilation © 2009 Acta Ophthalmol.

Microprocessor controlled system for automatic and semi-automatic syntheses of radiopharmaceuticals

International Nuclear Information System (INIS)

Ruth, T.J.; Adam, M.J.; Morris, D.; Jivan, S.

1986-01-01

A computer based system has been constructed to control the automatic synthesis of 2-deoxy-2-( 18 F)fluoro-D-glucose and is also being used in the development of an automatic synthesis of L-6-( 18 F)fluorodopa. (author)

Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices.

Science.gov (United States)

Jeong, Jenny; Frohberg, Nicholas J; Zhou, Enlu; Sulchek, Todd; Qiu, Peng

2018-01-01

Microfluidics are routinely used to study cellular properties, including the efficient quantification of single-cell biomechanics and label-free cell sorting based on the biomechanical properties, such as elasticity, viscosity, stiffness, and adhesion. Both quantification and sorting applications require optimal design of the microfluidic devices and mathematical modeling of the interactions between cells, fluid, and the channel of the device. As a first step toward building such a mathematical model, we collected video recordings of cells moving through a ridged microfluidic channel designed to compress and redirect cells according to cell biomechanics. We developed an efficient algorithm that automatically and accurately tracked the cell trajectories in the recordings. We tested the algorithm on recordings of cells with different stiffness, and showed the correlation between cell stiffness and the tracked trajectories. Moreover, the tracking algorithm successfully picked up subtle differences of cell motion when passing through consecutive ridges. The algorithm for accurately tracking cell trajectories paves the way for future efforts of modeling the flow, forces, and dynamics of cell properties in microfluidics applications.

Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices.

Directory of Open Access Journals (Sweden)

Jenny Jeong

Full Text Available Microfluidics are routinely used to study cellular properties, including the efficient quantification of single-cell biomechanics and label-free cell sorting based on the biomechanical properties, such as elasticity, viscosity, stiffness, and adhesion. Both quantification and sorting applications require optimal design of the microfluidic devices and mathematical modeling of the interactions between cells, fluid, and the channel of the device. As a first step toward building such a mathematical model, we collected video recordings of cells moving through a ridged microfluidic channel designed to compress and redirect cells according to cell biomechanics. We developed an efficient algorithm that automatically and accurately tracked the cell trajectories in the recordings. We tested the algorithm on recordings of cells with different stiffness, and showed the correlation between cell stiffness and the tracked trajectories. Moreover, the tracking algorithm successfully picked up subtle differences of cell motion when passing through consecutive ridges. The algorithm for accurately tracking cell trajectories paves the way for future efforts of modeling the flow, forces, and dynamics of cell properties in microfluidics applications.

Automatic Camera Control

DEFF Research Database (Denmark)

Burelli, Paolo; Preuss, Mike

2014-01-01

Automatically generating computer animations is a challenging and complex problem with applications in games and film production. In this paper, we investigate howto translate a shot list for a virtual scene into a series of virtual camera configurations — i.e automatically controlling the virtual...

Remote sensing application for delineating coastal vegetation - A case study

Digital Repository Service at National Institute of Oceanography (India)

Kunte, P.D.; Wagle, B.G.

Remote sensing data has been used for mapping coastal vegetation along the Goa Coast, India. The study envisages the use of digital image processing techniques for delineating geomorphic features and associated vegetation, including mangrove, along...

Delineating Biophysical Environments of the Sunda Banda Seascape, Indonesia

Directory of Open Access Journals (Sweden)

Mingshu Wang

2015-01-01

Full Text Available The Sunda Banda Seascape (SBS, located in the center of the Coral Triangle, is a global center of marine biodiversity and a conservation priority. We proposed the first biophysical environmental delineation of the SBS using globally available satellite remote sensing and model-assimilated data to categorize this area into unique and meaningful biophysical classes. Specifically, the SBS was partitioned into eight biophysical classes characterized by similar sea surface temperature, chlorophyll a concentration, currents, and salinity patterns. Areas within each class were expected to have similar habitat types and ecosystem functions. Our work supplemented prevailing global marine management schemes by focusing in on a regional scale with finer spatial resolution. It also provided a baseline for academic research, ecological assessments and will facilitate marine spatial planning and conservation activities in the area. In addition, the framework and methods of delineating biophysical environments we presented can be expanded throughout the whole Coral Triangle to support research and conservation activities in this important region.

Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches

Directory of Open Access Journals (Sweden)

Maggi Kelly

2013-08-01

Full Text Available Light detection and ranging (lidar data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA of a canopy height model (CHM. The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m2, discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r2: 0.93–0.96. Tree detection rates increased for more dominant trees (8–100 percent. The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.

Automatic registration using implicit shape representations: applications in intraoperative 3D rotational angiography to preoperative CTA registration

International Nuclear Information System (INIS)

Subramanian, Navneeth; Pichon, Eric; Solomon, Stephen B.

2009-01-01

A solution for automatic registration of 3D rotational angiography (XA) to CT/MR of the liver. Targeted for use in treatment planning of liver interventions. A shape-based approach to registration is proposed that does not require specification of landmarks nor is it prone to local minima like purely intensity-based registration methods. Through the use of vessel characteristics, accurate registration is possible even in the presence of deformations induced by catheters and respiratory motion. Registration was performed on eight pairs of multiphase CT angiography and 3D rotational digital angiography datasets. Quantitative validation of the registration accuracy using vessel landmarks was performed on these datasets. The validation study showed that the method has a registration error of 9.41±4.13 mm. In addition, the computation time is well below 60 s making it attractive for clinical application. A new method for fully automatic 3DXA to CT/MR image registration was developed and found to be efficient and accurate using clinically realistic datasets. (orig.)

Application of digital process controller for automatic pulse operation in the NSRR

International Nuclear Information System (INIS)

Ishijima, K.; Ueda, T.; Saigo, M.

1992-01-01

The NSRR at JAERI is a modified TRIGA Reactor. It was built for investigating reactor fuel behavior under reactivity initiated accident (RIA) conditions. Recently, there has been a need to improve the flexibility of pulsing operations in the NSRR to cover a wide range of accidental situations, including RIA events at elevated power levels, and various abnormal power transients. To satisfy this need, we developed a new reactor control system which allows us to perform 'Shaped Pulse Operation: SP' and 'Combined Pulse Operation: CP'. Quick, accurate and complicated manipulation of control rods was required to realize these operations. Therefore we installed a new reactor control system, which we call an automatic pulse control system. This control system is composed of digital processing controllers and other digital equipments, and is fully automated and highly accurate. (author)

Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

International Nuclear Information System (INIS)

Deeley, M A; Cmelak, A J; Malcolm, A W; Moretti, L; Jaboin, J; Niermann, K; Yang, Eddy S; Yu, David S; Ding, G X; Chen, A; Datteri, R; Noble, J H; Dawant, B M; Donnelly, E F; Yei, F; Koyama, T

2011-01-01

The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.

Automatic Discovery and Geotagging of Objects from Street View Imagery

Directory of Open Access Journals (Sweden)

Vladimir A. Krylov

2018-04-01

Full Text Available Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection and computation of the coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images, while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov random field model to estimate the objects’ geolocation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with the simultaneous presence of multiple, visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and position precision of approximately 2 m, which is approaching the precision of single-frequency GPS receivers.

Automatic yield-line analysis of slabs using discontinuity layout optimization.

Science.gov (United States)

Gilbert, Matthew; He, Linwei; Smith, Colin C; Le, Canh V

2014-08-08

The yield-line method of analysis is a long established and extremely effective means of estimating the maximum load sustainable by a slab or plate. However, although numerous attempts to automate the process of directly identifying the critical pattern of yield-lines have been made over the past few decades, to date none has proved capable of reliably analysing slabs of arbitrary geometry. Here, it is demonstrated that the discontinuity layout optimization (DLO) procedure can successfully be applied to such problems. The procedure involves discretization of the problem using nodes inter-connected by potential yield-line discontinuities, with the critical layout of these then identified using linear programming. The procedure is applied to various benchmark problems, demonstrating that highly accurate solutions can be obtained, and showing that DLO provides a truly systematic means of directly and reliably automatically identifying yield-line patterns. Finally, since the critical yield-line patterns for many problems are found to be quite complex in form, a means of automatically simplifying these is presented.

Automatic ultrasonic testing and the LOFT in-service inspection program

International Nuclear Information System (INIS)

Hunter, J.A.

1980-01-01

An automatic ultrasonic testing system has been developed which significantly improves the flaw indication detection and characterization capability over the capability of conventional volumetric examination techniques. The system utilizes an accurately located ultrasonic sensor to generate the examination data. A small computer performs and integrates control and data input/output functions. Computer software has been developed to provide a rigorous method for data analysis and ultrasonic image interpretation. The system has been used as part of an in-service inspection program to examine welds in thich austenitic stainless steel pipes in a small experimental nuclear reactor

Motor automaticity in Parkinson’s disease

Science.gov (United States)

Wu, Tao; Hallett, Mark; Chan, Piu

2017-01-01

Bradykinesia is the most important feature contributing to motor difficulties in Parkinson’s disease (PD). However, the pathophysiology underlying bradykinesia is not fully understood. One important aspect is that PD patients have difficulty in performing learned motor skills automatically, but this problem has been generally overlooked. Here we review motor automaticity associated motor deficits in PD, such as reduced arm swing, decreased stride length, freezing of gait, micrographia and reduced facial expression. Recent neuroimaging studies have revealed some neural mechanisms underlying impaired motor automaticity in PD, including less efficient neural coding of movement, failure to shift automated motor skills to the sensorimotor striatum, instability of the automatic mode within the striatum, and use of attentional control and/or compensatory efforts to execute movements usually performed automatically in healthy people. PD patients lose previously acquired automatic skills due to their impaired sensorimotor striatum, and have difficulty in acquiring new automatic skills or restoring lost motor skills. More investigations on the pathophysiology of motor automaticity, the effect of L-dopa or surgical treatments on automaticity, and the potential role of using measures of automaticity in early diagnosis of PD would be valuable. PMID:26102020

subsurface sequence delineation and saline water mapping of lagos

African Journals Online (AJOL)

A subsurface sequence delineation and saline water mapping of Lagos State was carried out. Ten (10) deep boreholes with average depth of 300 m were drilled within the sedimentary basin. The boreholes were lithologically and geophysically logged. The driller's lithological logs aided by gamma and resistivity logs, ...

High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately.

Science.gov (United States)

Chen, Wenjin; Wong, Chung; Vosburgh, Evan; Levine, Arnold J; Foran, David J; Xu, Eugenia Y

2014-07-08

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application - SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model

Delineation of peatland lagg boundaries from airborne LiDAR

Science.gov (United States)

Langlois, Melanie N.; Richardson, Murray C.; Price, Jonathan S.

2017-09-01

In Canada, peatlands are the most common type of wetland, but boundary delineation in peatland complexes has received little attention in the scientific literature. Typically, peatland boundaries are mapped as crisp, absolute features, and the transitional lagg zone—the ecotone found between a raised bog and the surrounding mineral land—is often overlooked. In this study, we aim (1) to advance existing approaches for detecting and locating laggs and lagg boundaries using airborne LiDAR surveys and (2) to describe the spatial distribution of laggs around raised bog peatlands. Two contrasting spatial analytical approaches for lagg detection were tested using five LiDAR-derived topographic and vegetation indices: topography, vegetation height, topographic wetness index, the standard deviation of the vegetation's height (as a proxy for the complexity of the vegetation's structure), and local indices of elevation variance. Using a dissimilarity approach (edge-detection, split-moving window analysis), no one variable accurately depicted both the lagg-mineral land and bog-lagg boundaries. Some indicators were better at predicting the bog-lagg boundary (i.e., vegetation height) and others at finding the lagg-mineral land boundary (i.e., topography). Dissimilarity analysis reinforces the usefulness of derived variables (e.g., wetness indices) in locating laggs, especially for those with weak topographic and vegetation gradients. When the lagg was confined between the bog and the adjacent upland, it took a linear form, parallel to the peatland's edge and was easier to predict. When the adjacent mineral land was flat or sloping away from the peatland, the lagg was discontinuous and intermittent and more difficult to predict.

PET functional volume delineation: a robustness and repeatability study

International Nuclear Information System (INIS)

Hatt, Mathieu; Cheze-le Rest, Catherine; Albarghach, Nidal; Pradier, Olivier; Visvikis, Dimitris

2011-01-01

Current state-of-the-art algorithms for functional uptake volume segmentation in PET imaging consist of threshold-based approaches, whose parameters often require specific optimization for a given scanner and associated reconstruction algorithms. Different advanced image segmentation approaches previously proposed and extensively validated, such as among others fuzzy C-means (FCM) clustering, or fuzzy locally adaptive bayesian (FLAB) algorithm have the potential to improve the robustness of functional uptake volume measurements. The objective of this study was to investigate robustness and repeatability with respect to various scanner models, reconstruction algorithms and acquisition conditions. Robustness was evaluated using a series of IEC phantom acquisitions carried out on different PET/CT scanners (Philips Gemini and Gemini Time-of-Flight, Siemens Biograph and GE Discovery LS) with their associated reconstruction algorithms (RAMLA, TF MLEM, OSEM). A range of acquisition parameters (contrast, duration) and reconstruction parameters (voxel size) were considered for each scanner model, and the repeatability of each method was evaluated on simulated and clinical tumours and compared to manual delineation. For all the scanner models, acquisition parameters and reconstruction algorithms considered, the FLAB algorithm demonstrated higher robustness in delineation of the spheres with low mean errors (10%) and variability (5%), with respect to threshold-based methodologies and FCM. The repeatability provided by all segmentation algorithms considered was very high with a negligible variability of <5% in comparison to that associated with manual delineation (5-35%). The use of advanced image segmentation algorithms may not only allow high accuracy as previously demonstrated, but also provide a robust and repeatable tool to aid physicians as an initial guess in determining functional volumes in PET. (orig.)

AUTOMATIC TAGGING OF PERSIAN WEB PAGES BASED ON N-GRAM LANGUAGE MODELS USING MAPREDUCE

Directory of Open Access Journals (Sweden)

Saeed Shahrivari

2015-07-01

Full Text Available Page tagging is one of the most important facilities for increasing the accuracy of information retrieval in the web. Tags are simple pieces of data that usually consist of one or several words, and briefly describe a page. Tags provide useful information about a page and can be used for boosting the accuracy of searching, document clustering, and result grouping. The most accurate solution to page tagging is using human experts. However, when the number of pages is large, humans cannot be used, and some automatic solutions should be used instead. We propose a solution called PerTag which can automatically tag a set of Persian web pages. PerTag is based on n-gram models and uses the tf-idf method plus some effective Persian language rules to select proper tags for each web page. Since our target is huge sets of web pages, PerTag is built on top of the MapReduce distributed computing framework. We used a set of more than 500 million Persian web pages during our experiments, and extracted tags for each page using a cluster of 40 machines. The experimental results show that PerTag is both fast and accurate

Delineation and segmentation of cerebral tumors by mapping blood-brain barrier disruption with dynamic contrast-enhanced CT and tracer kinetics modeling-a feasibility study

International Nuclear Information System (INIS)

Bisdas, S.; Vogl, T.J.; Yang, X.; Koh, T.S.; Lim, C.C.T.

2008-01-01

Dynamic contrast-enhanced (DCE) imaging is a promising approach for in vivo assessment of tissue microcirculation. Twenty patients with clinical and routine computed tomography (CT) evidence of intracerebral neoplasm were examined with DCE-CT imaging. Using a distributed-parameter model for tracer kinetics modeling of DCE-CT data, voxel-level maps of cerebral blood flow (F), intravascular blood volume (v i ) and intravascular mean transit time (t 1 ) were generated. Permeability-surface area product (PS), extravascular extracellular blood volume (v e ) and extraction ratio (E) maps were also calculated to reveal pathologic locations of tracer extravasation, which are indicative of disruptions in the blood-brain barrier (BBB). All maps were visually assessed for quality of tumor delineation and measurement of tumor extent by two radiologists. Kappa (κ) coefficients and their 95% confidence intervals (CI) were calculated to determine the interobserver agreement for each DCE-CT map. There was a substantial agreement for the tumor delineation quality in the F, v e and t 1 maps. The agreement for the quality of the tumor delineation was excellent for the v i , PS and E maps. Concerning the measurement of tumor extent, excellent and nearly excellent agreement was achieved only for E and PS maps, respectively. According to these results, we performed a segmentation of the cerebral tumors on the base of the E maps. The interobserver agreement for the tumor extent quantification based on manual segmentation of tumor in the E maps vs. the computer-assisted segmentation was excellent (κ = 0.96, CI: 0.93-0.99). The interobserver agreement for the tumor extent quantification based on computer segmentation in the mean images and the E maps was substantial (κ = 0.52, CI: 0.42-0.59). This study illustrates the diagnostic usefulness of parametric maps associated with BBB disruption on a physiology-based approach and highlights the feasibility for automatic segmentation of

Delineation of seismic source zones based on seismicity parameters ...

Indian Academy of Sciences (India)

In the present study, an attempt has been made to delineate seismic source zones in the study area (south India) based on the seismicity parameters. Seismicity parameters and the maximum probable earthquake for these source zones were evaluated and were used in the hazard evaluation. The probabilistic evaluation of ...

Automatic Photoelectric Telescope Service

International Nuclear Information System (INIS)

Genet, R.M.; Boyd, L.J.; Kissell, K.E.; Crawford, D.L.; Hall, D.S.; BDM Corp., McLean, VA; Kitt Peak National Observatory, Tucson, AZ; Dyer Observatory, Nashville, TN)

1987-01-01

Automatic observatories have the potential of gathering sizable amounts of high-quality astronomical data at low cost. The Automatic Photoelectric Telescope Service (APT Service) has realized this potential and is routinely making photometric observations of a large number of variable stars. However, without observers to provide on-site monitoring, it was necessary to incorporate special quality checks into the operation of the APT Service at its multiple automatic telescope installation on Mount Hopkins. 18 references

Automatic imitation: A meta-analysis.

Science.gov (United States)

Cracco, Emiel; Bardi, Lara; Desmet, Charlotte; Genschow, Oliver; Rigoni, Davide; De Coster, Lize; Radkova, Ina; Deschrijver, Eliane; Brass, Marcel

2018-05-01

Automatic imitation is the finding that movement execution is facilitated by compatible and impeded by incompatible observed movements. In the past 15 years, automatic imitation has been studied to understand the relation between perception and action in social interaction. Although research on this topic started in cognitive science, interest quickly spread to related disciplines such as social psychology, clinical psychology, and neuroscience. However, important theoretical questions have remained unanswered. Therefore, in the present meta-analysis, we evaluated seven key questions on automatic imitation. The results, based on 161 studies containing 226 experiments, revealed an overall effect size of g z = 0.95, 95% CI [0.88, 1.02]. Moderator analyses identified automatic imitation as a flexible, largely automatic process that is driven by movement and effector compatibility, but is also influenced by spatial compatibility. Automatic imitation was found to be stronger for forced choice tasks than for simple response tasks, for human agents than for nonhuman agents, and for goalless actions than for goal-directed actions. However, it was not modulated by more subtle factors such as animacy beliefs, motion profiles, or visual perspective. Finally, there was no evidence for a relation between automatic imitation and either empathy or autism. Among other things, these findings point toward actor-imitator similarity as a crucial modulator of automatic imitation and challenge the view that imitative tendencies are an indicator of social functioning. The current meta-analysis has important theoretical implications and sheds light on longstanding controversies in the literature on automatic imitation and related domains. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Automatic laser beam alignment using blob detection for an environment monitoring spectroscopy

Science.gov (United States)

Khidir, Jarjees; Chen, Youhua; Anderson, Gary

2013-05-01

This paper describes a fully automated system to align an infra-red laser beam with a small retro-reflector over a wide range of distances. The component development and test were especially used for an open-path spectrometer gas detection system. Using blob detection under OpenCV library, an automatic alignment algorithm was designed to achieve fast and accurate target detection in a complex background environment. Test results are presented to show that the proposed algorithm has been successfully applied to various target distances and environment conditions.

Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

Directory of Open Access Journals (Sweden)

Saurabh Jain

2015-01-01

Full Text Available The location and extent of white matter lesions on magnetic resonance imaging (MRI are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS. Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM and the appearance (hyperintense on FLAIR of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default

Neural dynamics of morphological processing in spoken word comprehension: Laterality and automaticity

Directory of Open Access Journals (Sweden)

Caroline M. Whiting

2013-11-01

Full Text Available Rapid and automatic processing of grammatical complexity is argued to take place during speech comprehension, engaging a left-lateralised fronto-temporal language network. Here we address how neural activity in these regions is modulated by the grammatical properties of spoken words. We used combined magneto- and electroencephalography (MEG, EEG to delineate the spatiotemporal patterns of activity that support the recognition of morphologically complex words in English with inflectional (-s and derivational (-er affixes (e.g. bakes, baker. The mismatch negativity (MMN, an index of linguistic memory traces elicited in a passive listening paradigm, was used to examine the neural dynamics elicited by morphologically complex words. Results revealed an initial peak 130-180 ms after the deviation point with a major source in left superior temporal cortex. The localisation of this early activation showed a sensitivity to two grammatical properties of the stimuli: 1 the presence of morphological complexity, with affixed words showing increased left-laterality compared to non-affixed words; and 2 the grammatical category, with affixed verbs showing greater left-lateralisation in inferior frontal gyrus compared to affixed nouns (bakes vs. beaks. This automatic brain response was additionally sensitive to semantic coherence (the meaning of the stem vs. the meaning of the whole form in fronto-temporal regions. These results demonstrate that the spatiotemporal pattern of neural activity in spoken word processing is modulated by the presence of morphological structure, predominantly engaging the left-hemisphere’s fronto-temporal language network, and does not require focused attention on the linguistic input.

Assessment of various strategies for 18F-FET PET-guided delineation of target volumes in high-grade glioma patients.

Science.gov (United States)

Vees, Hansjörg; Senthamizhchelvan, Srinivasan; Miralbell, Raymond; Weber, Damien C; Ratib, Osman; Zaidi, Habib

2009-02-01

The purpose of the study is to assess the contribution of (18)F-fluoro-ethyl-tyrosine ((18)F-FET) positron emission tomography (PET) in the delineation of gross tumor volume (GTV) in patients with high-grade gliomas compared with magnetic resonance imaging (MRI) alone. The study population consisted of 18 patients with high-grade gliomas. Seven image segmentation techniques were used to delineate (18)F-FET PET GTVs, and the results were compared to the manual MRI-derived GTV (GTV(MRI)). PET image segmentation techniques included manual delineation of contours (GTV(man)), a 2.5 standardized uptake value (SUV) cutoff (GTV(2.5)), a fixed threshold of 40% and 50% of the maximum signal intensity (GTV(40%) and GTV(50%)), signal-to-background ratio (SBR)-based adaptive thresholding (GTV(SBR)), gradient find (GTV(GF)), and region growing (GTV(RG)). Overlap analysis was also conducted to assess geographic mismatch between the GTVs delineated using the different techniques. Contours defined using GTV(2.5) failed to provide successful delineation technically in three patients (18% of cases) as SUV(max) segmentation algorithm is crucial, since it impacts both the volume and shape of the resulting GTV. The 2.5 SUV isocontour and GF segmentation techniques performed poorly and should not be used for GTV delineation. With adequate setting, the SBR-based PET technique may add considerably to conventional MRI-guided GTV delineation.

Automatic indexing, compiling and classification

International Nuclear Information System (INIS)

Andreewsky, Alexandre; Fluhr, Christian.

1975-06-01

A review of the principles of automatic indexing, is followed by a comparison and summing-up of work by the authors and by a Soviet staff from the Moscou INFORM-ELECTRO Institute. The mathematical and linguistic problems of the automatic building of thesaurus and automatic classification are examined [fr

FULLY AUTOMATED GENERATION OF ACCURATE DIGITAL SURFACE MODELS WITH SUB-METER RESOLUTION FROM SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

J. Wohlfeil

2012-07-01

Full Text Available Modern pixel-wise image matching algorithms like Semi-Global Matching (SGM are able to compute high resolution digital surface models from airborne and spaceborne stereo imagery. Although image matching itself can be performed automatically, there are prerequisites, like high geometric accuracy, which are essential for ensuring the high quality of resulting surface models. Especially for line cameras, these prerequisites currently require laborious manual interaction using standard tools, which is a growing problem due to continually increasing demand for such surface models. The tedious work includes partly or fully manual selection of tie- and/or ground control points for ensuring the required accuracy of the relative orientation of images for stereo matching. It also includes masking of large water areas that seriously reduce the quality of the results. Furthermore, a good estimate of the depth range is required, since accurate estimates can seriously reduce the processing time for stereo matching. In this paper an approach is presented that allows performing all these steps fully automated. It includes very robust and precise tie point selection, enabling the accurate calculation of the images’ relative orientation via bundle adjustment. It is also shown how water masking and elevation range estimation can be performed automatically on the base of freely available SRTM data. Extensive tests with a large number of different satellite images from QuickBird and WorldView are presented as proof of the robustness and reliability of the proposed method.

Improved automatic filtering methodology for an optimal pharmacokinetic modelling of DCE-MR images of the prostate

Energy Technology Data Exchange (ETDEWEB)

Vazquez Martinez, V.; Bosch Roig, I.; Sanz Requena, R.

2016-07-01

In Dynamic Contrast-Enhanced Magnetic Resonance (DCEMR) studies with high temporal resolution, images are quite noisy due to the complicate balance between temporal and spatial resolution. For this reason, the temporal curves extracted from the images present remarkable noise levels and, because of that, the pharmacokinetic parameters calculated by least squares fitting from the curves and the arterial phase (a useful marker in tumour diagnosis which appears in curves with high arterial contribution) are affected. In order to solve these limitations, an automatic filtering method was developed by our group. In this work, an advanced automatic filtering methodology is presented to further improve noise reduction of the temporal curves in order to obtain more accurate kinetic parameters and a proper modelling of the arterial phase. (Author)

An Integrative Approach to Accurate Vehicle Logo Detection

Directory of Open Access Journals (Sweden)

Hao Pan

2013-01-01

required for many applications in intelligent transportation systems and automatic surveillance. The task is challenging considering the small target of logos and the wide range of variability in shape, color, and illumination. A fast and reliable vehicle logo detection approach is proposed following visual attention mechanism from the human vision. Two prelogo detection steps, that is, vehicle region detection and a small RoI segmentation, rapidly focalize a small logo target. An enhanced Adaboost algorithm, together with two types of features of Haar and HOG, is proposed to detect vehicles. An RoI that covers logos is segmented based on our prior knowledge about the logos’ position relative to license plates, which can be accurately localized from frontal vehicle images. A two-stage cascade classier proceeds with the segmented RoI, using a hybrid of Gentle Adaboost and Support Vector Machine (SVM, resulting in precise logo positioning. Extensive experiments were conducted to verify the efficiency of the proposed scheme.

A generalized methodology for identification of threshold for HRU delineation in SWAT model

Science.gov (United States)

M, J.; Sudheer, K.; Chaubey, I.; Raj, C.

2016-12-01

The distributed hydrological model, Soil and Water Assessment Tool (SWAT) is a comprehensive hydrologic model widely used for making various decisions. The simulation accuracy of the distributed hydrological model differs due to the mechanism involved in the subdivision of the watershed. Soil and Water Assessment Tool (SWAT) considers sub-dividing the watershed and the sub-basins into small computing units known as 'hydrologic response units (HRU). The delineation of HRU is done based on unique combinations of land use, soil types, and slope within the sub-watersheds, which are not spatially defined. The computations in SWAT are done at HRU level and are then aggregated up to the sub-basin outlet, which is routed through the stream system. Generally, the HRUs are delineated by considering a threshold percentage of land use, soil and slope are to be given by the modeler to decrease the computation time of the model. The thresholds constrain the minimum area for constructing an HRU. In the current HRU delineation practice in SWAT, the land use, soil and slope of the watershed within a sub-basin, which is less than the predefined threshold, will be surpassed by the dominating land use, soil and slope, and introduce some level of ambiguity in the process simulations in terms of inappropriate representation of the area. But the loss of information due to variation in the threshold values depends highly on the purpose of the study. Therefore this research studies the effects of threshold values of HRU delineation on the hydrological modeling of SWAT on sediment simulations and suggests guidelines for selecting the appropriate threshold values considering the sediment simulation accuracy. The preliminary study was done on Illinois watershed by assigning different thresholds for land use and soil. A general methodology was proposed for identifying an appropriate threshold for HRU delineation in SWAT model that considered computational time and accuracy of the simulation

Learning-based automatic detection of severe coronary stenoses in CT angiographies

Science.gov (United States)

Melki, Imen; Cardon, Cyril; Gogin, Nicolas; Talbot, Hugues; Najman, Laurent

2014-03-01

3D cardiac computed tomography angiography (CCTA) is becoming a standard routine for non-invasive heart diseases diagnosis. Thanks to its high negative predictive value, CCTA is increasingly used to decide whether or not the patient should be considered for invasive angiography. However, an accurate assessment of cardiac lesions using this modality is still a time consuming task and needs a high degree of clinical expertise. Thus, providing automatic tool to assist clinicians during the diagnosis task is highly desirable. In this work, we propose a fully automatic approach for accurate severe cardiac stenoses detection. Our algorithm uses the Random Forest classi cation to detect stenotic areas. First, the classi er is trained on 18 CT cardiac exams with CTA reference standard. Then, then classi cation result is used to detect severe stenoses (with a narrowing degree higher than 50%) in a 30 cardiac CT exam database. Features that best captures the di erent stenoses con guration are extracted along the vessel centerlines at di erent scales. To ensure the accuracy against the vessel direction and scale changes, we extract features inside cylindrical patterns with variable directions and radii. Thus, we make sure that the ROIs contains only the vessel walls. The algorithm is evaluated using the Rotterdam Coronary Artery Stenoses Detection and Quantication Evaluation Framework. The evaluation is performed using reference standard quanti cations obtained from quantitative coronary angiography (QCA) and consensus reading of CTA. The obtained results show that we can reliably detect severe stenosis with a sensitivity of 64%.

Delineation of watershed in a mountainous area using different digital elevation modelsDelimitação de bacia hidrográfica em região montanhosa a partir de diferentes modelos digitais de elevação

Directory of Open Access Journals (Sweden)

Roberto Avelino Cecílio

2013-10-01

Full Text Available The precise delineation of watersheds is essential to studies related to environmental and hydrologic modeling. Such delineation is performed automatically in GIS softwares using algorithms that identify the watershed from grid representation of the terrain, the digital elevation model (DEM. This study evaluated the automatic delineation of a watershed located in the southern mountainous region of Espirito Santo (Brazil using six different DEMs. Three MDEs were obtained by radar images (SRTM and its refinements. Other three MDEs were obtained by process spatial interpolation of topographic data using different interpolators. It was found that the MDE obtained by the interpolation of topographic data using Top To Raster interpolator, (taking mapped hydrography as support promoted the best representation of the watershed topography for the purpose of its delimitation. A correta delimitação dos divisores de água da uma bacia hidrográfica é de grande importância para estudos ligados à modelagem hidrológica e ambiental. Tal procedimento é realizado de forma automática em aplicativos computacionais de Sistemas de Informaç��es Geográficas, por meio de algoritmos que identificam os divisores de águas a partir de uma representação matricial da topografia do terreno, denominada Modelo Digital de Elevação (MDE. O presente trabalho avaliou a delimitação automática de uma bacia hidrográfica situada em região montanhosa do Sul do Estado do Espírito Santo (Brasil feita a partir de seis diferentes MDEs: três MDEs originários de imagem de radar (SRTM e seus refinamentos, além de três MDEs originários de processos de interpolação espacial de curvas de nível por meio de diferentes formas de interpolação. Verificou-se que o MDE gerado a partir das curvas de nível e da hidrografia mapeada utilizando o interpolador Topo To Raster apresentou o melhor desempenho de representação do relevo da bacia para fins de delimitação da

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

Science.gov (United States)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse.

Science.gov (United States)

Hunter, James; Freer, Yvonne; Gatt, Albert; Reiter, Ehud; Sripada, Somayajulu; Sykes, Cindy

2012-11-01

Our objective was to determine whether and how a computer system could automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU). A system was developed which automatically generates partial NICU shift summaries (for the respiratory and cardiovascular systems), using data-to-text technology. It was evaluated for 2 months in the NICU at the Royal Infirmary of Edinburgh, under supervision. In an on-ward evaluation, a substantial majority of the summaries was found by outgoing and incoming nurses to be understandable (90%), and a majority was found to be accurate (70%), and helpful (59%). The evaluation also served to identify some outstanding issues, especially with regard to extra content the nurses wanted to see in the computer-generated summaries. It is technically possible automatically to generate limited natural language NICU shift summaries from an electronic patient record. However, it proved difficult to handle electronic data that was intended primarily for display to the medical staff, and considerable engineering effort would be required to create a deployable system from our proof-of-concept software. Copyright © 2012 Elsevier B.V. All rights reserved.

Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery.

Science.gov (United States)

Stow, Douglas A; Lippitt, Christopher D; Weeks, John R

2010-08-01

The objective was to test GEographic Object-based Image Analysis (GEOBIA) techniques for delineating neighborhoods of Accra, Ghana using QuickBird multispectral imagery. Two approaches to aggregating census enumeration areas (EAs) based on image-derived measures of vegetation objects were tested: (1) merging adjacent EAs according to vegetation measures and (2) image segmentation. Both approaches exploit readily available functions within commercial GEOBIA software. Image-derived neighborhood maps were compared to a reference map derived by spatial clustering of slum index values (from census data), to provide a relative assessment of potential map utility. A size-constrained iterative segmentation approach to aggregation was more successful than standard image segmentation or feature merge techniques. The segmentation approaches account for size and shape characteristics, enabling more realistic neighborhood boundaries to be delineated. The percentage of vegetation patches within each EA yielded more realistic delineation of potential neighborhoods than mean vegetation patch size per EA.

Automatic anterior chamber angle assessment for HD-OCT images.

Science.gov (United States)

Tian, Jing; Marziliano, Pina; Baskaran, Mani; Wong, Hong-Tym; Aung, Tin

2011-11-01

Angle-closure glaucoma is a major blinding eye disease and could be detected by measuring the anterior chamber angle in the human eyes. High-definition OCT (Cirrus HD-OCT) is an emerging noninvasive, high-speed, and high-resolution imaging modality for the anterior segment of the eye. Here, we propose a novel algorithm which automatically detects a new landmark, Schwalbe's line, and measures the anterior chamber angle in the HD-OCT images. The distortion caused by refraction is corrected by dewarping the HD-OCT images, and three biometric measurements are defined to quantitatively assess the anterior chamber angle. The proposed algorithm was tested on 40 HD-OCT images of the eye and provided accurate measurements in about 1 second.

delineating the jurassic to mid-cretaceous part of the pacific ...

African Journals Online (AJOL)

Mgina

paleomagnetic field. This problem was solved by the formulation of the seamount paleomagnetism technique (Talwani 1965,. Plouff 1976, Parker et al. 1987). Following this, Sager and Pringle (1988) using mainly seamount paleomagnetic data delineated a well defined Pacific ... earth's surface and abuts six major and.

Automatic determination of the size of elliptical nanoparticles from AFM images

International Nuclear Information System (INIS)

Sedlář, Jiří; Zitová, Barbara; Kopeček, Jaromír; Flusser, Jan; Todorciuc, Tatiana; Kratochvílová, Irena

2013-01-01

The objective of this work was to develop an accurate method for automatic determination of the size of elliptical nanoparticles from atomic force microscopy (AFM) images that would yield results consistent with results of manual measurements by experts. The proposed method was applied on phenylpyridyldiketopyrrolopyrrole (PPDP), a granular organic material with a wide scale of application and highly sensitive particle-size properties. A PPDP layer consists of similarly sized elliptical particles (c. 100 nm × 50 nm) and its properties can be estimated from the average length and width of the particles. The developed method is based on segmentation of salient particles by the watershed transform and approximation of their shapes by ellipses computed by image moments; it estimates the lengths and widths of the particles by the major and minor axes, respectively, of the corresponding ellipses. Its results proved to be consistent with results of manual measurements by a trained expert. The comparison showed that the developed method could be used in practice for precise automatic measurement of PPDP particles in AFM images

A fully automatic tool to perform accurate flood mapping by merging remote sensing imagery and ancillary data

Science.gov (United States)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco; Pasquariello, Guido

2016-04-01

Flooding is one of the most frequent and expansive natural hazard. High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas. Remote sensing data are recognized to be useful in this respect, thanks to the high resolution and regular revisit schedules of state-of-the-art satellites, moreover offering a synoptic overview of the extent of flooding. In particular, Synthetic Aperture Radar (SAR) data present several favorable characteristics for flood mapping, such as their relative insensitivity to the meteorological conditions during acquisitions, as well as the possibility of acquiring independently of solar illumination, thanks to the active nature of the radar sensors [1]. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground: the presence of many land cover types, each one with a particular signature in presence of flood, requires modelling the behavior of different objects in the scene in order to associate them to flood or no flood conditions [2]. Generally, the fusion of multi-temporal, multi-sensor, multi-resolution and/or multi-platform Earth observation image data, together with other ancillary information, seems to have a key role in the pursuit of a consistent interpretation of complex scenes. In the case of flooding, distance from the river, terrain elevation, hydrologic information or some combination thereof can add useful information to remote sensing data. Suitable methods, able to manage and merge different kind of data, are so particularly needed. In this work, a fully automatic tool, based on Bayesian Networks (BNs) [3] and able to perform data fusion, is presented. It supplies flood maps

An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype prediction in HIV-1.

Directory of Open Access Journals (Sweden)

Sergei L Kosakovsky Pond

2009-11-01

Full Text Available Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1 are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5% fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance

Determination of boron as boric acid by automatic potentiometric titration

International Nuclear Information System (INIS)

Midgley, D.

1988-06-01

Boron in PWR primary coolant and related waters may be determined as boric acid by titration with sodium hydroxide, using a glass electrode as a pH indicator. With a modern automatic titrator, the analysis is quick, convenient, accurate and precise. In the titration of 8 mg B (e.g. 4 ml of 2000 mg 1 -1 solution), no significant bias was observed and relative standard deviations were about 0.25%. With 0.8 g B, a bias of about 2% appears, although this could be reduced by restandardizing the titrant, but the relative standard deviation was still -1 B, depending on the stage of the fuel cycle. (author)

Automatic first-arrival picking based on extended super-virtual interferometry with quality control procedure

Science.gov (United States)

An, Shengpei; Hu, Tianyue; Liu, Yimou; Peng, Gengxin; Liang, Xianghao

2017-12-01

Static correction is a crucial step of seismic data processing for onshore play, which frequently has a complex near-surface condition. The effectiveness of the static correction depends on an accurate determination of first-arrival traveltimes. However, it is difficult to accurately auto-pick the first arrivals for data with low signal-to-noise ratios (SNR), especially for those measured in the area of the complex near-surface. The technique of the super-virtual interferometry (SVI) has the potential to enhance the SNR of first arrivals. In this paper, we develop the extended SVI with (1) the application of the reverse correlation to improve the capability of SNR enhancement at near-offset, and (2) the usage of the multi-domain method to partially overcome the limitation of current method, given insufficient available source-receiver combinations. Compared to the standard SVI, the SNR enhancement of the extended SVI can be up to 40%. In addition, we propose a quality control procedure, which is based on the statistical characteristics of multichannel recordings of first arrivals. It can auto-correct the mispicks, which might be spurious events generated by the SVI. This procedure is very robust, highly automatic and it can accommodate large data in batches. Finally, we develop one automatic first-arrival picking method to combine the extended SVI and the quality control procedure. Both the synthetic and the field data examples demonstrate that the proposed method is able to accurately auto-pick first arrivals in seismic traces with low SNR. The quality of the stacked seismic sections obtained from this method is much better than those obtained from an auto-picking method, which is commonly employed by the commercial software.

Automatic sets and Delone sets

International Nuclear Information System (INIS)

Barbe, A; Haeseler, F von

2004-01-01

Automatic sets D part of Z m are characterized by having a finite number of decimations. They are equivalently generated by fixed points of certain substitution systems, or by certain finite automata. As examples, two-dimensional versions of the Thue-Morse, Baum-Sweet, Rudin-Shapiro and paperfolding sequences are presented. We give a necessary and sufficient condition for an automatic set D part of Z m to be a Delone set in R m . The result is then extended to automatic sets that are defined as fixed points of certain substitutions. The morphology of automatic sets is discussed by means of examples

Clinical target volume delineation including elective nodal irradiation in preoperative and definitive radiotherapy of pancreatic cancer

Directory of Open Access Journals (Sweden)

Caravatta Luciana

2012-06-01

Full Text Available Abstract Background Radiotherapy (RT is widely used in the treatment of pancreatic cancer. Currently, recommendation has been given for the delineation of the clinical target volume (CTV in adjuvant RT. Based on recently reviewed pathologic data, the aim of this study is to propose criteria for the CTV definition and delineation including elective nodal irradiation (ENI in the preoperative and definitive treatment of pancreatic cancer. Methods The anatomical structures of interest, as well as the abdominal vasculature were identified on intravenous contrast-enhanced CT scans of two different patients with pancreatic cancer of the head and the body. To delineate the lymph node area, a margin of 10 mm was added to the arteries. Results We proposed a set of guidelines for elective treatment of high-risk nodal areas and CTV delineation. Reference CT images were provided. Conclusions The proposed guidelines could be used for preoperative or definitive RT for carcinoma of the head and body of the pancreas. Further clinical investigations are needed to validate the defined CTVs.

The impact of positron emission tomography on primary tumour delineation and dosimetric outcome in intensity modulated radiotherapy of early T-stage nasopharyngeal carcinoma.

Science.gov (United States)

Wu, Vincent W C; Leung, Wan-Shun; Wong, Kwun-Lam; Chan, Ying-Kit; Law, Wing-Lam; Leung, Wing-Kwan; Yu, Yat-Long

2016-08-24

In intensity modulated radiotherapy (IMRT) of nasopharyngeal carcinoma (NPC), accurate delineation of the gross tumour volume (GTV) is important. Image registration of CT and MRI has been routinely used in treatment planning. With recent development of positron emission tomography (PET), the aims of this study were to evaluate the impact of PET on GTV delineation and dosimetric outcome in IMRT of early stage NPC patients. Twenty NPC patients with T1 or T2 disease treated by IMRT were recruited. For each patient, 2 sets of NP GTVs were delineated separately, in which one set was performed using CT and MRI registration only (GTVCM), while the other set was carried out using PET, CT and MRI information (GTVCMP). A 9-field IMRT plan was computed based on the target volumes generated from CT and MRI (PTVCM). To assess the geometric difference between the GTVCM and GTVCMP, GTV volumes and DICE similarity coefficient (DSC), which measured the geometrical similarity between the two GTVs, were recorded. To evaluate the dosimetric impact, the Dmax, Dmin, Dmean and D95 of PTVs were obtained from their dose volume histograms generated by the treatment planning system. The overall mean volume of GTVCMP was greater than GTVCM by 4.4 %, in which GTVCMP was slightly greater in the T1 group but lower in the T2 group. The mean DSC of the whole group was 0.79 ± 0.05. Similar mean DSC values were also obtained from the T1 and T2 groups separately. The dosimetric parameters of PTVCM fulfilled the planning requirements. When applying this plan to the PTVCMP, the average Dmin (56.9 Gy) and D95 (68.6 Gy) of PTVCMP failed to meet the dose requirements and demonstrated significant differences from the PTVCM (p = 0.001 and 0.016 respectively), whereas the doses to GTVCMP did not show significant difference with the GTVCM. In IMRT of early stage NPC, PET was an important imaging modality in radiotherapy planning so as to avoid underdosing the PTV, although its effect on GTV

Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

Directory of Open Access Journals (Sweden)

Yehu Shen

2014-01-01

Full Text Available Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.

Automatic Sleep Scoring in Normals and in Individuals with Neurodegenerative Disorders According to New International Sleep Scoring Criteria

DEFF Research Database (Denmark)

Jensen, Peter S.; Sørensen, Helge Bjarup Dissing; Leonthin, Helle

2010-01-01

The aim of this study was to develop a fully automatic sleep scoring algorithm on the basis of a reproduction of new international sleep scoring criteria from the American Academy of Sleep Medicine. A biomedical signal processing algorithm was developed, allowing for automatic sleep depth....... Based on an observed reliability of the manual scorer of 92.5% (Cohen's Kappa: 0.87) in the normal group and 85.3% (Cohen's Kappa: 0.73) in the abnormal group, this study concluded that although the developed algorithm was capable of scoring normal sleep with an accuracy around the manual interscorer...... reliability, it failed in accurately scoring abnormal sleep as encountered for the Parkinson disease/multiple system atrophy patients....

Automatic sleep scoring in normals and in individuals with neurodegenerative disorders according to new international sleep scoring criteria

DEFF Research Database (Denmark)

Jensen, Peter S; Sorensen, Helge B D; Jennum, Poul

2010-01-01

The aim of this study was to develop a fully automatic sleep scoring algorithm on the basis of a reproduction of new international sleep scoring criteria from the American Academy of Sleep Medicine. A biomedical signal processing algorithm was developed, allowing for automatic sleep depth....... Based on an observed reliability of the manual scorer of 92.5% (Cohen's Kappa: 0.87) in the normal group and 85.3% (Cohen's Kappa: 0.73) in the abnormal group, this study concluded that although the developed algorithm was capable of scoring normal sleep with an accuracy around the manual interscorer...... reliability, it failed in accurately scoring abnormal sleep as encountered for the Parkinson disease/multiple system atrophy patients....

Machine learning of parameters for accurate semiempirical quantum chemical calculations

International Nuclear Information System (INIS)

Dral, Pavlo O.; Lilienfeld, O. Anatole von; Thiel, Walter

2015-01-01

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C 7 H 10 O 2 , for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules

Looking at the future of manufacturing metrology: roadmap document of the German VDI/VDE Society for Measurement and Automatic Control

Directory of Open Access Journals (Sweden)

J. Berthold

2013-02-01

Full Text Available "Faster, safer, more accurately and more flexibly'' is the title of the "manufacturing metrology roadmap'' issued by the VDI/VDE Society for Measurement and Automatic Control (http://www.vdi.de/gma. The document presents a view of the development of metrology for industrial production over the next ten years and was drawn up by a German group of experts from research and industry. The following paper summarizes the content of the roadmap and explains the individual concepts of "Faster, safer, more accurately and more flexibly'' with the aid of examples.

Automatically measuring brain ventricular volume within PACS using artificial intelligence.

Science.gov (United States)

Yepes-Calderon, Fernando; Nelson, Marvin D; McComb, J Gordon

2018-01-01

The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system's daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

Directory of Open Access Journals (Sweden)

Wei Chen

2018-01-01

Full Text Available Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

Automatic Transformation of MPI Programs to Asynchronous, Graph-Driven Form

Energy Technology Data Exchange (ETDEWEB)

Baden, Scott B [University of California, San Diego; Weare, John H [University of California, San Diego; Bylaska, Eric J [Pacific Northwest National Laboratory

2013-04-30

The goals of this project are to develop new, scalable, high-fidelity algorithms for atomic-level simulations and program transformations that automatically restructure existing applications, enabling them to scale forward to Petascale systems and beyond. The techniques enable legacy MPI application code to exploit greater parallelism though increased latency hiding and improved workload assignment. The techniques were successfully demonstrated on high-end scalable systems located at DOE laboratories. Besides the automatic MPI program transformations efforts, the project also developed several new scalable algorithms for ab-initio molecular dynamics, including new massively parallel algorithms for hybrid DFT and new parallel in time algorithms for molecular dynamics and ab-initio molecular dynamics. These algorithms were shown to scale to very large number of cores, and they were designed to work in the latency hiding framework developed in this project. The effectiveness of the developments was enhanced by the direct application to real grand challenge simulation problems covering a wide range of technologically important applications, time scales and accuracies. These included the simulation of the electronic structure of mineral/fluid interfaces, the very accurate simulation of chemical reactions in microsolvated environments, and the simulation of chemical behavior in very large enzyme reactions.

Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

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Zhao, Liming; Zhang, Yi; Xu, Xiaodong; Xiao, Hong; Huang, Chao

2016-09-01

To provide an accurate surface defects inspection method and make the automation of robust image region of interests(ROI) delineation strategy a reality in production line, a multi-source CCD imaging based fuzzy-rough sets method is proposed for hot slab surface quality assessment. The applicability of the presented method and the devised system are mainly tied to the surface quality inspection for strip, billet and slab surface etcetera. In this work we take into account the complementary advantages in two common machine vision (MV) systems(line array CCD traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging)), and through establishing the model of fuzzy-rough sets in the detection system the seeds for relative fuzzy connectedness(RFC) delineation for ROI can placed adaptively, which introduces the upper and lower approximation sets for RIO definition, and by which the boundary region can be delineated by RFC region competitive classification mechanism. For the first time, a Multi-source CCD imaging based fuzzy-rough sets strategy is attempted for CC-slab surface defects inspection that allows an automatic way of AI algorithms and powerful ROI delineation strategies to be applied to the MV inspection field.

11C-methionine PET improves the target volume delineation of meningiomas treated with stereotactic fractionated radiotherapy

International Nuclear Information System (INIS)

Grosu, Anca-Ligia; Weber, Wolfgang A.; Astner, Sabrina T.; Adam, Markus; Krause, Bernd J.; Schwaiger, Markus; Molls, Michael; Nieder, Carsten

2006-01-01

Purpose: To evaluate the role of 11 C-methionine positron emission tomography (MET-PET) in target volume delineation for meningiomas and to determine the interobserver variability. Methods and Materials: Two independent observers performed treatment planning in 10 patients according to a prospective written protocol. In the first step, they used coregistered computed tomography (CT) and magnetic resonance imaging (MRI). In the second step, MET-PET was added to CT/MRI (image fusion based on mutual information). Results: The correlation between gross tumor volume (GTVs) delineated by the two observers based on CT/MRI was r = 0.855 (Spearman's correlation coefficient, p = 0.002) and r = 0.988 (p = 0.000) when MET-PET/CT/MRI were used. The number of patients with agreement in more then 80% of the outlined volume increased with the availability of MET-PET from 1 in 10 to 5 in 10. The median volume of intersection between the regions delineated by two observers increased significantly from 69% (from the composite volume) to 79%, by the addition of MET-PET (p = 0.005). The information of MET-PET was useful to delineate GTV in the area of cavernous sinus, orbit, and base of the skull. Conclusions: The hypothesis-generating findings of potential normal tissue sparing and reduced interobserver variability provide arguments for invasive studies of the correlation between MET-PET images and histologic tumor extension and for prospective trials of target volume delineation with CT/MRI/MET-PET image fusion

Automatisms: bridging clinical neurology with criminal law.

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Rolnick, Joshua; Parvizi, Josef

2011-03-01

The law, like neurology, grapples with the relationship between disease states and behavior. Sometimes, the two disciplines share the same terminology, such as automatism. In law, the "automatism defense" is a claim that action was involuntary or performed while unconscious. Someone charged with a serious crime can acknowledge committing the act and yet may go free if, relying on the expert testimony of clinicians, the court determines that the act of crime was committed in a state of automatism. In this review, we explore the relationship between the use of automatism in the legal and clinical literature. We close by addressing several issues raised by the automatism defense: semantic ambiguity surrounding the term automatism, the presence or absence of consciousness during automatisms, and the methodological obstacles that have hindered the study of cognition during automatisms. Copyright © 2010 Elsevier Inc. All rights reserved.

Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

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Link, Daphna; Braginsky, Michael B; Joskowicz, Leo; Ben Sira, Liat; Harel, Shaul; Many, Ariel; Tarrasch, Ricardo; Malinger, Gustavo; Artzi, Moran; Kapoor, Cassandra; Miller, Elka; Ben Bashat, Dafna

2018-01-01

Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). The developed method showed high correlation with manual segmentation (r2 = 0.9183, p user independent, applicable with retrospective data, and is suggested for use in routine clinical practice. © 2017 S. Karger AG, Basel.

Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

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Xiao, Zhitao; Zhang, Xinpeng; Geng, Lei; Zhang, Fang; Wu, Jun; Tong, Jun; Ogunbona, Philip O; Shan, Chunyan

2017-10-26

Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

18F-fluorocholine PET-guided target volume delineation techniques for partial prostate re-irradiation in local recurrent prostate cancer

International Nuclear Information System (INIS)

Wang Hui; Vees, Hansjoerg; Miralbell, Raymond; Wissmeyer, Michael; Steiner, Charles; Ratib, Osman; Senthamizhchelvan, Srinivasan; Zaidi, Habib

2009-01-01

Background and purpose: We evaluate the contribution of 18 F-choline PET/CT in the delineation of gross tumour volume (GTV) in local recurrent prostate cancer after initial irradiation using various PET image segmentation techniques. Materials and methods: Seventeen patients with local-only recurrent prostate cancer (median = 5.7 years) after initial irradiation were included in the study. Rebiopsies were performed in 10 patients that confirmed the local recurrence. Following injection of 300 MBq of 18 F-fluorocholine, dynamic PET frames (3 min each) were reconstructed from the list-mode acquisition. Five PET image segmentation techniques were used to delineate the 18 F-choline-based GTVs. These included manual delineation of contours (GTV man ) by two teams consisting of a radiation oncologist and a nuclear medicine physician each, a fixed threshold of 40% and 50% of the maximum signal intensity (GTV 40% and GTV 50% ), signal-to-background ratio-based adaptive thresholding (GTV SBR ), and a region growing (GTV RG ) algorithm. Geographic mismatches between the GTVs were also assessed using overlap analysis. Results: Inter-observer variability for manual delineation of GTVs was high but not statistically significant (p = 0.459). In addition, the volumes and shapes of GTVs delineated using semi-automated techniques were significantly higher than those of GTVs defined manually. Conclusions: Semi-automated segmentation techniques for 18 F-choline PET-guided GTV delineation resulted in substantially higher GTVs compared to manual delineation and might replace the latter for determination of recurrent prostate cancer for partial prostate re-irradiation. The selection of the most appropriate segmentation algorithm still needs to be determined.

18F-fluorocholine PET-guided target volume delineation techniques for partial prostate re-irradiation in local recurrent prostate cancer.

Science.gov (United States)

Wang, Hui; Vees, Hansjörg; Miralbell, Raymond; Wissmeyer, Michael; Steiner, Charles; Ratib, Osman; Senthamizhchelvan, Srinivasan; Zaidi, Habib

2009-11-01

We evaluate the contribution of (18)F-choline PET/CT in the delineation of gross tumour volume (GTV) in local recurrent prostate cancer after initial irradiation using various PET image segmentation techniques. Seventeen patients with local-only recurrent prostate cancer (median=5.7 years) after initial irradiation were included in the study. Rebiopsies were performed in 10 patients that confirmed the local recurrence. Following injection of 300 MBq of (18)F-fluorocholine, dynamic PET frames (3 min each) were reconstructed from the list-mode acquisition. Five PET image segmentation techniques were used to delineate the (18)F-choline-based GTVs. These included manual delineation of contours (GTV(man)) by two teams consisting of a radiation oncologist and a nuclear medicine physician each, a fixed threshold of 40% and 50% of the maximum signal intensity (GTV(40%) and GTV(50%)), signal-to-background ratio-based adaptive thresholding (GTV(SBR)), and a region growing (GTV(RG)) algorithm. Geographic mismatches between the GTVs were also assessed using overlap analysis. Inter-observer variability for manual delineation of GTVs was high but not statistically significant (p=0.459). In addition, the volumes and shapes of GTVs delineated using semi-automated techniques were significantly higher than those of GTVs defined manually. Semi-automated segmentation techniques for (18)F-choline PET-guided GTV delineation resulted in substantially higher GTVs compared to manual delineation and might replace the latter for determination of recurrent prostate cancer for partial prostate re-irradiation. The selection of the most appropriate segmentation algorithm still needs to be determined.

Automatic anatomy recognition on CT images with pathology

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Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Vessel based delineation guidelines for the elective lymph node regions in breast cancer radiation therapy – PROCAB guidelines

International Nuclear Information System (INIS)

Verhoeven, Karolien; Weltens, Caroline; Remouchamps, Vincent; Mahjoubi, Khalil; Veldeman, Liv; Lengele, Benoit; Hortobagyi, Eszter; Kirkove, Carine

2015-01-01

Objective: A national project to improve the quality of breast radiation therapy was started, named PROCAB (PROject on CAncer of the Breast). One of the objectives was to reach a national consensus guideline for the delineation of the regional lymph node areas in breast radiation therapy. Methods: The realization of the new guidelines was a step by step process that started with multiple expert meetings where the existing guidelines were analyzed and the delineations of the lymph node regions were performed together with a surgeon, specialized in the anatomy of the drainage of the breast. Results: The delineation guidelines are vessel-based. Since the occurrence of pathological lymph nodes is typically around the veins, the cranial and caudal borders of all different nodal regions are based on a 5 mm margin around the veins, except for the parasternal lymph node area. Compared to the existing guidelines there are some major changes. Conclusion: With this project a national as well as a European (ESTRO) consensus guideline for the delineation of the regional lymph node areas in breast RT is reached. The new delineation atlas is vessel-based and no longer field-based

Automatic classification of time-variable X-ray sources

Energy Technology Data Exchange (ETDEWEB)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M. [Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006 (Australia)

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

Automatic classification of time-variable X-ray sources

International Nuclear Information System (INIS)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

2014-01-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

Automatic cloud coverage assessment of Formosat-2 image

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Hsu, Kuo-Hsien

2011-11-01

Formosat-2 satellite equips with the high-spatial-resolution (2m ground sampling distance) remote sensing instrument. It has been being operated on the daily-revisiting mission orbit by National Space organization (NSPO) of Taiwan since May 21 2004. NSPO has also serving as one of the ground receiving stations for daily processing the received Formosat- 2 images. The current cloud coverage assessment of Formosat-2 image for NSPO Image Processing System generally consists of two major steps. Firstly, an un-supervised K-means method is used for automatically estimating the cloud statistic of Formosat-2 image. Secondly, manual estimation of cloud coverage from Formosat-2 image is processed by manual examination. Apparently, a more accurate Automatic Cloud Coverage Assessment (ACCA) method certainly increases the efficiency of processing step 2 with a good prediction of cloud statistic. In this paper, mainly based on the research results from Chang et al, Irish, and Gotoh, we propose a modified Formosat-2 ACCA method which considered pre-processing and post-processing analysis. For pre-processing analysis, cloud statistic is determined by using un-supervised K-means classification, Sobel's method, Otsu's method, non-cloudy pixels reexamination, and cross-band filter method. Box-Counting fractal method is considered as a post-processing tool to double check the results of pre-processing analysis for increasing the efficiency of manual examination.

Reliable clarity automatic-evaluation method for optical remote sensing images

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Qin, Bangyong; Shang, Ren; Li, Shengyang; Hei, Baoqin; Liu, Zhiwen

2015-10-01

Image clarity, which reflects the sharpness degree at the edge of objects in images, is an important quality evaluate index for optical remote sensing images. Scholars at home and abroad have done a lot of work on estimation of image clarity. At present, common clarity-estimation methods for digital images mainly include frequency-domain function methods, statistical parametric methods, gradient function methods and edge acutance methods. Frequency-domain function method is an accurate clarity-measure approach. However, its calculation process is complicate and cannot be carried out automatically. Statistical parametric methods and gradient function methods are both sensitive to clarity of images, while their results are easy to be affected by the complex degree of images. Edge acutance method is an effective approach for clarity estimate, while it needs picking out the edges manually. Due to the limits in accuracy, consistent or automation, these existing methods are not applicable to quality evaluation of optical remote sensing images. In this article, a new clarity-evaluation method, which is based on the principle of edge acutance algorithm, is proposed. In the new method, edge detection algorithm and gradient search algorithm are adopted to automatically search the object edges in images. Moreover, The calculation algorithm for edge sharpness has been improved. The new method has been tested with several groups of optical remote sensing images. Compared with the existing automatic evaluation methods, the new method perform better both in accuracy and consistency. Thus, the new method is an effective clarity evaluation method for optical remote sensing images.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

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Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

A multi-institution evaluation of deformable image registration algorithms for automatic organ delineation in adaptive head and neck radiotherapy

International Nuclear Information System (INIS)

Hardcastle, Nicholas; Kumar, Prashant; Oechsner, Markus; Richter, Anne; Song, Shiyu; Myers, Michael; Polat, Bülent; Bzdusek, Karl; Tomé, Wolfgang A; Cannon, Donald M; Brouwer, Charlotte L; Wittendorp, Paul WH; Dogan, Nesrin; Guckenberger, Matthias; Allaire, Stéphane; Mallya, Yogish

2012-01-01

Adaptive Radiotherapy aims to identify anatomical deviations during a radiotherapy course and modify the treatment plan to maintain treatment objectives. This requires regions of interest (ROIs) to be defined using the most recent imaging data. This study investigates the clinical utility of using deformable image registration (DIR) to automatically propagate ROIs. Target (GTV) and organ-at-risk (OAR) ROIs were non-rigidly propagated from a planning CT scan to a per-treatment CT scan for 22 patients. Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs. The propagated ROIs were qualitatively examined by experts and scored based on their clinical utility. Good agreement between the DIR-propagated ROIs and expert-drawn ROIs was observed based on the metrics used. 94% of all ROIs generated using DIR were scored as being clinically useful, requiring minimal or no edits. However, 27% (12/44) of the GTVs required major edits. DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs. It is recommended that propagated target structures be thoroughly reviewed by the treating physician

A fast fiducial marker tracking model for fully automatic alignment in electron tomography

KAUST Repository

Han, Renmin; Zhang, Fa; Gao, Xin

2017-01-01

Automatic alignment, especially fiducial marker-based alignment, has become increasingly important due to the high demand of subtomogram averaging and the rapid development of large-field electron microscopy. Among the alignment steps, fiducial marker tracking is a crucial one that determines the quality of the final alignment. Yet, it is still a challenging problem to track the fiducial markers accurately and effectively in a fully automatic manner.In this paper, we propose a robust and efficient scheme for fiducial marker tracking. Firstly, we theoretically prove the upper bound of the transformation deviation of aligning the positions of fiducial markers on two micrographs by affine transformation. Secondly, we design an automatic algorithm based on the Gaussian mixture model to accelerate the procedure of fiducial marker tracking. Thirdly, we propose a divide-and-conquer strategy against lens distortions to ensure the reliability of our scheme. To our knowledge, this is the first attempt that theoretically relates the projection model with the tracking model. The real-world experimental results further support our theoretical bound and demonstrate the effectiveness of our algorithm. This work facilitates the fully automatic tracking for datasets with a massive number of fiducial markers.The C/C ++ source code that implements the fast fiducial marker tracking is available at https://github.com/icthrm/gmm-marker-tracking. Markerauto 1.6 version or later (also integrated in the AuTom platform at http://ear.ict.ac.cn/) offers a complete implementation for fast alignment, in which fast fiducial marker tracking is available by the

A fast fiducial marker tracking model for fully automatic alignment in electron tomography

KAUST Repository

Han, Renmin

2017-10-20

Automatic alignment, especially fiducial marker-based alignment, has become increasingly important due to the high demand of subtomogram averaging and the rapid development of large-field electron microscopy. Among the alignment steps, fiducial marker tracking is a crucial one that determines the quality of the final alignment. Yet, it is still a challenging problem to track the fiducial markers accurately and effectively in a fully automatic manner.In this paper, we propose a robust and efficient scheme for fiducial marker tracking. Firstly, we theoretically prove the upper bound of the transformation deviation of aligning the positions of fiducial markers on two micrographs by affine transformation. Secondly, we design an automatic algorithm based on the Gaussian mixture model to accelerate the procedure of fiducial marker tracking. Thirdly, we propose a divide-and-conquer strategy against lens distortions to ensure the reliability of our scheme. To our knowledge, this is the first attempt that theoretically relates the projection model with the tracking model. The real-world experimental results further support our theoretical bound and demonstrate the effectiveness of our algorithm. This work facilitates the fully automatic tracking for datasets with a massive number of fiducial markers.The C/C ++ source code that implements the fast fiducial marker tracking is available at https://github.com/icthrm/gmm-marker-tracking. Markerauto 1.6 version or later (also integrated in the AuTom platform at http://ear.ict.ac.cn/) offers a complete implementation for fast alignment, in which fast fiducial marker tracking is available by the

MIDAS robust trend estimator for accurate GPS station velocities without step detection

Science.gov (United States)

Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C.; Gazeaux, Julien

2016-03-01

Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj-xi)/(tj-ti) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.

Crustal Strain Observation Using a Two-Color Interferometer with Accurate Correction of Refractive Index of Air

Directory of Open Access Journals (Sweden)

Souichi Telada

2014-07-01

Full Text Available A highly accurate two-color interferometer with automatic correction of the refractive index of air was developed for crustal strain observation. The two-color interferometer, which can measure a geometrical distance of approximately 70 m, with a relative resolution of 2 × 10−9, clearly detected a change in strain due to earth tides in spite of optical measurement in air. Moreover, a large strain quake due to an earthquake could be observed without disturbing the measurement. We demonstrated the advantages of the two-color interferometer in air for geodetic observation.

The Regionalization of Africa: Delineating Africa's Subregions Using Airline Data

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Good, Pieter R.; Derudder, Ben; Witlox, Frank J.

2011-01-01

Current regionalizations of Africa have limitations in that they are attribute-based and regions are delineated according to national boundaries. Taking the world city network approach as starting point, it is possible to use relational data (i.e., information about the relationships between cities) rather than attribute data, and moreover, it…

Benchmark of Client and Server-Side Catchment Delineation Approaches on Web-Based Systems

Science.gov (United States)

Demir, I.; Sermet, M. Y.; Sit, M. A.

2016-12-01

Recent advances in internet and cyberinfrastructure technologies have provided the capability to acquire large scale spatial data from various gauges and sensor networks. The collection of environmental data increased demand for applications which are capable of managing and processing large-scale and high-resolution data sets. With the amount and resolution of data sets provided, one of the challenging tasks for organizing and customizing hydrological data sets is delineation of watersheds on demand. Watershed delineation is a process for creating a boundary that represents the contributing area for a specific control point or water outlet, with intent of characterization and analysis of portions of a study area. Although many GIS tools and software for watershed analysis are available on desktop systems, there is a need for web-based and client-side techniques for creating a dynamic and interactive environment for exploring hydrological data. In this project, we demonstrated several watershed delineation techniques on the web with various techniques implemented on the client-side using JavaScript and WebGL, and on the server-side using Python and C++. We also developed a client-side GPGPU (General Purpose Graphical Processing Unit) algorithm to analyze high-resolution terrain data for watershed delineation which allows parallelization using GPU. The web-based real-time analysis of watershed segmentation can be helpful for decision-makers and interested stakeholders while eliminating the need of installing complex software packages and dealing with large-scale data sets. Utilization of the client-side hardware resources also eliminates the need of servers due its crowdsourcing nature. Our goal for future work is to improve other hydrologic analysis methods such as rain flow tracking by adapting presented approaches.

The study of target delineation and target movement of whole breast assisted by active breathing control in intensity modulated radiotherapy after breast conservative surgery

International Nuclear Information System (INIS)

Li Jianbing; Yu Jinming; Ma Zhifang; Lu Jie; Sun Tao; Guo Shoufang; Wang Jingguo

2009-01-01

Objective: To explore the influence of different delineators and different delineating time on target determination of the whole breast and to explore intrafraction and interfraction target displacements of the breast on moderate deep inspiration breathing hold (mDIBH) assisted by active breathing control (ABC) alter breast conservative surgery. Methods: Twenty patients received primary CT-simulation assisted by ABC to get five sets of CT image on the three breathing condition which included one set from free breath (FB), two sets from mDIBH and two sets from deep expiration breathing control (DEBH). After radiotherapy with ten to fifteen fractions, the repeat CT-simulation was carried out to get the same five sets of CT image as the primary CT- simulation. The whole breast target were delineated at different time by the same delineator and delineated respectively by five delineators on the first set of CT images got with mDIBH from the primary CT-simulation, and to compare the influence of delineator and delineating time on the whole breast target. The total silver clips in the cavity were marked respectively on the two sets of CT images got with mDIBH from the primary CT-simulation, and to compare the intrafraction displacement of geometric body structured by the total of silver clips. The two ribs near the isocentric plane of the breast target were delineated respectively on two sets of the mDIBH CT image from the primary CT-simulation and on one set of the mDIBH CT image from the repeat CT-simulation, and comparing the movement of the point of interest (POI) of the ribs delineated to get the value of intrafraction and interfraction thoracic expansion. Results: There was not statistically significant between the four volumes of whole breast targets delineated by the same delineator at different time, but with statistics significant between the volumes of whole breast target delineated by the different delineators ( F=19.681, P=0.000). There was not statistically

Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.

Science.gov (United States)

Rios Velazquez, Emmanuel; Meier, Raphael; Dunn, William D; Alexander, Brian; Wiest, Roland; Bauer, Stefan; Gutman, David A; Reyes, Mauricio; Aerts, Hugo J W L

2015-11-18

Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.

Implementation of Automatic Clustering Algorithm and Fuzzy Time Series in Motorcycle Sales Forecasting

Science.gov (United States)

Rasim; Junaeti, E.; Wirantika, R.

2018-01-01

Accurate forecasting for the sale of a product depends on the forecasting method used. The purpose of this research is to build motorcycle sales forecasting application using Fuzzy Time Series method combined with interval determination using automatic clustering algorithm. Forecasting is done using the sales data of motorcycle sales in the last ten years. Then the error rate of forecasting is measured using Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting in the one-year period obtained in this study are included in good accuracy.

AN AUTOMATIC PROCEDURE FOR COMBINING DIGITAL IMAGES AND LASER SCANNER DATA

Directory of Open Access Journals (Sweden)

W. Moussa

2012-07-01

Full Text Available Besides improving both the geometry and the visual quality of the model, the integration of close-range photogrammetry and terrestrial laser scanning techniques directs at filling gaps in laser scanner point clouds to avoid modeling errors, reconstructing more details in higher resolution and recovering simple structures with less geometric details. Thus, within this paper a flexible approach for the automatic combination of digital images and laser scanner data is presented. Our approach comprises two methods for data fusion. The first method starts by a marker-free registration of digital images based on a point-based environment model (PEM of a scene which stores the 3D laser scanner point clouds associated with intensity and RGB values. The PEM allows the extraction of accurate control information for the direct computation of absolute camera orientations with redundant information by means of accurate space resection methods. In order to use the computed relations between the digital images and the laser scanner data, an extended Helmert (seven-parameter transformation is introduced and its parameters are estimated. Precedent to that, in the second method, the local relative orientation parameters of the camera images are calculated by means of an optimized Structure and Motion (SaM reconstruction method. Then, using the determined transformation parameters results in having absolute oriented images in relation to the laser scanner data. With the resulting absolute orientations we have employed robust dense image reconstruction algorithms to create oriented dense image point clouds, which are automatically combined with the laser scanner data to form a complete detailed representation of a scene. Examples of different data sets are shown and experimental results demonstrate the effectiveness of the presented procedures.

Assessment of various strategies for 18F-FET PET-guided delineation of target volumes in high-grade glioma patients

International Nuclear Information System (INIS)

Vees, Hansjoerg; Senthamizhchelvan, Srinivasan; Ratib, Osman; Miralbell, Raymond; Weber, Damien C.; Zaidi, Habib

2009-01-01

The purpose of the study is to assess the contribution of 18 F-fluoro-ethyl-tyrosine ( 18 F-FET) positron emission tomography (PET) in the delineation of gross tumor volume (GTV) in patients with high-grade gliomas compared with magnetic resonance imaging (MRI) alone. The study population consisted of 18 patients with high-grade gliomas. Seven image segmentation techniques were used to delineate 18 F-FET PET GTVs, and the results were compared to the manual MRI-derived GTV (GTV MRI ). PET image segmentation techniques included manual delineation of contours (GTV man ), a 2.5 standardized uptake value (SUV) cutoff (GTV 2.5 ), a fixed threshold of 40% and 50% of the maximum signal intensity (GTV 40% and GTV 50% ), signal-to-background ratio (SBR)-based adaptive thresholding (GTV SBR ), gradient find (GTV GF ), and region growing (GTV RG ). Overlap analysis was also conducted to assess geographic mismatch between the GTVs delineated using the different techniques. Contours defined using GTV 2.5 failed to provide successful delineation technically in three patients (18% of cases) as SUV max MRI (67% of cases). Yet, PET detected frequently tumors that are not visible on MRI and added substantially tumor extension outside the GTV MRI in six patients (33% of cases). The selection of the most appropriate 18 F-FET PET-based segmentation algorithm is crucial, since it impacts both the volume and shape of the resulting GTV. The 2.5 SUV isocontour and GF segmentation techniques performed poorly and should not be used for GTV delineation. With adequate setting, the SBR-based PET technique may add considerably to conventional MRI-guided GTV delineation. (orig.)

Inter-observer delineation uncertainty in radiotherapy of peripheral lung tumours

DEFF Research Database (Denmark)

Persson, Gitte Fredberg; Nygaard, Ditte Eklund; Roed, Anders Peter

was included in the study. In our clinical protocol the contrast enhanced PET/CT scan is primarily analysed by a specialist in nuclear medicine and a radiologists together. The PET positive volume is delineated by the specialist in nuclear medicine and only the CT scan and this contour is imported to Eclipse...

Neural Bases of Automaticity

Science.gov (United States)

Servant, Mathieu; Cassey, Peter; Woodman, Geoffrey F.; Logan, Gordon D.

2018-01-01

Automaticity allows us to perform tasks in a fast, efficient, and effortless manner after sufficient practice. Theories of automaticity propose that across practice processing transitions from being controlled by working memory to being controlled by long-term memory retrieval. Recent event-related potential (ERP) studies have sought to test this…

Variability in prostate and seminal vesicle delineations defined on magnetic resonance images, a multi-observer, -center and -sequence study

DEFF Research Database (Denmark)

Nyholm, Tufve; Jonsson, Joakim; Söderström, Karin

2013-01-01

and approximately equal for the prostate and seminal vesicles. Large differences in variability were observed for individual patients, and also for individual imaging sequences used at the different centers. There was however no indication of decreased variability with higher field strength. CONCLUSION: The overall......BACKGROUND: The use of magnetic resonance (MR) imaging as a part of preparation for radiotherapy is increasing. For delineation of the prostate several publications have shown decreased delineation variability using MR compared to computed tomography (CT). The purpose of the present work....... Two physicians from each center delineated the prostate and the seminal vesicles on each of the 25 image sets. The variability between the delineations was analyzed with respect to overall, intra- and inter-physician variability, and dependence between variability and origin of the MR images, i...

Brand and automaticity

OpenAIRE

Liu, J.

2008-01-01

A presumption of most consumer research is that consumers endeavor to maximize the utility of their choices and are in complete control of their purchasing and consumption behavior. However, everyday life experience suggests that many of our choices are not all that reasoned or conscious. Indeed, automaticity, one facet of behavior, is indispensable to complete the portrait of consumers. Despite its importance, little attention is paid to how the automatic side of behavior can be captured and...

Automatic Program Development

DEFF Research Database (Denmark)

Automatic Program Development is a tribute to Robert Paige (1947-1999), our accomplished and respected colleague, and moreover our good friend, whose untimely passing was a loss to our academic and research community. We have collected the revised, updated versions of the papers published in his...... honor in the Higher-Order and Symbolic Computation Journal in the years 2003 and 2005. Among them there are two papers by Bob: (i) a retrospective view of his research lines, and (ii) a proposal for future studies in the area of the automatic program derivation. The book also includes some papers...... by members of the IFIP Working Group 2.1 of which Bob was an active member. All papers are related to some of the research interests of Bob and, in particular, to the transformational development of programs and their algorithmic derivation from formal specifications. Automatic Program Development offers...

Evaluation of a new software tool for the automatic volume calculation of hepatic tumors. First results

International Nuclear Information System (INIS)

Meier, S.; Mildenberger, P.; Pitton, M.; Thelen, M.; Schenk, A.; Bourquain, H.

2004-01-01

Purpose: computed tomography has become the preferred method in detecting liver carcinomas. The introduction of spiral CT added volumetric assessment of intrahepatic tumors, which was unattainable in the clinical routine with incremental CT due to complex planimetric revisions and excessive computing time. In an ongoing clinical study, a new software tool was tested for the automatic detection of tumor volume and the time needed for this procedure. Materials and methods: we analyzed patients suffering from hepatocellular carcinoma (HCC). All patients underwent treatment with repeated transcatheter chemoembolization of the hepatic arteria. The volumes of the HCC lesions detected in CT were measured with the new software tool in HepaVison (MeVis, Germany). The results were compared with manual planimetric calculation of the volume performed by three independent radiologists. Results: our first results in 16 patients show a correlation between the automatically and the manually calculated volumes (up to a difference of 2 ml) of 96.8%. While the manual method of analyzing the volume of a lesion requires 2.5 minutes on average, the automatic method merely requires about 30 seconds of user interaction time. Conclusion: These preliminary results show a good correlation between automatic and manual calculations of the tumor volume. The new software tool requires less time for accurate determination of the tumor volume and can be applied in the daily clinical routine. (orig.) [de

A System for Continual Quality Improvement of Normal Tissue Delineation for Radiation Therapy Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Breunig, Jennifer; Hernandez, Sophy; Lin, Jeffrey; Alsager, Stacy; Dumstorf, Christine; Price, Jennifer; Steber, Jennifer; Garza, Richard; Nagda, Suneel; Melian, Edward; Emami, Bahman [Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois (United States); Roeske, John C., E-mail: jroeske@lumc.edu [Department of Radiation Oncology, Loyola University Medical Center, Maywood, Illinois (United States)

2012-08-01

Purpose: To implement the 'plan-do-check-act' (PDCA) cycle for the continual quality improvement of normal tissue contours used for radiation therapy treatment planning. Methods and Materials: The CT scans of patients treated for tumors of the brain, head and neck, thorax, pancreas and prostate were selected for this study. For each scan, a radiation oncologist and a diagnostic radiologist, outlined the normal tissues ('gold' contours) using Radiation Therapy Oncology Group (RTOG) guidelines. A total of 30 organs were delineated. Independently, 5 board-certified dosimetrists and 1 trainee then outlined the same organs. Metrics used to compare the agreement between the dosimetrists' contours and the gold contours included the Dice Similarity Coefficient (DSC), and a penalty function using distance to agreement. Based on these scores, dosimetrists were re-trained on those organs in which they did not receive a passing score, and they were subsequently re-tested. Results: Passing scores were achieved on 19 of 30 organs evaluated. These scores were correlated to organ volume. For organ volumes <8 cc, the average DSC was 0.61 vs organ volumes {>=}8 cc, for which the average DSC was 0.91 (P=.005). Normal tissues that had the lowest scores included the lenses, optic nerves, chiasm, cochlea, and esophagus. Of the 11 organs that were considered for re-testing, 10 showed improvement in the average score, and statistically significant improvement was noted in more than half of these organs after education and re-assessment. Conclusions: The results of this study indicate the feasibility of applying the PDCA cycle to assess competence in the delineation of individual organs, and to identify areas for improvement. With testing, guidance, and re-evaluation, contouring consistency can be obtained across multiple dosimetrists. Our expectation is that continual quality improvement using the PDCA approach will ensure more accurate treatments and dose

A System for Continual Quality Improvement of Normal Tissue Delineation for Radiation Therapy Treatment Planning

International Nuclear Information System (INIS)

Breunig, Jennifer; Hernandez, Sophy; Lin, Jeffrey; Alsager, Stacy; Dumstorf, Christine; Price, Jennifer; Steber, Jennifer; Garza, Richard; Nagda, Suneel; Melian, Edward; Emami, Bahman; Roeske, John C.

2012-01-01

Purpose: To implement the “plan-do-check-act” (PDCA) cycle for the continual quality improvement of normal tissue contours used for radiation therapy treatment planning. Methods and Materials: The CT scans of patients treated for tumors of the brain, head and neck, thorax, pancreas and prostate were selected for this study. For each scan, a radiation oncologist and a diagnostic radiologist, outlined the normal tissues (“gold” contours) using Radiation Therapy Oncology Group (RTOG) guidelines. A total of 30 organs were delineated. Independently, 5 board-certified dosimetrists and 1 trainee then outlined the same organs. Metrics used to compare the agreement between the dosimetrists' contours and the gold contours included the Dice Similarity Coefficient (DSC), and a penalty function using distance to agreement. Based on these scores, dosimetrists were re-trained on those organs in which they did not receive a passing score, and they were subsequently re-tested. Results: Passing scores were achieved on 19 of 30 organs evaluated. These scores were correlated to organ volume. For organ volumes <8 cc, the average DSC was 0.61 vs organ volumes ≥8 cc, for which the average DSC was 0.91 (P=.005). Normal tissues that had the lowest scores included the lenses, optic nerves, chiasm, cochlea, and esophagus. Of the 11 organs that were considered for re-testing, 10 showed improvement in the average score, and statistically significant improvement was noted in more than half of these organs after education and re-assessment. Conclusions: The results of this study indicate the feasibility of applying the PDCA cycle to assess competence in the delineation of individual organs, and to identify areas for improvement. With testing, guidance, and re-evaluation, contouring consistency can be obtained across multiple dosimetrists. Our expectation is that continual quality improvement using the PDCA approach will ensure more accurate treatments and dose assessment in

Combined reflectance confocal microscopy-optical coherence tomography for delineation of basal cell carcinoma margins: an ex vivo study

Science.gov (United States)

Iftimia, Nicusor; Peterson, Gary; Chang, Ernest W.; Maguluri, Gopi; Fox, William; Rajadhyaksha, Milind

2016-01-01

We present a combined reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) approach, integrated within a single optical layout, for diagnosis of basal cell carcinomas (BCCs) and delineation of margins. While RCM imaging detects BCC presence (diagnoses) and its lateral spreading (margins) with measured resolution of ˜1 μm, OCT imaging delineates BCC depth spreading (margins) with resolution of ˜7 μm. When delineating margins in 20 specimens of superficial and nodular BCCs, depth could be reliably determined down to ˜600 μm, and agreement with histology was within about ±50 μm.

Automatic tracking of the intersection of a laser and electron beam

International Nuclear Information System (INIS)

Turko, B.T.; Fuzesy, R.Z.; Pripstein, D.A.; Kowitt, M.; Chamberlain, O.; Shapiro, G.; Hughes, E.

1990-05-01

For the Compton Polarimeter experiment at the Stanford Linear Accelerator the crossing point of a laser beam and an electron beam must be kept accurate and stable. An electronic system is described for the automatic tracking and correcting of the beam crossing. A remote CCD camera, relatively insensitive to electromagnetic disturbance, records small displacements of the pulsed laser beam. Video signals are analyzed at a remote station, the amount of drift from a selected reference point determined and the appropriate correction commands sent to the motorized mirror deflecting the laser beam. A description of the system, its performance and the test results are presented. 2 refs., 4 figs

Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach.

Science.gov (United States)

Avendi, Michael R; Kheradvar, Arash; Jafarkhani, Hamid

2017-12-01

This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects). An average Dice metric of 82.5% along with an average Hausdorff distance of 7.85 mm were achieved for all the studied subjects. Furthermore, a high correlation and level of agreement with the ground truth contours for end-diastolic volume (0.98), end-systolic volume (0.99), and ejection fraction (0.93) were observed. Our results show that deep learning algorithms can be effectively used for automatic segmentation of the RV. Computed quantitative metrics of our method outperformed that of the existing techniques participated in the MICCAI 2012 challenge, as reported by the challenge organizers. Magn Reson Med 78:2439-2448, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Development of advanced automatic operation system for nuclear ship. 1. Perfect automatic normal operation

International Nuclear Information System (INIS)

Nakazawa, Toshio; Yabuuti, Noriaki; Takahashi, Hiroki; Shimazaki, Junya

1999-02-01

Development of operation support system such as automatic operating system and anomaly diagnosis systems of nuclear reactor is very important in practical nuclear ship because of a limited number of operators and severe conditions in which receiving support from others in a case of accident is very difficult. The goal of development of the operation support systems is to realize the perfect automatic control system in a series of normal operation from the reactor start-up to the shutdown. The automatic control system for the normal operation has been developed based on operating experiences of the first Japanese nuclear ship 'Mutsu'. Automation technique was verified by 'Mutsu' plant data at manual operation. Fully automatic control of start-up and shutdown operations was achieved by setting the desired value of operation and the limiting value of parameter fluctuation, and by making the operation program of the principal equipment such as the main coolant pump and the heaters. This report presents the automatic operation system developed for the start-up and the shutdown of reactor and the verification of the system using the Nuclear Ship Engineering Simulator System. (author)

Multimodality Tumor Delineation and Predictive Modelling via Fuzzy-Fusion Deformable Models and Biological Potential Functions

Science.gov (United States)

Wasserman, Richard Marc

The radiation therapy treatment planning (RTTP) process may be subdivided into three planning stages: gross tumor delineation, clinical target delineation, and modality dependent target definition. The research presented will focus on the first two planning tasks. A gross tumor target delineation methodology is proposed which focuses on the integration of MRI, CT, and PET imaging data towards the generation of a mathematically optimal tumor boundary. The solution to this problem is formulated within a framework integrating concepts from the fields of deformable modelling, region growing, fuzzy logic, and data fusion. The resulting fuzzy fusion algorithm can integrate both edge and region information from multiple medical modalities to delineate optimal regions of pathological tissue content. The subclinical boundaries of an infiltrating neoplasm cannot be determined explicitly via traditional imaging methods and are often defined to extend a fixed distance from the gross tumor boundary. In order to improve the clinical target definition process an estimation technique is proposed via which tumor growth may be modelled and subclinical growth predicted. An in vivo, macroscopic primary brain tumor growth model is presented, which may be fit to each patient undergoing treatment, allowing for the prediction of future growth and consequently the ability to estimate subclinical local invasion. Additionally, the patient specific in vivo tumor model will be of significant utility in multiple diagnostic clinical applications.

Automatic sleep scoring in normals and in individuals with neurodegenerative disorders according to new international sleep scoring criteria

DEFF Research Database (Denmark)

Jensen, Peter S.; Sørensen, Helge Bjarup Dissing; Jennum, P. J.