WorldWideScience

Sample records for adaptive 3-d segmentation

  1. 3D segmentation of masses in DCE-MRI images using FCM and adaptive MRF

    Science.gov (United States)

    Zhang, Chengjie; Li, Lihua

    2014-03-01

    Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging modality for the detection of breast cancer. Automated segmentation of breast lesions in DCE-MRI images is challenging due to inherent signal-to-noise ratios and high inter-patient variability. A novel 3D segmentation method based on FCM and MRF is proposed in this study. In this method, a MRI image is segmented by spatial FCM, firstly. And then MRF segmentation is conducted to refine the result. We combined with the 3D information of lesion in the MRF segmentation process by using segmentation result of contiguous slices to constraint the slice segmentation. At the same time, a membership matrix of FCM segmentation result is used for adaptive adjustment of Markov parameters in MRF segmentation process. The proposed method was applied for lesion segmentation on 145 breast DCE-MRI examinations (86 malignant and 59 benign cases). An evaluation of segmentation was taken using the traditional overlap rate method between the segmented region and hand-drawing ground truth. The average overlap rates for benign and malignant lesions are 0.764 and 0.755 respectively. Then we extracted five features based on the segmentation region, and used an artificial neural network (ANN) to classify between malignant and benign cases. The ANN had a classification performance measured by the area under the ROC curve of AUC=0.73. The positive and negative predictive values were 0.86 and 0.58, respectively. The results demonstrate the proposed method not only achieves a better segmentation performance in accuracy also has a reasonable classification performance.

  2. AN ADAPTIVE APPROACH FOR SEGMENTATION OF 3D LASER POINT CLOUD

    Directory of Open Access Journals (Sweden)

    Z. Lari

    2012-09-01

    Full Text Available Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each point is firstly established using an adaptive cylinder while considering the local point density and surface trend. This neighbourhood definition has a major effect on the computational accuracy of the segmentation attributes. In order to efficiently cluster planar surfaces and prevent introducing ambiguities, the coordinates of the origin's projection on each point's best fitted plane are used as the clustering attributes. Then, an octree space partitioning method is utilized to detect and extract peaks from the attribute space. Each detected peak represents a specific cluster of points which are located on a distinct planar surface in the object space. Experimental results show the potential and feasibility of applying this method for segmentation of both airborne and terrestrial laser data.

  3. An Adaptive Approach for Segmentation of 3d Laser Point Cloud

    Science.gov (United States)

    Lari, Z.; Habib, A.; Kwak, E.

    2011-09-01

    Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each point is firstly established using an adaptive cylinder while considering the local point density and surface trend. This neighbourhood definition has a major effect on the computational accuracy of the segmentation attributes. In order to efficiently cluster planar surfaces and prevent introducing ambiguities, the coordinates of the origin's projection on each point's best fitted plane are used as the clustering attributes. Then, an octree space partitioning method is utilized to detect and extract peaks from the attribute space. Each detected peak represents a specific cluster of points which are located on a distinct planar surface in the object space. Experimental results show the potential and feasibility of applying this method for segmentation of both airborne and terrestrial laser data.

  4. Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

    Science.gov (United States)

    He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer

    2015-03-01

    Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

  5. AN ADAPTIVE APPROACH FOR SEGMENTATION OF 3D LASER POINT CLOUD

    OpenAIRE

    Z. Lari; Habib, A.; E. Kwak

    2012-01-01

    Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each poin...

  6. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

    Science.gov (United States)

    Rebouças Filho, Pedro Pedrosa; Cortez, Paulo César; da Silva Barros, Antônio C; C Albuquerque, Victor Hugo; R S Tavares, João Manuel

    2017-01-01

    The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Spatio-Temporal Video Object Segmentation via Scale-Adaptive 3D Structure Tensor

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    Hai-Yun Wang

    2004-06-01

    Full Text Available To address multiple motions and deformable objects' motions encountered in existing region-based approaches, an automatic video object (VO segmentation methodology is proposed in this paper by exploiting the duality of image segmentation and motion estimation such that spatial and temporal information could assist each other to jointly yield much improved segmentation results. The key novelties of our method are (1 scale-adaptive tensor computation, (2 spatial-constrained motion mask generation without invoking dense motion-field computation, (3 rigidity analysis, (4 motion mask generation and selection, and (5 motion-constrained spatial region merging. Experimental results demonstrate that these novelties jointly contribute much more accurate VO segmentation both in spatial and temporal domains.

  8. Adaptive model based pulmonary artery segmentation in 3D chest CT

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    Feuerstein, Marco; Kitasaka, Takayuki; Mori, Kensaku

    2010-03-01

    The extraction and analysis of the pulmonary artery in computed tomography (CT) of the chest can be an important, but time-consuming step for the diagnosis and treatment of lung disease, in particular in non-contrast data, where the pulmonary artery has low contrast and frequently merges with adjacent tissue of similar intensity. We here present a new method for the automatic segmentation of the pulmonary artery based on an adaptive model, Hough and Euclidean distance transforms, and spline fitting, which works equally well on non-contrast and contrast enhanced data. An evaluation on 40 patient data sets and a comparison to manual segmentations in terms of Jaccard index, sensitivity, specificity, and minimum mean distance shows its overall robustness.

  9. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

    Science.gov (United States)

    Boegel, Marco; Hoelter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Doerfler, Arnd

    2015-01-01

    Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.

  10. Towards ultrasound-guided adaptive radiotherapy for cervical cancer: Evaluation of Elekta's semiautomated uterine segmentation method on 3D ultrasound images.

    Science.gov (United States)

    Mason, Sarah A; O'Shea, Tuathan P; White, Ingrid M; Lalondrelle, Susan; Downey, Kate; Baker, Mariwan; Behrens, Claus F; Bamber, Jeffrey C; Harris, Emma J

    2017-07-01

    3D ultrasound (US) images of the uterus may be used to adapt radiotherapy (RT) for cervical cancer patients based on changes in daily anatomy. This requires accurate on-line segmentation of the uterus. The aim of this work was to assess the accuracy of Elekta's "Assisted Gyne Segmentation" (AGS) algorithm in semi-automatically segmenting the uterus on 3D transabdominal ultrasound images by comparison with manual contours. Nine patients receiving RT for cervical cancer were imaged with the 3D Clarity ® transabdominal probe at RT planning, and 1 to 7 times during treatment. Image quality was rated from unusable (0)-excellent (3). Four experts segmented the uterus (defined as the uterine body and cervix) manually and using AGS on images with a ranking > 0. Pairwise analysis between manual contours was evaluated to determine interobserver variability. The accuracy of the AGS method was assessed by measuring its agreement with manual contours via pairwise analysis. 35/44 images acquired (79.5%) received a ranking > 0. For the manual contour variation, the median [interquartile range (IQR)] distance between centroids (DC) was 5.41 [5.0] mm, the Dice similarity coefficient (DSC) was 0.78 [0.11], the mean surface-to-surface distance (MSSD) was 3.20 [1.8] mm, and the uniform margin of 95% (UM95) was 4.04 [5.8] mm. There was no correlation between image quality and manual contour agreement. AGS failed to give a result in 19.3% of cases. For the remaining cases, the level of agreement between AGS contours and manual contours depended on image quality. There were no significant differences between the AGS segmentations and the manual segmentations on the images that received a quality rating of 3. However, the AGS algorithm had significantly worse agreement with manual contours on images with quality ratings of 1 and 2 compared with the corresponding interobserver manual variation. The overall median [IQR] DC, DSC, MSSD, and UM95 between AGS and manual contours was 5.48 [5

  11. Authoring Adaptive 3D Virtual Learning Environments

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    Ewais, Ahmed; De Troyer, Olga

    2014-01-01

    The use of 3D and Virtual Reality is gaining interest in the context of academic discussions on E-learning technologies. However, the use of 3D for learning environments also has drawbacks. One way to overcome these drawbacks is by having an adaptive learning environment, i.e., an environment that dynamically adapts to the learner and the…

  12. Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

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    Shuo-Tsung Chen

    2015-01-01

    Full Text Available Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

  13. 3D visualization for medical volume segmentation validation

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    Eldeib, Ayman M.

    2002-05-01

    This paper presents a 3-D visualization tool that manipulates and/or enhances by user input the segmented targets and other organs. A 3-D visualization tool is developed to create a precise and realistic 3-D model from CT/MR data set for manipulation in 3-D and permitting physician or planner to look through, around, and inside the various structures. The 3-D visualization tool is designed to assist and to evaluate the segmentation process. It can control the transparency of each 3-D object. It displays in one view a 2-D slice (axial, coronal, and/or sagittal)within a 3-D model of the segmented tumor or structures. This helps the radiotherapist or the operator to evaluate the adequacy of the generated target compared to the original 2-D slices. The graphical interface enables the operator to easily select a specific 2-D slice of the 3-D volume data set. The operator is enabled to manually override and adjust the automated segmentation results. After correction, the operator can see the 3-D model again and go back and forth till satisfactory segmentation is obtained. The novelty of this research work is in using state-of-the-art of image processing and 3-D visualization techniques to facilitate a process of a medical volume segmentation validation and assure the accuracy of the volume measurement of the structure of interest.

  14. 3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches

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    Shadi AlZu'bi

    2010-01-01

    that 3D methodologies can accurately detect the Region Of Interest (ROI. Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations.

  15. Chest Wall Segmentation in Automated 3D Breast Ultrasound Scans

    NARCIS (Netherlands)

    Tan, T.; Platel, B.; Mann, R.M.; Huisman, H.; Karssemeijer, N.

    2013-01-01

    In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates

  16. Prostate MR image segmentation using 3D active appearance models

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    Maan, Bianca; van der Heijden, Ferdinand

    2012-01-01

    This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain

  17. Precise segmentation of 3-D magnetic resonance angiography.

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    El-Baz, Ayman; Elnakib, Ahmed; Khalifa, Fahmi; El-Ghar, Mohamed Abou; McClure, Patrick; Soliman, Ahmed; Gimel'farb, Georgy

    2012-07-01

    Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussian-approximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.

  18. Needle segmentation using 3D Hough transform in 3D TRUS guided prostate transperineal therapy

    Energy Technology Data Exchange (ETDEWEB)

    Qiu Wu [Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074 (China); Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario N6A 5K8 (Canada); Yuchi Ming; Ding Mingyue [Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074 (China); Tessier, David; Fenster, Aaron [Imaging Research Laboratories, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5K8 (Canada)

    2013-04-15

    Purpose: Prostate adenocarcinoma is the most common noncutaneous malignancy in American men with over 200 000 new cases diagnosed each year. Prostate interventional therapy, such as cryotherapy and brachytherapy, is an effective treatment for prostate cancer. Its success relies on the correct needle implant position. This paper proposes a robust and efficient needle segmentation method, which acts as an aid to localize the needle in three-dimensional (3D) transrectal ultrasound (TRUS) guided prostate therapy. Methods: The procedure of locating the needle in a 3D TRUS image is a three-step process. First, the original 3D ultrasound image containing a needle is cropped; the cropped image is then converted to a binary format based on its histogram. Second, a 3D Hough transform based needle segmentation method is applied to the 3D binary image in order to locate the needle axis. The position of the needle endpoint is finally determined by an optimal threshold based analysis of the intensity probability distribution. The overall efficiency is improved through implementing a coarse-fine searching strategy. The proposed method was validated in tissue-mimicking agar phantoms, chicken breast phantoms, and 3D TRUS patient images from prostate brachytherapy and cryotherapy procedures by comparison to the manual segmentation. The robustness of the proposed approach was tested by means of varying parameters such as needle insertion angle, needle insertion length, binarization threshold level, and cropping size. Results: The validation results indicate that the proposed Hough transform based method is accurate and robust, with an achieved endpoint localization accuracy of 0.5 mm for agar phantom images, 0.7 mm for chicken breast phantom images, and 1 mm for in vivo patient cryotherapy and brachytherapy images. The mean execution time of needle segmentation algorithm was 2 s for a 3D TRUS image with size of 264 Multiplication-Sign 376 Multiplication-Sign 630 voxels. Conclusions

  19. 3D geomarketing segmentation: A higher spatial dimension planning perspective

    DEFF Research Database (Denmark)

    Suhaibah, A.; Uznir, U.; Rahman, A. A.

    2016-01-01

    , several locations of customers and stores in 3D are used in the test. The experiments demonstrated in this paper substantiated that the proposed approach is capable of minimizing overlapping segmentation and reducing repetitive data entries. The structure is also tested for retrieving the spatial records...

  20. Automatic segmentation of the puborectalis muscle in 3D transperineal ultrasound.

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    van den Noort, Frieda; Grob, Anique T M; Slump, Cornelis H; van der Vaart, Carl H; van Stralen, Marijn

    2017-10-11

    The introduction of 3D analysis of the puborectalis muscle, for diagnostic purposes, into daily practice is hindered by the need for appropriate training of the observers. Automatic 3D segmentation of the puborectalis muscle in 3D transperineal ultrasound may aid to its adaption in clinical practice. A manual 3D segmentation protocol was developed to segment the puborectalis muscle. The data of 20 women, in their first trimester of pregnancy, was used to validate the reproducibility of this protocol. For automatic segmentation, active appearance models of the puborectalis muscle were developed. Those models were trained using manual segmentation data of 50 women. The performance of both manual and automatic segmentation was analyzed by measuring the overlap and distance between the segmentations. Also, the interclass correlation coefficients and their 95% confidence intervals were determined for mean echogenicity and volume of the puborectalis muscle. The ICC values of mean echogenicity (0.968-0.991) and volume (0.626-0.910) are good to very good for both automatic and manual segmentation. The results of overlap and distance for manual segmentation are as expected, showing only few pixels (2-3) mismatch on average and a reasonable overlap. Based on overlap and distance 5 mismatches in automatic segmentation were detected, resulting in an automatic segmentation a success rate of 90%. In conclusion, this study presents a reliable manual and automatic 3D segmentation of the puborectalis muscle. This will facilitate future investigation of the puborectalis muscle. It also allows for reliable measurements of clinically potentially valuable parameters like mean echogenicity. This article is protected by copyright. All rights reserved.

  1. 3D segmentation of breast tumor in ultrasound images

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    Kwak, Jong In; Jung, Mal Nam; Kim, Sang Hyun; Kim, Nam Chul

    2003-05-01

    This paper proposes a three-dimensional (3D) region-based segmentation algorithm for extracting a diagnostic tumor from ultrasound images by using a split-and-merge and seeded region growing with a distortion-based homogeneity cost. In the proposed algorithm, 2D cutting planes are first obtained by the equiangular revolution of a cross sectional plane on a reference axis for a 3D volume data. In each cutting plane, an elliptic seed mask that is included tightly in a tumor of interest is set. At the same time, each plane is finely segmented using the split-and-merge with a distortion-based cost. In the result segmented finely, all of the regions that are across or contained in the elliptic seed mask are then merged. The merged region is taken as a seed region for the seeded region growing. In the seeded region growing, the seed region is recursively merged with adjacent regions until a predefined condition is reached. Then, the contour of the final seed region is extracted as a contour of the tumor. Finally, a 3D volume of the tumor is rendered from the set of tumor contours obtained for the entire cutting planes. Experimental results for a 3D artificial volume data show that the proposed method yields maximum three times reduction in error rate over the Krivanek"s method. For a real 3D ultrasonic volume data, the error rates of the proposed method are shown to be lower than 17% when the results obtained manually are used as a reference data. It also is found that the contours of the tumor extracted by the proposed algorithm coincide closely with those estimated by human vision.

  2. Segmentation of 3D EBSD data for subgrain boundary identification and feature characterization

    Energy Technology Data Exchange (ETDEWEB)

    Loeb, Andrew, E-mail: ael89@cornell.edu [Department of Engineering, Harvey Mudd College, 301 Platt Blvd, Claremont, CA 91711 (United States); Ferry, Michael [School of Materials Science and Engineering, The University of New South Wales, NSW 2052 (Australia); Bassman, Lori [Department of Engineering, Harvey Mudd College, 301 Platt Blvd, Claremont, CA 91711 (United States)

    2016-02-15

    Subgrain structures formed during plastic deformation of metals can be observed by electron backscatter diffraction (EBSD) but are challenging to identify automatically. We have adapted a 2D image segmentation technique, fast multiscale clustering (FMC), to 3D EBSD data using a novel variance function to accommodate quaternion data. This adaptation, which has been incorporated into the free open source texture analysis software package MTEX, is capable of segmenting based on subtle and gradual variation as well as on sharp boundaries within the data. FMC has been further modified to group the resulting closed 3D segment boundaries into distinct coherent surfaces based on local normals of a triangulated surface. We demonstrate the excellent capabilities of this technique with application to 3D EBSD data sets generated from cold rolled aluminum containing well-defined microbands, cold rolled and partly recrystallized extra low carbon steel microstructure containing three magnitudes of boundary misorientations, and channel-die plane strain compressed Goss-oriented nickel crystal containing microbands with very subtle changes in orientation. - Highlights: • Novel fast multiscale clustering (FMC) implementation segments 3D EBSD data. • FMC segments based on subtle or gradual variation as well as sharp boundaries. • A modification of FMC segments surfaces of microstructural feature volumes. • Method is incorporated in free open source texture analysis software package MTEX. • Several data sets demonstrate FMC and its advantages over Kuwahara filtering.

  3. 3D GEOMARKETING SEGMENTATION: A HIGHER SPATIAL DIMENSION PLANNING PERSPECTIVE

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    A. Suhaibah

    2016-09-01

    Full Text Available Geomarketing is a discipline which uses geographic information in the process of planning and implementation of marketing activities. It can be used in any aspect of the marketing such as price, promotion or geo targeting. The analysis of geomarketing data use a huge data pool such as location residential areas, topography, it also analyzes demographic information such as age, genre, annual income and lifestyle. This information can help users to develop successful promotional campaigns in order to achieve marketing goals. One of the common activities in geomarketing is market segmentation. The segmentation clusters the data into several groups based on its geographic criteria. To refine the search operation during analysis, we proposed an approach to cluster the data using a clustering algorithm. However, with the huge data pool, overlap among clusters may happen and leads to inefficient analysis. Moreover, geomarketing is usually active in urban areas and requires clusters to be organized in a three-dimensional (3D way (i.e. multi-level shop lots, residential apartments. This is a constraint with the current Geographic Information System (GIS framework. To avoid this issue, we proposed a combination of market segmentation based on geographic criteria and clustering algorithm for 3D geomarketing data management. The proposed approach is capable in minimizing the overlap region during market segmentation. In this paper, geomarketing in urban area is used as a case study. Based on the case study, several locations of customers and stores in 3D are used in the test. The experiments demonstrated in this paper substantiated that the proposed approach is capable of minimizing overlapping segmentation and reducing repetitive data entries. The structure is also tested for retrieving the spatial records from the database. For marketing purposes, certain radius of point is used to analyzing marketing targets. Based on the presented tests in this paper

  4. Comparison of thyroid segmentation techniques for 3D ultrasound

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    Wunderling, T.; Golla, B.; Poudel, P.; Arens, C.; Friebe, M.; Hansen, C.

    2017-02-01

    The segmentation of the thyroid in ultrasound images is a field of active research. The thyroid is a gland of the endocrine system and regulates several body functions. Measuring the volume of the thyroid is regular practice of diagnosing pathological changes. In this work, we compare three approaches for semi-automatic thyroid segmentation in freehand-tracked three-dimensional ultrasound images. The approaches are based on level set, graph cut and feature classification. For validation, sixteen 3D ultrasound records were created with ground truth segmentations, which we make publicly available. The properties analyzed are the Dice coefficient when compared against the ground truth reference and the effort of required interaction. Our results show that in terms of Dice coefficient, all algorithms perform similarly. For interaction, however, each algorithm has advantages over the other. The graph cut-based approach gives the practitioner direct influence on the final segmentation. Level set and feature classifier require less interaction, but offer less control over the result. All three compared methods show promising results for future work and provide several possible extensions.

  5. Correlation-based discrimination between cardiac tissue and blood for segmentation of 3D echocardiographic images

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    Saris, Anne E. C. M.; Nillesen, Maartje M.; Lopata, Richard G. P.; de Korte, Chris L.

    2013-03-01

    Automated segmentation of 3D echocardiographic images in patients with congenital heart disease is challenging, because the boundary between blood and cardiac tissue is poorly defined in some regions. Cardiologists mentally incorporate movement of the heart, using temporal coherence of structures to resolve ambiguities. Therefore, we investigated the merit of temporal cross-correlation for automated segmentation over the entire cardiac cycle. Optimal settings for maximum cross-correlation (MCC) calculation, based on a 3D cross-correlation based displacement estimation algorithm, were determined to obtain the best contrast between blood and myocardial tissue over the entire cardiac cycle. Resulting envelope-based as well as RF-based MCC values were used as additional external force in a deformable model approach, to segment the left-ventricular cavity in entire systolic phase. MCC values were tested against, and combined with, adaptive filtered, demodulated RF-data. Segmentation results were compared with manually segmented volumes using a 3D Dice Similarity Index (3DSI). Results in 3D pediatric echocardiographic images sequences (n = 4) demonstrate that incorporation of temporal information improves segmentation. The use of MCC values, either alone or in combination with adaptive filtered, demodulated RF-data, resulted in an increase of the 3DSI in 75% of the cases (average 3DSI increase: 0.71 to 0.82). Results might be further improved by optimizing MCC-contrast locally, in regions with low blood-tissue contrast. Reducing underestimation of the endocardial volume due to MCC processing scheme (choice of window size) and consequential border-misalignment, could also lead to more accurate segmentations. Furthermore, increasing the frame rate will also increase MCC-contrast and thus improve segmentation.

  6. Segmentation of the heart muscle in 3-D pediatric echocardiographic images.

    Science.gov (United States)

    Nillesen, Maartje M; Lopata, Richard G P; Gerrits, Inge H; Kapusta, Livia; Huisman, Henkjan J; Thijssen, Johan M; de Korte, Chris L

    2007-09-01

    This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle.

  7. Object Segmentation and Ground Truth in 3D Embryonic Imaging.

    Directory of Open Access Journals (Sweden)

    Bhavna Rajasekaran

    Full Text Available Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.

  8. Ultrafast superpixel segmentation of large 3D medical datasets

    Science.gov (United States)

    Leblond, Antoine; Kauffmann, Claude

    2016-03-01

    Even with recent hardware improvements, superpixel segmentation of large 3D medical images at interactive speed (GPU based hybrid framework implementing wavefront propagation and cellular automata resolution. Tasks will be scheduled in blocks (work units) using a wavefront propagation strategy, therefore allowing sparse scheduling. Because work units has been designed as spatially cohesive, the fast Thread Group Shared Memory can be used and reused through a Gauss-Seidel like acceleration. The work unit partitioning scheme will however vary on odd- and even-numbered iterations to reduce convergence barriers. Synchronization will be ensured by an 8-step 3D variant of the traditional Red Black Ordering scheme. An attack model and early termination will also be described and implemented as additional acceleration techniques. Using our hybrid framework and typical operating parameters, we were able to compute the superpixels of a high-resolution 512x512x512 aortic angioCT scan in 283 ms using a AMD R9 290X GPU. We achieved a 22.3X speed-up factor compared to the published reference GPU implementation.

  9. A combined learning algorithm for prostate segmentation on 3D CT images.

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

    2017-11-01

    Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is

  10. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

    Science.gov (United States)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

    2016-12-01

    The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

  11. 3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

    Science.gov (United States)

    Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

    2015-03-01

    During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

  12. 3D mesh segmentation of historic buildings for architectural surveys

    Directory of Open Access Journals (Sweden)

    Borja Javier Herráez

    2018-01-01

    Full Text Available Advances in three-dimensional (3D acquisition systems have introduced this technology to more fields of study, such as archaeology or architecture. In the architectural field, scanning a building is one of the first possible steps from which a 3D model can be obtained and can be later used for visualisation and/or feature analysis, thanks to computer-based pattern recognition tools. The automation of these tools allows for temporal savings and has become a strong aid for professionals, so that more and more methods are developed with this objective. In this article, a method for 3D mesh segmentation focused  on  the representation  of  historic  buildings  is  proposed.  This  type  of  buildings is characterised  by  having singularities  and features in  façades, such  as  doors  or  windows. The  main  objective  is  to  recognise  these  features, understanding them as those parts of the model that differ from the main structure of the building. The idea is to use a recognition algorithm for planar faces that allows users to create a graph showing the connectivity between them, therefore allowing the reflection of the shape of the 3Dmodel. At a later step, this graph is matched against some pre-defined graphs that  represent  the  patterns  to  look  for. Each  coincidence  between  both  graphs  indicate  the  position  of  one  of  the characteristics sought. The developed method has proved to be effective for feature detection and suitable for inclusion in architectural surveying applications.

  13. Usability Evaluation of an Adaptive 3D Virtual Learning Environment

    Science.gov (United States)

    Ewais, Ahmed; De Troyer, Olga

    2013-01-01

    Using 3D virtual environments for educational purposes is becoming attractive because of their rich presentation and interaction capabilities. Furthermore, dynamically adapting the 3D virtual environment to the personal preferences, prior knowledge, skills and competence, learning goals, and the personal or (social) context in which the learning…

  14. A random walk-based segmentation framework for 3D ultrasound images of the prostate.

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Fei, Baowei

    2017-10-01

    Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images. The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance. We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist. The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures. © 2017

  15. Automatic 2D and 3D segmentation of liver from Computerised Tomography

    Science.gov (United States)

    Evans, Alun

    As part of the diagnosis of liver disease, a Computerised Tomography (CT) scan is taken of the patient, which the clinician then uses for assistance in determining the presence and extent of the disease. This thesis presents the background, methodology, results and future work of a project that employs automated methods to segment liver tissue. The clinical motivation behind this work is the desire to facilitate the diagnosis of liver disease such as cirrhosis or cancer, assist in volume determination for liver transplantation, and possibly assist in measuring the effect of any treatment given to the liver. Previous attempts at automatic segmentation of liver tissue have relied on 2D, low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. The 2D results presented in this thesis improve greatly on this previous work by using a topology adaptive active contour model to accurately segment liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver this new technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. The concepts underlying the 2D technique are extended to 3D, and results of full 3D segmentation of the liver are presented. The 3D technique makes use of an inflationary active surface model which is adaptively reparameterised, according to its size and local curvature, in order that it may more accurately segment the organ. Statistical analysis of the accuracy of the segmentation is presented for 18 healthy liver datasets, and results of the segmentation of unhealthy livers are also shown. The novel work developed during the course of this project has possibilities for use in other areas of medical imaging research, for example the

  16. 3D Object Segmentation of Point Clouds using Profiling Techniques ...

    African Journals Online (AJOL)

    In the automatic processing of point clouds, higher level information in the form of point segments is required for classification and object detection purposes. Segmentation allows for the definition of these segments. Because of the increasing size of point clouds faster and more reliable segmentation methods are being ...

  17. Improving Semantic Updating Method on 3d City Models Using Hybrid Semantic-Geometric 3d Segmentation Technique

    Science.gov (United States)

    Sharkawi, K.-H.; Abdul-Rahman, A.

    2013-09-01

    to LoD4. The accuracy and structural complexity of the 3D objects increases with the LoD level where LoD0 is the simplest LoD (2.5D; Digital Terrain Model (DTM) + building or roof print) while LoD4 is the most complex LoD (architectural details with interior structures). Semantic information is one of the main components in CityGML and 3D City Models, and provides important information for any analyses. However, more often than not, the semantic information is not available for the 3D city model due to the unstandardized modelling process. One of the examples is where a building is normally generated as one object (without specific feature layers such as Roof, Ground floor, Level 1, Level 2, Block A, Block B, etc). This research attempts to develop a method to improve the semantic data updating process by segmenting the 3D building into simpler parts which will make it easier for the users to select and update the semantic information. The methodology is implemented for 3D buildings in LoD2 where the buildings are generated without architectural details but with distinct roof structures. This paper also introduces hybrid semantic-geometric 3D segmentation method that deals with hierarchical segmentation of a 3D building based on its semantic value and surface characteristics, fitted by one of the predefined primitives. For future work, the segmentation method will be implemented as part of the change detection module that can detect any changes on the 3D buildings, store and retrieve semantic information of the changed structure, automatically updates the 3D models and visualize the results in a userfriendly graphical user interface (GUI).

  18. Isotropic 3D cardiac cine MRI allows efficient sparse segmentation strategies based on 3D surface reconstruction.

    Science.gov (United States)

    Odille, Freddy; Bustin, Aurélien; Liu, Shufang; Chen, Bailiang; Vuissoz, Pierre-André; Felblinger, Jacques; Bonnemains, Laurent

    2017-10-02

    Segmentation of cardiac cine MRI data is routinely used for the volumetric analysis of cardiac function. Conventionally, 2D contours are drawn on short-axis (SAX) image stacks with relatively thick slices (typically 8 mm). Here, an acquisition/reconstruction strategy is used for obtaining isotropic 3D cine datasets; reformatted slices are then used to optimize the manual segmentation workflow. Isotropic 3D cine datasets were obtained from multiple 2D cine stacks (acquired during free-breathing in SAX and long-axis (LAX) orientations) using nonrigid motion correction (cine-GRICS method) and super-resolution. Several manual segmentation strategies were then compared, including conventional SAX segmentation, LAX segmentation in three views only, and combinations of SAX and LAX slices. An implicit B-spline surface reconstruction algorithm is proposed to reconstruct the left ventricular cavity surface from the sparse set of 2D contours. All tested sparse segmentation strategies were in good agreement, with Dice scores above 0.9 despite using fewer slices (3-6 sparse slices instead of 8-10 contiguous SAX slices). When compared to independent phase-contrast flow measurements, stroke volumes computed from four or six sparse slices had slightly higher precision than conventional SAX segmentation (error standard deviation of 5.4 mL against 6.1 mL) at the cost of slightly lower accuracy (bias of -1.2 mL against 0.2 mL). Functional parameters also showed a trend to improved precision, including end-diastolic volumes, end-systolic volumes, and ejection fractions). The postprocessing workflow of 3D isotropic cardiac imaging strategies can be optimized using sparse segmentation and 3D surface reconstruction. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  19. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

    Science.gov (United States)

    Kamnitsas, Konstantinos; Ledig, Christian; Newcombe, Virginia F J; Simpson, Joanna P; Kane, Andrew D; Menon, David K; Rueckert, Daniel; Glocker, Ben

    2017-02-01

    We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  20. 3d object segmentation of point clouds using profiling techniques

    African Journals Online (AJOL)

    Administrator

    Wang, J. & Shan, J., 2009. Segmentation of lidar point clouds for building extraction. In Proceedings. American Society of Photogramm Remote Sensing Annual Conference. p. 9–13. Yang, M., Lee, E., 1999. Segmentation of measured point data using a parametric quadric surface approximation, Computer-Aided Design 31, ...

  1. An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images.

    Science.gov (United States)

    Indhumathi, C; Cai, Y Y; Guan, Y Q; Opas, M

    2011-07-01

    With the rapid advance of three-dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images. © 2011 Nanyang Technological University Journal of Microscopy © 2011 Royal Microscopical Society.

  2. Parameterization adaption for 3D shape optimization in aerodynamics

    Directory of Open Access Journals (Sweden)

    Badr Abou El Majd

    2013-10-01

    Full Text Available When solving a PDE problem numerically, a certain mesh-refinement process is always implicit, and very classically, mesh adaptivity is a very effective means to accelerate grid convergence. Similarly, when optimizing a shape by means of an explicit geometrical representation, it is natural to seek for an analogous concept of parameterization adaptivity. We propose here an adaptive parameterization for three-dimensional optimum design in aerodynamics by using the so-called “Free-Form Deformation” approach based on 3D tensorial Bézier parameterization. The proposed procedure leads to efficient numerical simulations with highly reduced computational costs.[How to cite this article:  Majd, B.A.. 2014. Parameterization adaption for 3D shape optimization in aerodynamics. International Journal of Science and Engineering, 6(1:61-69. Doi: 10.12777/ijse.6.1.61-69

  3. A vectorizable adaptive grid solver for PDEs in 3D

    NARCIS (Netherlands)

    J.G. Blom (Joke); J.G. Verwer (Jan)

    1993-01-01

    textabstractThis paper describes the application of an adaptive-grid finite-difference solver to some time-dependent three-dimensional systems of partial differential equations. The code is a 3D extension of the 2D code VLUGR2[3].

  4. Content-adaptive pyramid representation for 3D object classification

    DEFF Research Database (Denmark)

    Kounalakis, Tsampikos; Boulgouris, Nikolaos; Triantafyllidis, Georgios

    2016-01-01

    In this paper we introduce a novel representation for the classification of 3D images. Unlike most current approaches, our representation is not based on a fixed pyramid but adapts to image content and uses image regions instead of rectangular pyramid scales. Image characteristics, such as depth...

  5. Spatially adaptive alpha-rooting in BM3D sharpening

    Science.gov (United States)

    Mäkitalo, Markku; Foi, Alessandro

    2011-03-01

    The block-matching and 3-D filtering (BM3D) algorithm is currently one of the most powerful and effective image denoising procedures. It exploits a specific nonlocal image modelling through grouping and collaborative filtering. Grouping finds mutually similar 2-D image blocks and stacks them together in 3-D arrays. Collaborative filtering produces individual estimates of all grouped blocks by filtering them jointly, through transform-domain shrinkage of the 3-D arrays (groups). BM3D can be combined with transform-domain alpha-rooting in order to simultaneously sharpen and denoise the image. Specifically, the thresholded 3-D transform-domain coefficients are modified by taking the alpha-root of their magnitude for some alpha > 1, thus amplifying the differences both within and between the grouped blocks. While one can use a constant (global) alpha throughout the entire image, further performance can be achieved by allowing different degrees of sharpening in different parts of the image, based on content-dependent information. We propose to vary the value of alpha used for sharpening a group through weighted estimates of the low-frequency, edge, and high-frequency content of the average block in the group. This is shown to be a viable approach for image sharpening, and in particular it can provide an improvement (both visually and in terms of PSNR) over its global non-adaptive counterpart.

  6. Adaptive kernel regression for freehand 3D ultrasound reconstruction

    Science.gov (United States)

    Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

    2017-03-01

    Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

  7. 3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS

    Directory of Open Access Journals (Sweden)

    C. Zhang

    2016-10-01

    Full Text Available 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

  8. Shape-driven 3D segmentation using spherical wavelets.

    Science.gov (United States)

    Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

    2006-01-01

    This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.

  9. 3D deeply supervised network for automated segmentation of volumetric medical images.

    Science.gov (United States)

    Dou, Qi; Yu, Lequan; Chen, Hao; Jin, Yueming; Yang, Xin; Qin, Jing; Heng, Pheng-Ann

    2017-10-01

    While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Multiscale 3-D shape representation and segmentation using spherical wavelets.

    Science.gov (United States)

    Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

    2007-04-01

    This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed shape representation can be optimized to compactly encode shape variations in a population at the needed scale and spatial locations, enabling the construction of more descriptive, nonglobal, nonuniform shape probability priors to be included in the segmentation and shape analysis framework. In particular, this representation addresses the shortcomings of techniques that learn a global shape prior at a single scale of analysis and cannot represent fine, local variations in a population of shapes in the presence of a limited dataset. Specifically, our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to two different brain structures, the caudate nucleus and the hippocampus, of interest in the study of schizophrenia. We show: 1) a reconstruction task of a test set to validate the expressiveness of

  11. 3D Filament Network Segmentation with Multiple Active Contours

    Science.gov (United States)

    Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

    2014-03-01

    Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

  12. Segmentation of the ovine lung in 3D CT Images

    Science.gov (United States)

    Shi, Lijun; Hoffman, Eric A.; Reinhardt, Joseph M.

    2004-04-01

    Pulmonary CT images can provide detailed information about the regional structure and function of the respiratory system. Prior to any of these analyses, however, the lungs must be identified in the CT data sets. A popular animal model for understanding lung physiology and pathophysiology is the sheep. In this paper we describe a lung segmentation algorithm for CT images of sheep. The algorithm has two main steps. The first step is lung extraction, which identifies the lung region using a technique based on optimal thresholding and connected components analysis. The second step is lung separation, which separates the left lung from the right lung by identifying the central fissure using an anatomy-based method incorporating dynamic programming and a line filter algorithm. The lung segmentation algorithm has been validated by comparing our automatic method to manual analysis for five pulmonary CT datasets. The RMS error between the computer-defined and manually-traced boundary is 0.96 mm. The segmentation requires approximately 10 minutes for a 512x512x400 dataset on a PC workstation (2.40 GHZ CPU, 2.0 GB RAM), while it takes human observer approximately two hours to accomplish the same task.

  13. Esophagus Segmentation from 3D CT Data Using Skeleton Prior-Based Graph Cut

    Directory of Open Access Journals (Sweden)

    Damien Grosgeorge

    2013-01-01

    Full Text Available The segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper, we focus on esophagus segmentation, a challenging application since the wall of the esophagus, made of muscle tissue, has very low contrast in CT images. We propose in this paper an original method to segment in thoracic CT scans the 3D esophagus using a skeleton-shape model to guide the segmentation. Our method is composed of two steps: a 3D segmentation by graph cut with skeleton prior, followed by a 2D propagation. Our method yields encouraging results over 6 patients.

  14. Method, Software and Aparatus for Segmenting a Series of 2D or 3D Images

    NARCIS (Netherlands)

    Noble, Nicholas M.I.; Spreeuwers, Lieuwe Jan; Breeuwer, Marcel

    2005-01-01

    he invention relates to an apparatus having means for segmenting a series of 2D or 3D images obtained by monitoring a patient's organ or other body part, wherein a first segmentation is carried out on a first image of the series of images and wherein the first segmentation is used for the subsequent

  15. Method, Software and Aparatus for Segmenting a Series of 2D or 3D Images

    NARCIS (Netherlands)

    Noble, Nicholas Michael Ian; Spreeuwers, Lieuwe Jan; Breeuwer, Marcel

    2010-01-01

    he invention relates to an apparatus having means for segmenting a series of 2D or 3D images obtained by monitoring a patient's organ or other body part, wherein a first segmentation is carried out on a first image of the series of images and wherein the first segmentation is used for the subsequent

  16. A Hybrid 3D Learning-and-Interaction-based Segmentation Approach Applied on CT Liver Volumes

    Directory of Open Access Journals (Sweden)

    M. Danciu

    2013-04-01

    Full Text Available Medical volume segmentation in various imaging modalities using real 3D approaches (in contrast to slice-by-slice segmentation represents an actual trend. The increase in the acquisition resolution leads to large amount of data, requiring solutions to reduce the dimensionality of the segmentation problem. In this context, the real-time interaction with the large medical data volume represents another milestone. This paper addresses the twofold problem of the 3D segmentation applied to large data sets and also describes an intuitive neuro-fuzzy trained interaction method. We present a new hybrid semi-supervised 3D segmentation, for liver volumes obtained from computer tomography scans. This is a challenging medical volume segmentation task, due to the acquisition and inter-patient variability of the liver parenchyma. The proposed solution combines a learning-based segmentation stage (employing 3D discrete cosine transform and a probabilistic support vector machine classifier with a post-processing stage (automatic and manual segmentation refinement. Optionally, an optimization of the segmentation can be achieved by level sets, using as initialization the segmentation provided by the learning-based solution. The supervised segmentation is applied on elementary cubes in which the CT volume is decomposed by tilling, thus ensuring a significant reduction of the data to be classified by the support vector machine into liver/not liver. On real volumes, the proposed approach provides good segmentation accuracy, with a significant reduction in the computational complexity.

  17. Parallel 3D Mortar Element Method for Adaptive Nonconforming Meshes

    Science.gov (United States)

    Feng, Huiyu; Mavriplis, Catherine; VanderWijngaart, Rob; Biswas, Rupak

    2004-01-01

    High order methods are frequently used in computational simulation for their high accuracy. An efficient way to avoid unnecessary computation in smooth regions of the solution is to use adaptive meshes which employ fine grids only in areas where they are needed. Nonconforming spectral elements allow the grid to be flexibly adjusted to satisfy the computational accuracy requirements. The method is suitable for computational simulations of unsteady problems with very disparate length scales or unsteady moving features, such as heat transfer, fluid dynamics or flame combustion. In this work, we select the Mark Element Method (MEM) to handle the non-conforming interfaces between elements. A new technique is introduced to efficiently implement MEM in 3-D nonconforming meshes. By introducing an "intermediate mortar", the proposed method decomposes the projection between 3-D elements and mortars into two steps. In each step, projection matrices derived in 2-D are used. The two-step method avoids explicitly forming/deriving large projection matrices for 3-D meshes, and also helps to simplify the implementation. This new technique can be used for both h- and p-type adaptation. This method is applied to an unsteady 3-D moving heat source problem. With our new MEM implementation, mesh adaptation is able to efficiently refine the grid near the heat source and coarsen the grid once the heat source passes. The savings in computational work resulting from the dynamic mesh adaptation is demonstrated by the reduction of the the number of elements used and CPU time spent. MEM and mesh adaptation, respectively, bring irregularity and dynamics to the computer memory access pattern. Hence, they provide a good way to gauge the performance of computer systems when running scientific applications whose memory access patterns are irregular and unpredictable. We select a 3-D moving heat source problem as the Unstructured Adaptive (UA) grid benchmark, a new component of the NAS Parallel

  18. Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Fenster, Aaron

    2015-02-01

    Efficient and accurate segmentations of 3D end-firing transrectal ultrasound (TRUS) images play an important role in planning of 3D TRUS guided prostate biopsy. However, poor image quality of the input 3D TRUS images, such as strong imaging artifacts and speckles, often makes it a challenging task to extract the prostate boundaries accurately and efficiently. In this paper, the authors propose a novel convex optimization-based approach to delineate the prostate surface from a given 3D TRUS image, which reduces the original 3D segmentation problem to a sequence of simple 2D segmentation subproblems over the rotational reslices of the 3D TRUS volume. Essentially, the authors introduce a novel convex relaxation-based contour evolution approach to each 2D slicewise image segmentation with the joint optimization of shape information, where the learned 2D nonlinear statistical shape prior is incorporated to segment the initial slice, its result is propagated as a shape constraint to the segmentation of the following slices. In practice, the proposed segmentation algorithm is implemented on a GPU to achieve the high computational performance. Experimental results using 30 patient 3D TRUS images show that the proposed method can achieve a mean Dice similarity coefficient of 93.4% ± 2.2% in 20 s for one 3D image, outperforming the existing local-optimization-based methods, e.g., level-set and active-contour, in terms of accuracy and efficiency. In addition, inter- and intraobserver variability experiments show its good reproducibility. A semiautomatic segmentation approach is proposed and evaluated to extract the prostate boundary from 3D TRUS images acquired by a 3D end-firing TRUS guided prostate biopsy system. Experimental results suggest that it may be suitable for the clinical use involving the image guided prostate biopsy procedures.

  19. Extended 3D Line Segments from RGB-D Data for Pose Estimation

    DEFF Research Database (Denmark)

    Buch, Anders Glent; Jessen, Jeppe Barsøe; Kraft, Dirk

    2013-01-01

    We propose a method for the extraction of complete and rich symbolic line segments in 3D based on RGB-D data. Edges are detected by combining cues from the RGB image and the aligned depth map. 3D line segments are then reconstructed by back-projecting 2D line segments and intersecting this with l...... this with local surface patches computed from the 3D point cloud. Different edge types are classified using the new enriched representation and the potential of this representation for the task of pose estimation is demonstrated....

  20. Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior

    Science.gov (United States)

    Yang, Xiaofeng; Schuster, David; Master, Viraj; Nieh, Peter; Fenster, Aaron; Fei, Baowei

    2011-03-01

    We are developing a molecular image-directed, 3D ultrasound-guided, targeted biopsy system for improved detection of prostate cancer. In this paper, we propose an automatic 3D segmentation method for transrectal ultrasound (TRUS) images, which is based on multi-atlas registration and statistical texture prior. The atlas database includes registered TRUS images from previous patients and their segmented prostate surfaces. Three orthogonal Gabor filter banks are used to extract texture features from each image in the database. Patient-specific Gabor features from the atlas database are used to train kernel support vector machines (KSVMs) and then to segment the prostate image from a new patient. The segmentation method was tested in TRUS data from 5 patients. The average surface distance between our method and manual segmentation is 1.61 +/- 0.35 mm, indicating that the atlas-based automatic segmentation method works well and could be used for 3D ultrasound-guided prostate biopsy.

  1. 3D conformal planning using low segment multi-criteria IMRT optimization

    CERN Document Server

    Khan, Fazal

    2014-01-01

    Purpose: To evaluate automated multicriteria optimization (MCO)-- designed for intensity modulated radiation therapy (IMRT), but invoked with limited segmentation -- to efficiently produce high quality 3D conformal treatment (3D-CRT) plans. Methods: Ten patients previously planned with 3D-CRT were replanned with a low-segment inverse multicriteria optimized technique. The MCO-3D plans used the same number of beams, beam geometry and machine parameters of the corresponding 3D plans, but were limited to an energy of 6 MV. The MCO-3D plans were optimized using a fluence-based MCO IMRT algorithm and then, after MCO navigation, segmented with a low number of segments. The 3D and MCO-3D plans were compared by evaluating mean doses to individual organs at risk (OARs), mean doses to combined OARs, homogeneity indexes (HI), monitor units (MUs), physician preference, and qualitative assessments of planning time and plan customizability. Results: The MCO-3D plans significantly reduced the OAR mean doses and monitor unit...

  2. 3D Game Content Distributed Adaptation in Heterogeneous Environments

    Directory of Open Access Journals (Sweden)

    Berretty Robert-Paul

    2007-01-01

    Full Text Available Most current multiplayer 3D games can only be played on a single dedicated platform (a particular computer, console, or cell phone, requiring specifically designed content and communication over a predefined network. Below we show how, by using signal processing techniques such as multiresolution representation and scalable coding for all the components of a 3D graphics object (geometry, texture, and animation, we enable online dynamic content adaptation, and thus delivery of the same content over heterogeneous networks to terminals with very different profiles, and its rendering on them. We present quantitative results demonstrating how the best displayed quality versus computational complexity versus bandwidth tradeoffs have been achieved, given the distributed resources available over the end-to-end content delivery chain. Additionally, we use state-of-the-art, standardised content representation and compression formats (MPEG-4 AFX, JPEG 2000, XML, enabling deployment over existing infrastructure, while keeping hooks to well-established practices in the game industry.

  3. 3D Game Content Distributed Adaptation in Heterogeneous Environments

    Science.gov (United States)

    Morán, Francisco; Preda, Marius; Lafruit, Gauthier; Villegas, Paulo; Berretty, Robert-Paul

    2007-12-01

    Most current multiplayer 3D games can only be played on a single dedicated platform (a particular computer, console, or cell phone), requiring specifically designed content and communication over a predefined network. Below we show how, by using signal processing techniques such as multiresolution representation and scalable coding for all the components of a 3D graphics object (geometry, texture, and animation), we enable online dynamic content adaptation, and thus delivery of the same content over heterogeneous networks to terminals with very different profiles, and its rendering on them. We present quantitative results demonstrating how the best displayed quality versus computational complexity versus bandwidth tradeoffs have been achieved, given the distributed resources available over the end-to-end content delivery chain. Additionally, we use state-of-the-art, standardised content representation and compression formats (MPEG-4 AFX, JPEG 2000, XML), enabling deployment over existing infrastructure, while keeping hooks to well-established practices in the game industry.

  4. An improved Marching Cube algorithm for 3D data segmentation

    Science.gov (United States)

    Masala, G. L.; Golosio, B.; Oliva, P.

    2013-03-01

    The marching cube algorithm is one of the most popular algorithms for isosurface triangulation. It is based on a division of the data volume into elementary cubes, followed by a standard triangulation inside each cube. In the original formulation, the marching cube algorithm is based on 15 basic triangulations and a total of 256 elementary triangulations are obtained from the basic ones by rotation, reflection, conjugation, and combinations of these operations. The original formulation of the algorithm suffers from well-known problems of connectivity among triangles of adjacent cubes, which has been solved in various ways. We developed a variant of the marching cube algorithm that makes use of 21 basic triangulations. Triangles of adjacent cubes are always well connected in this approach. The output of the code is a triangulated model of the isosurface in raw format or in VRML (Virtual Reality Modelling Language) format. Catalogue identifier: AENS_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENS_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 147558 No. of bytes in distributed program, including test data, etc.: 26084066 Distribution format: tar.gz Programming language: C. Computer: Pentium 4, CPU 3.2 GHz and 3.24 GB of RAM (2.77 GHz). Operating system: Tested on several Linux distribution, but generally works in all Linux-like platforms. RAM: Approximately 2 MB Classification: 6.5. Nature of problem: Given a scalar field μ(x,y,z) sampled on a 3D regular grid, build a discrete model of the isosurface associated to the isovalue μIso, which is defined as the set of points that satisfy the equation μ(x,y,z)=μIso. Solution method: The proposed solution is an improvement of the Marching Cube algorithm, which approximates the isosurface using a set of

  5. SEGMENTATION OF UAV-BASED IMAGES INCORPORATING 3D POINT CLOUD INFORMATION

    Directory of Open Access Journals (Sweden)

    A. Vetrivel

    2015-03-01

    Full Text Available Numerous applications related to urban scene analysis demand automatic recognition of buildings and distinct sub-elements. For example, if LiDAR data is available, only 3D information could be leveraged for the segmentation. However, this poses several risks, for instance, the in-plane objects cannot be distinguished from their surroundings. On the other hand, if only image based segmentation is performed, the geometric features (e.g., normal orientation, planarity are not readily available. This renders the task of detecting the distinct sub-elements of the building with similar radiometric characteristic infeasible. In this paper the individual sub-elements of buildings are recognized through sub-segmentation of the building using geometric and radiometric characteristics jointly. 3D points generated from Unmanned Aerial Vehicle (UAV images are used for inferring the geometric characteristics of roofs and facades of the building. However, the image-based 3D points are noisy, error prone and often contain gaps. Hence the segmentation in 3D space is not appropriate. Therefore, we propose to perform segmentation in image space using geometric features from the 3D point cloud along with the radiometric features. The initial detection of buildings in 3D point cloud is followed by the segmentation in image space using the region growing approach by utilizing various radiometric and 3D point cloud features. The developed method was tested using two data sets obtained with UAV images with a ground resolution of around 1-2 cm. The developed method accurately segmented most of the building elements when compared to the plane-based segmentation using 3D point cloud alone.

  6. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

    OpenAIRE

    Seoungjae Cho; Jonghyun Kim; Warda Ikram; Kyungeun Cho; Young-Sik Jeong; Kyhyun Um; Sungdae Sim

    2014-01-01

    A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geogr...

  7. Automatic 3D segmentation of spinal cord MRI using propagated deformable models

    Science.gov (United States)

    De Leener, B.; Cohen-Adad, J.; Kadoury, S.

    2014-03-01

    Spinal cord diseases or injuries can cause dysfunction of the sensory and locomotor systems. Segmentation of the spinal cord provides measures of atrophy and allows group analysis of multi-parametric MRI via inter-subject registration to a template. All these measures were shown to improve diagnostic and surgical intervention. We developed a framework to automatically segment the spinal cord on T2-weighted MR images, based on the propagation of a deformable model. The algorithm is divided into three parts: first, an initialization step detects the spinal cord position and orientation by using the elliptical Hough transform on multiple adjacent axial slices to produce an initial tubular mesh. Second, a low-resolution deformable model is iteratively propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a contrast adaptation at each iteration. Third, a refinement process and a global deformation are applied on the low-resolution mesh to provide an accurate segmentation of the spinal cord. Our method was evaluated against a semi-automatic edge-based snake method implemented in ITK-SNAP (with heavy manual adjustment) by computing the 3D Dice coefficient, mean and maximum distance errors. Accuracy and robustness were assessed from 8 healthy subjects. Each subject had two volumes: one at the cervical and one at the thoracolumbar region. Results show a precision of 0.30 +/- 0.05 mm (mean absolute distance error) in the cervical region and 0.27 +/- 0.06 mm in the thoracolumbar region. The 3D Dice coefficient was of 0.93 for both regions.

  8. New approach for validating the segmentation of 3D data applied to individual fibre extraction

    DEFF Research Database (Denmark)

    Emerson, Monica Jane; Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2017-01-01

    We present two approaches for validating the segmentation of 3D data. The first approach consists on comparing the amount of estimated material to a value provided by the manufacturer. The second approach consists on comparing the segmented results to those obtained from imaging modalities...

  9. 3D modeling of geological anomalies based on segmentation of multiattribute fusion

    Science.gov (United States)

    Liu, Zhi-Ning; Song, Cheng-Yun; Li, Zhi-Yong; Cai, Han-Peng; Yao, Xing-Miao; Hu, Guang-Min

    2016-09-01

    3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset-based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.

  10. Segmentation of vertebral bodies in CT and MR images based on 3D deterministic models

    Science.gov (United States)

    Štern, Darko; Vrtovec, Tomaž; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    The evaluation of vertebral deformations is of great importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is oriented towards the computed tomography (CT) and magnetic resonance (MR) imaging techniques, as they can provide a detailed 3D representation of vertebrae, the established methods for the evaluation of vertebral deformations still provide only a two-dimensional (2D) geometrical description. Segmentation of vertebrae in 3D may therefore not only improve their visualization, but also provide reliable and accurate 3D measurements of vertebral deformations. In this paper we propose a method for 3D segmentation of individual vertebral bodies that can be performed in CT and MR images. Initialized with a single point inside the vertebral body, the segmentation is performed by optimizing the parameters of a 3D deterministic model of the vertebral body to achieve the best match of the model to the vertebral body in the image. The performance of the proposed method was evaluated on five CT (40 vertebrae) and five T2-weighted MR (40 vertebrae) spine images, among them five are normal and five are pathological. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images and that the proposed model can describe a variety of vertebral body shapes. The method may be therefore used for initializing whole vertebra segmentation or reliably describing vertebral body deformations.

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

    DEFF Research Database (Denmark)

    Jørgensen, Peter Stanley; Bowen, Jacob R.

    2008-01-01

    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......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...... 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...

  12. 3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

    Science.gov (United States)

    Aloni, Doron; Jung, Jae-Hyun; Yitzhaky, Yitzhak

    2017-10-01

    Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

  13. Active contours extension and similarity indicators for improved 3D segmentation of thyroid ultrasound images

    Science.gov (United States)

    Poudel, P.; Illanes, A.; Arens, C.; Hansen, C.; Friebe, M.

    2017-03-01

    Thyroid segmentation in tracked 2D ultrasound (US) using active contours has a low segmentation accuracy mainly due to the fact that smaller structures cannot be efficiently recognized and segmented. To address this issue, we propose a new similarity indicator with the main objective to provide information to the active contour algorithm concerning the regions that the active contour should continue to expand or should stop. First, a preprocessing step is carried out in order to attenuate the noise present in the US image and to increase its contrast, using histogram equalization and a median filter. In the second step, active contours are used to segment the thyroid in each 2D image of the dataset. After performing a first segmentation, two similarity indicators (ratio of mean square error, MSE and correlation between histograms) are computed at each contour point of the initial segmented thyroid between rectangles located inside and outside the obtained contour. A threshold is used on a final indicator computed from the other two indicators to find the probable regions for further segmentation using active contours. This process is repeated until no new segmentation region is identified. Finally, all the segmented thyroid images passed through a 3D reconstruction algorithm to obtain a 3D volume segmented thyroid. The results showed that including similarity indicators based on histogram equalization and MSE between inside and outside regions of the contour can help to segment difficult areas that active contours have problem to segment.

  14. 3-D electrical resistivity tomography using adaptive wavelet parameter grids

    Science.gov (United States)

    Plattner, A.; Maurer, H. R.; Vorloeper, J.; Blome, M.

    2012-04-01

    We present a novel adaptive model parametrization strategy for the 3-D electrical resistivity tomography problem and demonstrate its capabilities with a series of numerical examples. In contrast to traditional parametrization schemes, which are based on fixed disjoint blocks, we discretize the subsurface in terms of Haar wavelets and adaptively adjust the parametrization as the iterative inversion proceeds. This results in a favourable balance of cell sizes and parameter reliability, that is, in regions where the data constrain the subsurface properties well, our parametrization strategy leads to a fine grid, whereas poorly resolved areas are represented only by a few large blocks. This is documented with eigenvalue analyses and by computing model resolution matrices. During the initial iteration steps, only a few model parameters are involved, which reduces the risk that the regularization dominates the inversion. The algorithm also automatically accounts for non-linear effects caused by pronounced conductivity contrasts. Inside conductive features a finer grid is generated than inside more resistive structures. The automated parameter adaptation is computationally efficient, because the coarsening and refinement subroutines have a nearly linear numerical complexity with respect to the number of model parameters. Because our approach is not tightly coupled to electrical resistivity tomography, it should be straightforward to adapt it to other data types.

  15. Adaptive segmentation for scientific databases

    NARCIS (Netherlands)

    Ivanova, M.; Kersten, M.L.; Nes, N.

    2008-01-01

    In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate)

  16. Segmented images and 3D images for studying the anatomical structures in MRIs

    Science.gov (United States)

    Lee, Yong Sook; Chung, Min Suk; Cho, Jae Hyun

    2004-05-01

    For identifying the pathological findings in MRIs, the anatomical structures in MRIs should be identified in advance. For studying the anatomical structures in MRIs, an education al tool that includes the horizontal, coronal, sagittal MRIs of entire body, corresponding segmented images, 3D images, and browsing software is necessary. Such an educational tool, however, is hard to obtain. Therefore, in this research, such an educational tool which helps medical students and doctors study the anatomical structures in MRIs was made as follows. A healthy, young Korean male adult with standard body shape was selected. Six hundred thirteen horizontal MRIs of the entire body were scanned and inputted to the personal computer. Sixty anatomical structures in the horizontal MRIs were segmented to make horizontal segmented images. Coronal, sagittal MRIs and coronal, sagittal segmented images were made. 3D images of anatomical structures in the segmented images were reconstructed by surface rendering method. Browsing software of the MRIs, segmented images, and 3D images was composed. This educational tool that includes horizontal, coronal, sagittal MRIs of entire body, corresponding segmented images, 3D images, and browsing software is expected to help medical students and doctors study anatomical structures in MRIs.

  17. 3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.

    Science.gov (United States)

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods.

  18. A fast and memory efficient stationary wavelet transform for 3D cell segmentation

    Science.gov (United States)

    Padfield, Dirk R.

    2015-03-01

    Wavelet approaches have proven effective in many segmentation applications and in particular in the segmentation of cells, which are blob-like in shape. We build upon an established wavelet segmentation algorithm and demonstrate how to overcome some of its limitations based on the theoretical derivation of the compounding process of iterative convolutions. We demonstrate that the wavelet decomposition can be computed for any desired level directly without iterative decompositions that require additional computation and memory. This is especially important when dealing with large 3D volumes that consume significant amounts of memory and require intense computation. Our approach is generalized to automatically handle both 2D and 3D and also implicitly handles the anisotropic pixel size inherent in such datasets. Our results demonstrate a 28X improvement in speed and 8X improvement in memory efficiency for standard size 3D confocal image volumes without adversely affecting the accuracy.

  19. 3D Segmentations of Neuronal Nuclei from Confocal Microscope Image Stacks

    Directory of Open Access Journals (Sweden)

    Antonio eLaTorre

    2013-12-01

    Full Text Available In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells. We have tested our algorithm in a real scenario --- the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei.

  20. 3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images.

    Science.gov (United States)

    Pei, Yuru; Ai, Xingsheng; Zha, Hongbin; Xu, Tianmin; Ma, Gengyu

    2016-09-01

    Tooth segmentation is an essential step in acquiring patient-specific dental geometries from cone-beam computed tomography (CBCT) images. Tooth segmentation from CBCT images is still a challenging task considering the comparatively low image quality caused by the limited radiation dose, as well as structural ambiguities from intercuspation and nearby alveolar bones. The goal of this paper is to present and discuss the latest accomplishments in semisupervised tooth segmentation with adaptive 3D shape constraints. The authors propose a 3D exemplar-based random walk method of tooth segmentation from CBCT images. The proposed method integrates semisupervised label propagation and regularization by 3D exemplar registration. To begin with, the pure random walk method is to get an initial segmentation of the teeth, which tends to be erroneous because of the structural ambiguity of CBCT images. And then, as an iterative refinement, the authors conduct a regularization by using 3D exemplar registration, as well as label propagation by random walks with soft constraints, to improve the tooth segmentation. In the first stage of the iteration, 3D exemplars with well-defined topologies are adapted to fit the tooth contours, which are obtained from the random walks based segmentation. The soft constraints on voxel labeling are defined by shape-based foreground dentine probability acquired by the exemplar registration, as well as the appearance-based probability from a support vector machine (SVM) classifier. In the second stage, the labels of the volume-of-interest (VOI) are updated by the random walks with soft constraints. The two stages are optimized iteratively. Instead of the one-shot label propagation in the VOI, an iterative refinement process can achieve a reliable tooth segmentation by virtue of exemplar-based random walks with adaptive soft constraints. The proposed method was applied for tooth segmentation of twenty clinically captured CBCT images. Three metrics

  1. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-07-15

    different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

  2. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-07-01

    prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

  3. Automatic 3D segmentation of individual facial muscles using unlabeled prior information.

    Science.gov (United States)

    Rezaeitabar, Yousef; Ulusoy, Ilkay

    2012-01-01

    Segmentation of facial soft tissues is required for surgical planning and evaluation, but this is laborious using manual methods and has been difficult to achieve with digital segmentation methods. A new automatic 3D segmentation method for facial soft tissues in magnetic resonance imaging (MRI) images was designed, implemented, and tested. A region growing algorithm based on local energy functions, using intensity similarities among neighboring regions as criteria, was developed. This local energy function includes the neighborhood relationships not only in the same dataset but from other training datasets. This approach differs from previous studies where the prior information was represented as manual segmented atlases. In this study, a consensus of many datasets, none of which was labeled a priori, is used to guide the segmentation. The method was tested in MRI scans for adult facial structures. MRI scans were obtained from the Alzheimer's disease neuroimaging initiative database. Comparison was made to results from expert manual segmentation and region growing techniques. The volumetric overlap between automated 3D segmentation results and the ground truth was 82.6% for masseter and 78.8% for temporalis tissues. A new automated method to segment various facial soft tissues was implemented and the results compared with standard region growing results. The proposed method shows 3.9% improvement in accuracy over the standard method. Reduction in segmentation errors was consistently achieved in MRI scans.

  4. Atlas-based segmentation of abdominal organs in 3D ultrasound, and its application in automated kidney segmentation.

    Science.gov (United States)

    Marsousi, Mahdi; Plataniotis, Konstantinos N; Stergiopoulos, Stergios

    2015-01-01

    Automated segmentation of abdominal organs in 3D ultrasound images is an important and challenging task toward computer assisted emergency diagnosis. However, speckle noise, low-contrast organ tissues, intensity-profile inhomogeneity, and partial organ visibility are some ultrasound challenges which limits the utility of the automated diagnosis solutions. In this paper, an atlas-based method to automatically segment an organ of interest in abdominal 3D ultrasound images is proposed. The atlas model contains texture information and shape knowledge of the organ, which facilitates an accurate discrimination of organ from non-organ voxels in input 3D ultrasound images. The proposed method offers a mechanism to automatically detect the organ, and therefore, it eliminates the need of manual initialization of organ segmentation. The proposed method is applied to automatically segment the right kidney in 3D ultrasound images. The experimental results indicate that the proposed method provides a higher detection and segmentation accuracy compared to state-of-the-art.

  5. 3D segmentation of kidney tumors from freehand 2D ultrasound

    Science.gov (United States)

    Ahmad, Anis; Cool, Derek; Chew, Ben H.; Pautler, Stephen E.; Peters, Terry M.

    2006-03-01

    To completely remove a tumor from a diseased kidney, while minimizing the resection of healthy tissue, the surgeon must be able to accurately determine its location, size and shape. Currently, the surgeon mentally estimates these parameters by examining pre-operative Computed Tomography (CT) images of the patient's anatomy. However, these images do not reflect the state of the abdomen or organ during surgery. Furthermore, these images can be difficult to place in proper clinical context. We propose using Ultrasound (US) to acquire images of the tumor and the surrounding tissues in real-time, then segmenting these US images to present the tumor as a three dimensional (3D) surface. Given the common use of laparoscopic procedures that inhibit the range of motion of the operator, we propose segmenting arbitrarily placed and oriented US slices individually using a tracked US probe. Given the known location and orientation of the US probe, we can assign 3D coordinates to the segmented slices and use them as input to a 3D surface reconstruction algorithm. We have implemented two approaches for 3D segmentation from freehand 2D ultrasound. Each approach was evaluated on a tissue-mimicking phantom of a kidney tumor. The performance of our approach was determined by measuring RMS surface error between the segmentation and the known gold standard and was found to be below 0.8 mm.

  6. Multi-Camera Sensor System for 3D Segmentation and Localization of Multiple Mobile Robots

    Science.gov (United States)

    Losada, Cristina; Mazo, Manuel; Palazuelos, Sira; Pizarro, Daniel; Marrón, Marta

    2010-01-01

    This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence. PMID:22319297

  7. 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning

    Science.gov (United States)

    Yang, Xiaofeng; Fei, Baowei

    2012-02-01

    We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 +/- 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.

  8. 3D-SIFT-Flow for atlas-based CT liver image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Yan, E-mail: xuyan04@gmail.com [State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191, China and Research Institute of Beihang University in Shenzhen and Microsoft Research, Beijing 100080 (China); Xu, Chenchao, E-mail: chenchaoxu33@gmail.com; Kuang, Xiao, E-mail: kuangxiao.ace@gmail.com [School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 (China); Wang, Hongkai, E-mail: wang.hongkai@gmail.com [Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024 (China); Chang, Eric I-Chao, E-mail: eric.chang@microsoft.com [Microsoft Research, Beijing 100080 (China); Huang, Weimin, E-mail: wmhuang@i2r.a-star.edu.sg [Institute for Infocomm Research (I2R), Singapore 138632 (Singapore); Fan, Yubo, E-mail: yubofan@buaa.edu.cn [Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191 (China)

    2016-05-15

    Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

  9. Adaptive Kalman snake for semi-autonomous 3D vessel tracking.

    Science.gov (United States)

    Lee, Sang-Hoon; Lee, Sanghoon

    2015-10-01

    In this paper, we propose a robust semi-autonomous algorithm for 3D vessel segmentation and tracking based on an active contour model and a Kalman filter. For each computed tomography angiography (CTA) slice, we use the active contour model to segment the vessel boundary and the Kalman filter to track position and shape variations of the vessel boundary between slices. For successful segmentation via active contour, we select an adequate number of initial points from the contour of the first slice. The points are set manually by user input for the first slice. For the remaining slices, the initial contour position is estimated autonomously based on segmentation results of the previous slice. To obtain refined segmentation results, an adaptive control spacing algorithm is introduced into the active contour model. Moreover, a block search-based initial contour estimation procedure is proposed to ensure that the initial contour of each slice can be near the vessel boundary. Experiments were performed on synthetic and real chest CTA images. Compared with the well-known Chan-Vese (CV) model, the proposed algorithm exhibited better performance in segmentation and tracking. In particular, receiver operating characteristic analysis on the synthetic and real CTA images demonstrated the time efficiency and tracking robustness of the proposed model. In terms of computational time redundancy, processing time can be effectively reduced by approximately 20%. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  10. 3D prostate TRUS segmentation using globally optimized volume-preserving prior.

    Science.gov (United States)

    Qiu, Wu; Rajchl, Martin; Guo, Fumin; Sun, Yue; Ukwatta, Eranga; Fenster, Aaron; Yuan, Jing

    2014-01-01

    An efficient and accurate segmentation of 3D transrectal ultrasound (TRUS) images plays an important role in the planning and treatment of the practical 3D TRUS guided prostate biopsy. However, a meaningful segmentation of 3D TRUS images tends to suffer from US speckles, shadowing and missing edges etc, which make it a challenging task to delineate the correct prostate boundaries. In this paper, we propose a novel convex optimization based approach to extracting the prostate surface from the given 3D TRUS image, while preserving a new global volume-size prior. We, especially, study the proposed combinatorial optimization problem by convex relaxation and introduce its dual continuous max-flow formulation with the new bounded flow conservation constraint, which results in an efficient numerical solver implemented on GPUs. Experimental results using 12 patient 3D TRUS images show that the proposed approach while preserving the volume-size prior yielded a mean DSC of 89.5% +/- 2.4%, a MAD of 1.4 +/- 0.6 mm, a MAXD of 5.2 +/- 3.2 mm, and a VD of 7.5% +/- 6.2% in - 1 minute, deomonstrating the advantages of both accuracy and efficiency. In addition, the low standard deviation of the segmentation accuracy shows a good reliability of the proposed approach.

  11. Segmentation of the heart muscle in 3-D pediatric echocardiographic images.

    NARCIS (Netherlands)

    Nillesen, M.M.; Lopata, R.G.P.; Gerrits, I.H.; Kapusta, L.; Huisman, H.J.; Thijssen, J.M.; Korte, C.L. de

    2007-01-01

    This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from

  12. Chestwall segmentation in 3D breast ultrasound using a deformable volume model.

    NARCIS (Netherlands)

    Huisman, H.J.; Karssemeijer, N.

    2007-01-01

    A deformable volume segmentation method is proposed to detect the breast parenchyma in frontal scanned 3D whole breast ultrasound. Deformable volumes are a viable alternative to the deformable surface paradigm in noisy images with poorly defined object boundaries. A deformable ultrasound volume

  13. A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction

    Directory of Open Access Journals (Sweden)

    Yiming Yan

    2017-01-01

    Full Text Available In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM, which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed ‘occlusions of random textures model’ are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images.

  14. 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation

    OpenAIRE

    Patravali, Jay; Jain, Shubham; Chilamkurthy, Sasank

    2017-01-01

    In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR images in End Diastole and End Systole phase. We show that both our segmentation models achieve near state-of-the-art performance scores in terms of distance metrics and have convincing accuracy in te...

  15. MRI Slice Segmentation and 3D Modelling of Temporomandibular Joint Measured by Microscopic Coil

    Science.gov (United States)

    Smirg, O.; Liberda, O.; Smekal, Z.; Sprlakova-Pukova, A.

    2012-01-01

    The paper focuses on the segmentation of magnetic resonance imaging (MRI) slices and 3D modelling of the temporomandibular joint disc in order to help physicians diagnose patients with dysfunction of the temporomandibular joint (TMJ). The TMJ is one of the most complex joints in the human body. The most common joint dysfunction is due to the disc. The disc is a soft tissue, which in principle cannot be diagnosed by the CT method. Therefore, a 3D model is made from the MRI slices, which can image soft tissues. For the segmentation of the disc in individual slices a new method is developed based on spatial distribution and anatomical TMJ structure with automatic thresholding. The thresholding is controlled by a genetic algorithm. The 3D model is realized using the marching cube method.

  16. 3D segmentation of the left ventricle combining long- and short-axis MR images.

    Science.gov (United States)

    Säring, D; Relan, J; Groth, M; Müllerleile, K; Handels, H

    2009-01-01

    Segmentation of the left ventricle (LV) is required to quantify LV remodeling after myocardial infarction. Therefore spatiotemporal cine MR sequences including long-axis and short-axis images are acquired. In this paper a new segmentation method for fast and robust segmentation of the left ventricle is presented. The new approach considers the position of the mitral valve and the apex as well as the long-axis contours to generate a 3D LV surface model. The segmentation result can be checked and adjusted in the short-axis images. Finally quantitative parameters were extracted. For evaluation the LV was segmented in eight datasets of the same subject by two medical experts using a contour drawing tool and the new segmentation tool. The results of both methods were compared concerning interaction time and intra- and inter-observer variance. The presented segmentation method proved to be fast. The mean difference and standard deviation of all parameters are decreased. In case of intra-observer comparison e.g. the mean ESV difference is reduced from 8.8% to 0.5%. A semi-automatic LV segmentation method has been developed that combines long- and short-axis views. Using the presented approach the intra- and interobserver difference as well as the time for the segmentation process are decreased. So the semi-automatic segmentation using long- and short-axis information proved to be fast and robust for the quantification of LV mass and volume properties.

  17. VOXEL- AND GRAPH-BASED POINT CLOUD SEGMENTATION OF 3D SCENES USING PERCEPTUAL GROUPING LAWS

    Directory of Open Access Journals (Sweden)

    Y. Xu

    2017-05-01

    Full Text Available Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

  18. - and Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

    Science.gov (United States)

    Xu, Y.; Hoegner, L.; Tuttas, S.; Stilla, U.

    2017-05-01

    Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

  19. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

    Directory of Open Access Journals (Sweden)

    Seoungjae Cho

    2014-01-01

    Full Text Available A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.

  20. Sloped terrain segmentation for autonomous drive using sparse 3D point cloud.

    Science.gov (United States)

    Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Jeong, Young-Sik; Um, Kyhyun; Sim, Sungdae

    2014-01-01

    A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.

  1. Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images.

    Science.gov (United States)

    Stern, Darko; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2011-12-07

    Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evaluation of vertebral deformations are limited to measuring deformations in two-dimensional (2D) x-ray images. In this paper, we propose a method for quantitative description of vertebral body deformations by efficient modelling and segmentation of vertebral bodies in 3D. The deformations are evaluated from the parameters of a 3D superquadric model, which is initialized as an elliptical cylinder and then gradually deformed by introducing transformations that yield a more detailed representation of the vertebral body shape. After modelling the vertebral body shape with 25 clinically meaningful parameters and the vertebral body pose with six rigid body parameters, the 3D model is aligned to the observed vertebral body in the 3D image. The performance of the method was evaluated on 75 vertebrae from CT and 75 vertebrae from T(2)-weighted MR spine images, extracted from the thoracolumbar part of normal and pathological spines. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images, as the proposed 3D model is able to describe both normal and pathological vertebral body deformations. The method may therefore be used for initialization of whole vertebra segmentation or for quantitative measurement of vertebral body deformations.

  2. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

    Science.gov (United States)

    Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

    2012-07-01

    Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational

  3. Segmentation of 3d Models for Cultural Heritage Structural Analysis - Some Critical Issues

    Science.gov (United States)

    Gonizzi Barsanti, S.; Guidi, G.; De Luca, L.

    2017-08-01

    Cultural Heritage documentation and preservation has become a fundamental concern in this historical period. 3D modelling offers a perfect aid to record ancient buildings and artefacts and can be used as a valid starting point for restoration, conservation and structural analysis, which can be performed by using Finite Element Methods (FEA). The models derived from reality-based techniques, made up of the exterior surfaces of the objects captured at high resolution, are - for this reason - made of millions of polygons. Such meshes are not directly usable in structural analysis packages and need to be properly pre-processed in order to be transformed in volumetric meshes suitable for FEA. In addition, dealing with ancient objects, a proper segmentation of 3D volumetric models is needed to analyse the behaviour of the structure with the most suitable level of detail for the different sections of the structure under analysis. Segmentation of 3D models is still an open issue, especially when dealing with ancient, complicated and geometrically complex objects that imply the presence of anomalies and gaps, due to environmental agents such as earthquakes, pollution, wind and rain, or human factors. The aims of this paper is to critically analyse some of the different methodologies and algorithms available to segment a 3D point cloud or a mesh, identifying difficulties and problems by showing examples on different structures.

  4. SEGMENTATION OF 3D MODELS FOR CULTURAL HERITAGE STRUCTURAL ANALYSIS – SOME CRITICAL ISSUES

    Directory of Open Access Journals (Sweden)

    S. Gonizzi Barsanti

    2017-08-01

    Full Text Available Cultural Heritage documentation and preservation has become a fundamental concern in this historical period. 3D modelling offers a perfect aid to record ancient buildings and artefacts and can be used as a valid starting point for restoration, conservation and structural analysis, which can be performed by using Finite Element Methods (FEA. The models derived from reality-based techniques, made up of the exterior surfaces of the objects captured at high resolution, are - for this reason - made of millions of polygons. Such meshes are not directly usable in structural analysis packages and need to be properly pre-processed in order to be transformed in volumetric meshes suitable for FEA. In addition, dealing with ancient objects, a proper segmentation of 3D volumetric models is needed to analyse the behaviour of the structure with the most suitable level of detail for the different sections of the structure under analysis. Segmentation of 3D models is still an open issue, especially when dealing with ancient, complicated and geometrically complex objects that imply the presence of anomalies and gaps, due to environmental agents such as earthquakes, pollution, wind and rain, or human factors. The aims of this paper is to critically analyse some of the different methodologies and algorithms available to segment a 3D point cloud or a mesh, identifying difficulties and problems by showing examples on different structures.

  5. Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

    Science.gov (United States)

    Morris, Mark; Sellers, William I.

    2015-01-01

    Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints. PMID:25780778

  6. Intervertebral disc segmentation in MR images with 3D convolutional networks

    Science.gov (United States)

    Korez, Robert; Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2017-02-01

    The vertebral column is a complex anatomical construct, composed of vertebrae and intervertebral discs (IVDs) supported by ligaments and muscles. During life, all components undergo degenerative changes, which may in some cases cause severe, chronic and debilitating low back pain. The main diagnostic challenge is to locate the pain generator, and degenerated IVDs have been identified to act as such. Accurate and robust segmentation of IVDs is therefore a prerequisite for computer-aided diagnosis and quantification of IVD degeneration, and can be also used for computer-assisted planning and simulation in spinal surgery. In this paper, we present a novel fully automated framework for supervised segmentation of IVDs from three-dimensional (3D) magnetic resonance (MR) spine images. By considering global intensity appearance and local shape information, a landmark-based approach is first used for the detection of IVDs in the observed image, which then initializes the segmentation of IVDs by coupling deformable models with convolutional networks (ConvNets). For this purpose, a 3D ConvNet architecture was designed that learns rich high-level appearance representations from a training repository of IVDs, and then generates spatial IVD probability maps that guide deformable models towards IVD boundaries. By applying the proposed framework to 15 3D MR spine images containing 105 IVDs, quantitative comparison of the obtained against reference IVD segmentations yielded an overall mean Dice coefficient of 92.8%, mean symmetric surface distance of 0.4 mm and Hausdorff surface distance of 3.7 mm.

  7. 3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

    Directory of Open Access Journals (Sweden)

    Yankui Sun

    2016-03-01

    Full Text Available With the introduction of spectral-domain optical coherence tomography (SD-OCT, much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of three-dimensional (3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of datasets contains 97 OCT B-Scan images with a resolution of 496×512 (each B-Scan comprising 512 A-Scans containing 496 pixels; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.

  8. Automatic renal segmentation for MR urography using 3D-GrabCut and random forests.

    Science.gov (United States)

    Yoruk, Umit; Hargreaves, Brian A; Vasanawala, Shreyas S

    2018-03-01

    To introduce and evaluate a fully automated renal segmentation technique for glomerular filtration rate (GFR) assessment in children. An image segmentation method based on iterative graph cuts (GrabCut) was modified to work on time-resolved 3D dynamic contrast-enhanced MRI data sets. A random forest classifier was trained to further segment the renal tissue into cortex, medulla, and the collecting system. The algorithm was tested on 26 subjects and the segmentation results were compared to the manually drawn segmentation maps using the F1-score metric. A two-compartment model was used to estimate the GFR of each subject using both automatically and manually generated segmentation maps. Segmentation maps generated automatically showed high similarity to the manually drawn maps for the whole-kidney (F1 = 0.93) and renal cortex (F1 = 0.86). GFR estimations using whole-kidney segmentation maps from the automatic method were highly correlated (Spearman's ρ = 0.99) to the GFR values obtained from manual maps. The mean GFR estimation error of the automatic method was 2.98 ± 0.66% with an average segmentation time of 45 s per patient. The automatic segmentation method performs as well as the manual segmentation for GFR estimation and reduces the segmentation time from several hours to 45 s. Magn Reson Med 79:1696-1707, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  9. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

    Science.gov (United States)

    Zhou, Xiangrong; Takayama, Ryosuke; Wang, Song; Hara, Takeshi; Fujita, Hiroshi

    2017-10-01

    We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. We propose a single network based on pixel-to-label deep learning to address the challenging

  10. 3D video bit rate adaptation decision taking using ambient illumination context

    Directory of Open Access Journals (Sweden)

    G. Nur Yilmaz

    2014-09-01

    Full Text Available 3-Dimensional (3D video adaptation decision taking is an open field in which not many researchers have carried out investigations yet compared to 3D video display, coding, etc. Moreover, utilizing ambient illumination as an environmental context for 3D video adaptation decision taking has particularly not been studied in literature to date. In this paper, a user perception model, which is based on determining perception characteristics of a user for a 3D video content viewed under a particular ambient illumination condition, is proposed. Using the proposed model, a 3D video bit rate adaptation decision taking technique is developed to determine the adapted bit rate for the 3D video content to maintain 3D video quality perception by considering the ambient illumination condition changes. Experimental results demonstrate that the proposed technique is capable of exploiting the changes in ambient illumination level to use network resources more efficiently without sacrificing the 3D video quality perception.

  11. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

    Science.gov (United States)

    Chen, Hao; Dou, Qi; Yu, Lequan; Qin, Jing; Heng, Pheng-Ann

    2017-04-23

    Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain

  12. A shape prior-based MRF model for 3D masseter muscle segmentation

    Science.gov (United States)

    Majeed, Tahir; Fundana, Ketut; Lüthi, Marcel; Beinemann, Jörg; Cattin, Philippe

    2012-02-01

    Medical image segmentation is generally an ill-posed problem that can only be solved by incorporating prior knowledge. The ambiguities arise due to the presence of noise, weak edges, imaging artifacts, inhomogeneous interior and adjacent anatomical structures having similar intensity profile as the target structure. In this paper we propose a novel approach to segment the masseter muscle using the graph-cut incorporating additional 3D shape priors in CT datasets, which is robust to noise; artifacts; and shape deformations. The main contribution of this paper is in translating the 3D shape knowledge into both unary and pairwise potentials of the Markov Random Field (MRF). The segmentation task is casted as a Maximum-A-Posteriori (MAP) estimation of the MRF. Graph-cut is then used to obtain the global minimum which results in the segmentation of the masseter muscle. The method is tested on 21 CT datasets of the masseter muscle, which are noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. the very common dental fillings and dental implants. We show that the proposed technique produces clinically acceptable results to the challenging problem of muscle segmentation, and further provide a quantitative and qualitative comparison with other methods. We statistically show that adding additional shape prior into both unary and pairwise potentials can increase the robustness of the proposed method in noisy datasets.

  13. Fast Streaming 3D Level set Segmentation on the GPU for Smooth Multi-phase Segmentation

    DEFF Research Database (Denmark)

    Sharma, Ojaswa; Zhang, Qin; Anton, François

    2011-01-01

    Level set method based segmentation provides an efficient tool for topological and geometrical shape handling, but it is slow due to high computational burden. In this work, we provide a framework for streaming computations on large volumetric images on the GPU. A streaming computational model...

  14. Automatic 3D graph cuts for brain cortex segmentation in patients with focal cortical dysplasia.

    Science.gov (United States)

    Despotović, Ivana; Segers, Ief; Platisa, Ljiljana; Vansteenkiste, Ewout; Pizurica, Aleksandra; Deblaere, Karel; Philips, Wilfried

    2011-01-01

    In patients with intractable epilepsy, focal cortical dysplasia (FCD) is the most frequent malformation of cortical development. Identification of subtle FCD lesions using brain MRI scans is very often based on the cortical thickness measurement, where brain cortex segmentation is required as a preprocessing step. However, the accuracy of the selected segmentation method can highly affect the final FCD lesion detection. In this work, we propose an improved graph cuts algorithm integrating Markov random field-based energy function for more accurate brain cortex MRI segmentation. Our method uses three-label graph cuts and preforms automatic 3D MRI brain cortex segmentation integrating intensity and boundary information. The performance of the method is tested on both simulated MR brain images with different noise levels and real patients with FCD lesions. Experimental quantitative segmentation results showed that the proposed method is effective, robust to noise and achieves higher accuracy than other popular brain MRI segmentation methods. The qualitative validation, visually verified by a medical expert, showed that the FCD lesions were segmented well as a part of the cortex, indicating increased thickness and cortical deformation. The proposed technique can be successfully used in this, as well as in many other clinical applications.

  15. Biview learning for human posture segmentation from 3D points cloud.

    Directory of Open Access Journals (Sweden)

    Maoying Qiao

    Full Text Available Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF and relative position features (RPF. Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation.

  16. Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

    Directory of Open Access Journals (Sweden)

    Sungdae Sim

    2012-12-01

    Full Text Available Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

  17. Application-adapted mobile 3D video coding and streaming — A survey

    Science.gov (United States)

    Liu, Yanwei; Ci, Song; Tang, Hui; Ye, Yun

    2012-03-01

    3D video technologies have been gradually matured to be moved into mobile platforms. In the mobile environments, the specific characteristics of wireless network and mobile device present great challenges for 3D video coding and streaming. The application-adapted mobile 3D video coding and streaming technologies are urgently needed. Based on the mobile 3D video application framework, this paper reviews the state-of-the-art technologies of mobile 3D video coding and streaming. Specifically, the mobile 3D video formats and the corresponding coding methods are firstly reviewed and then the streaming adaptation technologies including 3D video transcoding, 3D video rate control and cross-layer optimized 3D video streaming are surveyed. [Figure not available: see fulltext.

  18. Dual optimization based prostate zonal segmentation in 3D MR images.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron

    2014-05-01

    Efficient and accurate segmentation of the prostate and two of its clinically meaningful sub-regions: the central gland (CG) and peripheral zone (PZ), from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, a novel multi-region segmentation approach is proposed to simultaneously segment the prostate and its two major sub-regions from only a single 3D T2-weighted (T2w) MR image, which makes use of the prior spatial region consistency and incorporates a customized prostate appearance model into the segmentation task. The formulated challenging combinatorial optimization problem is solved by means of convex relaxation, for which a novel spatially continuous max-flow model is introduced as the dual optimization formulation to the studied convex relaxed optimization problem with region consistency constraints. The proposed continuous max-flow model derives an efficient duality-based algorithm that enjoys numerical advantages and can be easily implemented on GPUs. The proposed approach was validated using 18 3D prostate T2w MR images with a body-coil and 25 images with an endo-rectal coil. Experimental results demonstrate that the proposed method is capable of efficiently and accurately extracting both the prostate zones: CG and PZ, and the whole prostate gland from the input 3D prostate MR images, with a mean Dice similarity coefficient (DSC) of 89.3±3.2% for the whole gland (WG), 82.2±3.0% for the CG, and 69.1±6.9% for the PZ in 3D body-coil MR images; 89.2±3.3% for the WG, 83.0±2.4% for the CG, and 70.0±6.5% for the PZ in 3D endo-rectal coil MR images. In addition, the experiments of intra- and inter-observer variability introduced by user initialization indicate a good reproducibility of the proposed approach in terms of volume difference (VD) and coefficient-of-variation (CV) of DSC. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

    Science.gov (United States)

    Patel, Ajay; van de Leemput, Sil C.; Prokop, Mathias; van Ginneken, Bram; Manniesing, Rashindra

    2017-03-01

    Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 +/- 0.01 and mean absolute volume difference of 4.77 +/- 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

  20. Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters.

    Science.gov (United States)

    Schneider, Matthias; Hirsch, Sven; Weber, Bruno; Székely, Gábor; Menze, Bjoern H

    2015-01-01

    We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Improved intracranial lesion characterization by tissue segmentation based on a 3D feature map.

    Science.gov (United States)

    Vinitski, S; Gonzalez, C; Mohamed, F; Iwanaga, T; Knobler, R L; Khalili, K; Mack, J

    1997-03-01

    Our aim was to develop an accurate multispectral tissue segmentation method based on 3D feature maps. We utilized proton density (PD), T2-weighted fast spin-echo (FSE), and T1-weighted spin-echo images as inputs for segmentation. Phantom constructs, cadaver brains, an animal brain tumor model and both normal human brains and those from patients with either multiple sclerosis (MS) or primary brain tumors were analyzed with this technique. Initially, misregistration, RF inhomogeneity and image noise problems were addressed. Next, a qualified observer identified samples representing the tissues of interest. Finally, k-nearest neighbor algorithm (k-NN) was utilized to create a stack of color-coded segmented images. The inclusion of T1 based images, as a third input, produced significant improvement in the delineation of tissues. In MS, our 3D technique was found to be far superior to that based on any combination of 2D feature maps (P lesions within the same MS plaque, representing different stages of the disease process. Further, we obtained the regional distribution of MS lesion burden and followed its changes over time. Neuropsychological aberrations were the clinical counterpart of the structural changes detected in segmentation. We could also delineate the margins of benign brain tumors. In malignant tumors, up to four abnormal tissues were identified: 1) a solid tumor core, 2) a cystic component, 3) edema in the white matter, and 4) areas of necrosis and hemorrhage. Subsequent neurosurgical exploration confirmed the distribution of tissues as predicted by this analysis.

  2. Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors

    Directory of Open Access Journals (Sweden)

    Andrés Ortiz

    2013-01-01

    Full Text Available Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE and has been assessed using real brain images from the Internet Brain Image Repository (IBSR.

  3. Multivariate statistical analysis as a tool for the segmentation of 3D spectral data.

    Science.gov (United States)

    Lucas, G; Burdet, P; Cantoni, M; Hébert, C

    2013-01-01

    Acquisition of three-dimensional (3D) spectral data is nowadays common using many different microanalytical techniques. In order to proceed to the 3D reconstruction, data processing is necessary not only to deal with noisy acquisitions but also to segment the data in term of chemical composition. In this article, we demonstrate the value of multivariate statistical analysis (MSA) methods for this purpose, allowing fast and reliable results. Using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) coupled with a focused ion beam (FIB), a stack of spectrum images have been acquired on a sample produced by laser welding of a nickel-titanium wire and a stainless steel wire presenting a complex microstructure. These data have been analyzed using principal component analysis (PCA) and factor rotations. PCA allows to significantly improve the overall quality of the data, but produces abstract components. Here it is shown that rotated components can be used without prior knowledge of the sample to help the interpretation of the data, obtaining quickly qualitative mappings representative of elements or compounds found in the material. Such abundance maps can then be used to plot scatter diagrams and interactively identify the different domains in presence by defining clusters of voxels having similar compositions. Identified voxels are advantageously overlaid on secondary electron (SE) images with higher resolution in order to refine the segmentation. The 3D reconstruction can then be performed using available commercial softwares on the basis of the provided segmentation. To asses the quality of the segmentation, the results have been compared to an EDX quantification performed on the same data. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI.

    Science.gov (United States)

    Kurzendorfer, Tanja; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas; Brost, Alexander

    2017-07-01

    The current challenge for electrophysiology procedures, targeting the left ventricle, is the localization and qualification of myocardial scar. Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the current gold standard to visualize regions of myocardial infarction. Commonly, a stack of 2-D images is acquired of the left ventricle in short-axis orientation. Recently, 3-D LGE-MRI methods were proposed that continuously cover the whole heart with a high resolution within a single acquisition. The acquisition promises an accurate quantification of the myocardium to the extent of myocardial scarring. The major challenge arises in the analysis of the resulting images, as the accurate segmentation of the myocardium is a requirement for a precise scar tissue quantification. In this work, we propose a novel approach for fully automatic left ventricle segmentation in 3-D whole-heart LGE-MRI, to address this limitation. First, a two-step registration is performed to initialize the left ventricle. In the next step, the principal components are computed and a pseudo short axis view of the left ventricle is estimated. The refinement of the endocardium and epicardium is performed in polar space. Prior knowledge for shape and inter-slice smoothness is used during segmentation. The proposed method was evaluated on 30 clinical 3-D LGE-MRI datasets from individual patients obtained at two different clinical sites and were compared to gold standard segmentations of two clinical experts. This comparison resulted in a Dice coefficient of 0.83 for the endocardium and 0.80 for the epicardium. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Analysis of 3D multi-segment lumbar spine motion during gait and prone hip extension.

    Science.gov (United States)

    Ryan, Nicholas; Bruno, Paul

    2017-04-01

    Modeling the lumbar spine as a single rigid segment does not consider the relative contribution of regional or segmental motion that may occur during a task. The current study used a multi-segment model to measure three-dimensional (3D) upper and lower lumbar spine motion during walking and prone hip extension (PHE). The degree of segmental redundancy during these movements was assessed by calculating the cross-correlation of the segmental angle time series (R0) and the correlation of the segmental ranges of motion (RROM). All correlation coefficients (R0, RROM) were interpreted as follows: very strong (0.80-1.00), strong (0.60-0.79), moderate (0.40-0.59), weak (0.20-0.39), and very weak (0.00-0.19). Strong/very strong positive R0 were demonstrated between the two segments in all three planes during PHE and in the transverse plane during walking. Weak/moderate R0 were demonstrated in the sagittal and frontal planes during walking. Strong/very strong positive RROM were demonstrated in the transverse plane during PHE, and moderate positive RROM was demonstrated in the sagittal plane during walking. Non-significant RROM were demonstrated for all other planes and movements. These results suggest the motion patterns of the upper and lower lumbar regions during walking and PHE are sufficiently distinct to warrant the use a multi-segment model for these movements. It also appears that the degree of redundancy between the upper and lower lumbar regions may be task-dependent. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Semi-automatic 3D segmentation of costal cartilage in CT data from Pectus Excavatum patients

    Science.gov (United States)

    Barbosa, Daniel; Queirós, Sandro; Rodrigues, Nuno; Correia-Pinto, Jorge; Vilaça, J.

    2015-03-01

    One of the current frontiers in the clinical management of Pectus Excavatum (PE) patients is the prediction of the surgical outcome prior to the intervention. This can be done through computerized simulation of the Nuss procedure, which requires an anatomically correct representation of the costal cartilage. To this end, we take advantage of the costal cartilage tubular structure to detect it through multi-scale vesselness filtering. This information is then used in an interactive 2D initialization procedure which uses anatomical maximum intensity projections of 3D vesselness feature images to efficiently initialize the 3D segmentation process. We identify the cartilage tissue centerlines in these projected 2D images using a livewire approach. We finally refine the 3D cartilage surface through region-based sparse field level-sets. We have tested the proposed algorithm in 6 noncontrast CT datasets from PE patients. A good segmentation performance was found against reference manual contouring, with an average Dice coefficient of 0.75±0.04 and an average mean surface distance of 1.69+/-0.30mm. The proposed method requires roughly 1 minute for the interactive initialization step, which can positively contribute to an extended use of this tool in clinical practice, since current manual delineation of the costal cartilage can take up to an hour.

  7. Preliminary results in large bone segmentation from 3D freehand ultrasound

    Science.gov (United States)

    Fanti, Zian; Torres, Fabian; Arámbula Cosío, Fernando

    2013-11-01

    Computer Assisted Orthopedic Surgery (CAOS) requires a correct registration between the patient in the operating room and the virtual models representing the patient in the computer. In order to increase the precision and accuracy of the registration a set of new techniques that eliminated the need to use fiducial markers have been developed. The majority of these newly developed registration systems are based on costly intraoperative imaging systems like Computed Tomography (CT scan) or Magnetic resonance imaging (MRI). An alternative to these methods is the use of an Ultrasound (US) imaging system for the implementation of a more cost efficient intraoperative registration solution. In order to develop the registration solution with the US imaging system, the bone surface is segmented in both preoperative and intraoperative images, and the registration is done using the acquire surface. In this paper, we present the a preliminary results of a new approach to segment bone surface from ultrasound volumes acquired by means 3D freehand ultrasound. The method is based on the enhancement of the voxels that belongs to surface and its posterior segmentation. The enhancement process is based on the information provided by eigenanalisis of the multiscale 3D Hessian matrix. The preliminary results shows that from the enhance volume the final bone surfaces can be extracted using a singular value thresholding.

  8. Lateral ventricle segmentation of 3D pre-term neonates US using convex optimization.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Kishimoto, Jessica; Ukwatta, Eranga; Fenster, Aaron

    2013-01-01

    Intraventricular hemorrhage (IVH) is a common disease among preterm infants with an occurrence of 12-20% in those born at less than 35 weeks gestational age. Neonates at risk of IVH are monitored by conventional 2D ultrasound (US) for hemorrhage and potential ventricular dilation. Compared to 2D US relying on linear measurements from a single slice and visually estimates to determine ventricular dilation, 3D US can provide volumetric ventricle measurements, more sensitive to longitudinal changes in ventricular volume. In this work, we propose a global optimization-based surface evolution approach to the segmentation of the lateral ventricles in preterm neonates with IVH. The proposed segmentation approach makes use of convex optimization technique in combination with a subject-specific shape model. We show that the introduced challenging combinatorial optimization problem can be solved globally by means of convex relaxation. In this regard, we propose a coupled continuous max-flow model, which derives a new and efficient dual based algorithm, that can be implemented on GPUs to achieve a high-performance in numerics. Experiments demonstrate the advantages of our approach in both accuracy and efficiency. To the best of our knowledge, this paper reports the first study on semi-automatic segmentation of lateral ventricles in neonates with IVH from 3D US images.

  9. Atlas-registration based image segmentation of MRI human thigh muscles in 3D space

    Science.gov (United States)

    Ahmad, Ezak; Yap, Moi Hoon; Degens, Hans; McPhee, Jamie S.

    2014-03-01

    Automatic segmentation of anatomic structures of magnetic resonance thigh scans can be a challenging task due to the potential lack of precisely defined muscle boundaries and issues related to intensity inhomogeneity or bias field across an image. In this paper, we demonstrate a combination framework of atlas construction and image registration methods to propagate the desired region of interest (ROI) between atlas image and the targeted MRI thigh scans for quadriceps muscles, femur cortical layer and bone marrow segmentations. The proposed system employs a semi-automatic segmentation method on an initial image in one dataset (from a series of images). The segmented initial image is then used as an atlas image to automate the segmentation of other images in the MRI scans (3-D space). The processes include: ROI labeling, atlas construction and registration, and morphological transform correspondence pixels (in terms of feature and intensity value) between the atlas (template) image and the targeted image based on the prior atlas information and non-rigid image registration methods.

  10. Segmental tracheal reconstruction by 3D-printed scaffold: Pivotal role of asymmetrically porous membrane.

    Science.gov (United States)

    Lee, Doh Young; Park, Su A; Lee, Sang Jin; Kim, Tae Ho; Oh, Se Heang; Lee, Jin Ho; Kwon, Seong Keun

    2016-09-01

    Three-dimensional (3D) printed scaffold for tracheal reconstruction can substitute the conventional treatment of tracheal stenosis. This study investigated the survival outcomes of segmental tracheal reconstruction using 3D printed polycaprolactone (PCL) scaffold with or without asymmetrically porous membrane in rabbit animal model. Animal study. Six mature New Zealand white rabbits were categorized into two groups (three animals for each) according to the procedures they received: tracheal reconstruction using 3D printed PCL scaffold without asymmetrically porous membrane (group 1) versus with asymmetrically porous membrane (group 2). We compared the endoscopic findings of tracheal lumen, radiologic assessment using microcomputed tomography (CT) scanner and histologic findings. Overall survival duration after procedure was compared in both groups. The survival of group 2 was longer than group 1 (21, 37, 46 days vs. 4, 10, 12 days, respectively). Although mucosal regeneration in tracheal lumen was not full enough in both groups, the patency was well maintained in group 2. Micro-CT and histologic analysis showed that there were tracheal narrowing in the whole length in group 1, whereas only the anastomosis site was stenotic in group 2. Asymmetrically porous membrane reinforced by 3D printed mesh is promising as a 360-degree tracheal substitute with comparable survival and luminal patency. Further study is necessary to minimize the narrowing of the anastomosis site and improve the mucosal regeneration for longer survival. NA. Laryngoscope, 126:E304-E309, 2016. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

  11. Automatic segmentation of teeth from dentomaxillofacial 3D-CT images.

    Science.gov (United States)

    Aizawa, Mitsuhiro; Nishikawa, Keiichi; Sasaki, Keita; Kobayashi, Norio; Yama, Mitsuru; Kakizawa, Takashi; Sano, Tsukasa; Murakami, Shinichi

    2010-04-20

    CT is an effective tool for image diagnosis because it enables noninvasive observation of internal organs. In the course of CT, 3D-CT, such as a helical scanning CT and a multi-detector row CT, has been developed. With the use of 3D-CT, organs can be observed from several viewing directions. Even now, however, 3D-CT images are generated by manual procedures to extract objective organs using the threshold method. These procedures waste time and effort. Therefore, development of highly automated and effective extracting techniques has been desired. The region growing (RG) method is one of the effective techniques of extracting internal organs. The conventional RG method, however, has some defects. Extracted regions are strongly affected by the threshold value for segmentation. A break point on a region contour yields a leakage of region. To overcome these defects, we formulated a modified region growing method with edge detection (MRGWED) which combines a three-dimensional region growing technique and an edge detection technique. Using the MRGWED, we tried to extract teeth from dentomaxillofacial 3D-CT image data.

  12. Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

    Science.gov (United States)

    Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

    2008-02-01

    Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

  13. Automated bone segmentation from large field of view 3D MR images of the hip joint

    Science.gov (United States)

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S.; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-10-01

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.

  14. Semantic segmentation of 3D textured meshes for urban scene analysis

    Science.gov (United States)

    Rouhani, Mohammad; Lafarge, Florent; Alliez, Pierre

    2017-01-01

    Classifying 3D measurement data has become a core problem in photogrammetry and 3D computer vision, since the rise of modern multiview geometry techniques, combined with affordable range sensors. We introduce a Markov Random Field-based approach for segmenting textured meshes generated via multi-view stereo into urban classes of interest. The input mesh is first partitioned into small clusters, referred to as superfacets, from which geometric and photometric features are computed. A random forest is then trained to predict the class of each superfacet as well as its similarity with the neighboring superfacets. Similarity is used to assign the weights of the Markov Random Field pairwise-potential and to account for contextual information between the classes. The experimental results illustrate the efficacy and accuracy of the proposed framework.

  15. Adaptation of a 3D prostate cancer atlas for transrectal ultrasound guided target-specific biopsy.

    Science.gov (United States)

    Narayanan, R; Werahera, P N; Barqawi, A; Crawford, E D; Shinohara, K; Simoneau, A R; Suri, J S

    2008-10-21

    Due to lack of imaging modalities to identify prostate cancer in vivo, current TRUS guided prostate biopsies are taken randomly. Consequently, many important cancers are missed during initial biopsies. The purpose of this study was to determine the potential clinical utility of a high-speed registration algorithm for a 3D prostate cancer atlas. This 3D prostate cancer atlas provides voxel-level likelihood of cancer and optimized biopsy locations on a template space (Zhan et al 2007). The atlas was constructed from 158 expert annotated, 3D reconstructed radical prostatectomy specimens outlined for cancers (Shen et al 2004). For successful clinical implementation, the prostate atlas needs to be registered to each patient's TRUS image with high registration accuracy in a time-efficient manner. This is implemented in a two-step procedure, the segmentation of the prostate gland from a patient's TRUS image followed by the registration of the prostate atlas. We have developed a fast registration algorithm suitable for clinical applications of this prostate cancer atlas. The registration algorithm was implemented on a graphical processing unit (GPU) to meet the critical processing speed requirements for atlas guided biopsy. A color overlay of the atlas superposed on the TRUS image was presented to help pick statistically likely regions known to harbor cancer. We validated our fast registration algorithm using computer simulations of two optimized 7- and 12-core biopsy protocols to maximize the overall detection rate. Using a GPU, patient's TRUS image segmentation and atlas registration took less than 12 s. The prostate cancer atlas guided 7- and 12-core biopsy protocols had cancer detection rates of 84.81% and 89.87% respectively when validated on the same set of data. Whereas the sextant biopsy approach without the utility of 3D cancer atlas detected only 70.5% of the cancers using the same histology data. We estimate 10-20% increase in prostate cancer detection rates

  16. Investigations on the quality of manual image segmentation in 3D radiotherapy planning; Untersuchungen zur Qualitaet der manuellen Bildsegmentierung in der 3D-Bestrahlungsplanung

    Energy Technology Data Exchange (ETDEWEB)

    Perelmouter, J. [Tuebingen Univ. (Germany). Abt. Strahlentherapie]|[Tuebingen Univ. (Germany). Inst. fuer Medizinische Psychologie und Verhaltensneurobiologie; Bohsung, J.; Nuesslin, F. [Tuebingen Univ. (Germany). Abt. fuer Medizinische Physik; Becker, G.; Kortmann, R.D.; Bamberg, M. [Tuebingen Univ. (Germany). Abt. Strahlentherapie

    1998-12-31

    In 3D radiotherapy planning image segmentation plays an important role in the definition process of target volume and organs at risk. Here, we present a method to quantify the technical precision of the manual image segmentation process. To validate our method we developed a virtual phantom consisting of several geometrical objects of changing form and contrast, which should be contoured by volunteers using the TOMAS tool for manual segmentation of the Heidelberg VOXELPLAN system. The results of this examination are presented. (orig.) [Deutsch] Die Segmentierung von Schnittbildserien spielt bei der Definition von Zielvolumen und Risikoorganen fuer die 3D-Bestrahlungsplanung eine grosse Rolle. In dieser Arbeit wird eine Methode zur Quantifizierung der rein technisch bedingten Ungenauigkeit bei der manuellen Bildsegmentierung beschrieben. Zur Validierung dieser Methode wurde ein Graustufenphantom mit verschiedenen geometrischen Figuren unterschiedlichen Kontrastes entwickelt, die von verschiedenen Versuchspersonen mit Hilfe des Segmentierungsprogramms TOMAS des Heidelberger VOXELPLAN-Systems konturiert wurden. Ueber die erzielten Ergebnisse wird berichtet. (orig.)

  17. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

    Science.gov (United States)

    Fechter, Tobias; Adebahr, Sonja; Baltas, Dimos; Ben Ayed, Ismail; Desrosiers, Christian; Dolz, Jose

    2017-12-01

    Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 ± 0.11, an average symmetric square distance of 1.36 ± 0.90 mm, and an average Hausdorff distance of 11.68 ± 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. We show that a CNN can yield accurate estimations of esophagus location, and that

  18. 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

    Science.gov (United States)

    Dolz, Jose; Desrosiers, Christian; Ben Ayed, Ismail

    2017-04-24

    This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We address the problem via small kernels, allowing deeper architectures. We further model both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. Our model is efficiently trained end-to-end on a graphics processing unit (GPU), in a single stage, exploiting the dense inference capabilities of fully CNNs. We performed comprehensive experiments over two publicly available datasets. First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then, we report a large-scale multi-site evaluation over 1112 unregistered subject datasets acquired from 17 different sites (ABIDE dataset), with ages ranging from 7 to 64 years, showing that our method is robust to various acquisition protocols, demographics and clinical factors. Our method yielded segmentations that are highly consistent with a standard atlas-based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps. This makes it convenient for massive multi-site neuroanatomical imaging studies. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Lung lobe segmentation by graph search with 3D shape constraints

    Science.gov (United States)

    Zhang, Li; Hoffman, Eric A.; Reinhardt, Joseph M.

    2001-05-01

    The lung lobes are natural units for reporting image-based measurements of the respiratory system. Lobar segmentation can also be used in pulmonary image processing to guide registration and drive additional segmentation. We have developed a 3D shape-constrained lobar segmentation technique for volumetric pulmonary CT images. The method consists of a search engine and shape constraints that work together to detect lobar fissures using gray level information and anatomic shape characteristics in two steps: (1) a coarse localization step, (2) a fine tuning step. An error detecting mechanism using shape constraints is used in our method to correct erroneous search results. Our method has been tested in four subjects, and the results are compared to manually traced results. The average RMS difference between the manual results and shape-constrained segmentation results is 2.23 mm. We further validated our method by evaluating the repeatability of lobar volumes measured from repeat scans of the same subject. We compared lobar air and tissue volume variations to show that most of the lobar volume variations are due to difference in air volume scan to scan.

  20. TU-F-BRF-06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Gou, S; Rapacchi, S; Hu, P; Sheng, K [UCLA School of Medicine, Los Angeles, CA (United States)

    2014-06-15

    Purpose: The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC). Methods: Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean-shift clustering and K-SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods. Results: DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over-segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient. Conclusion: We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment.

  1. A 3-D Active Contour Method for Automated Segmentation of the Left Ventricle From Magnetic Resonance Images.

    Science.gov (United States)

    Hajiaghayi, Mahdi; Groves, Elliott M; Jafarkhani, Hamid; Kheradvar, Arash

    2017-01-01

    This study's objective is to develop and validate a fast automated 3-D segmentation method for cardiac magnetic resonance imaging (MRI). The segmentation algorithm automatically reconstructs cardiac MRI DICOM data into a 3-D model (i.e., direct volumetric segmentation), without relying on prior statistical knowledge. A novel 3-D active contour method was employed to detect the left ventricular cavity in 33 subjects with heterogeneous heart diseases from the York University database. Papillary muscles were identified and added to the chamber using a convex hull of the left ventricle and interpolation. The myocardium was then segmented using a similar 3-D segmentation method according to anatomic information. A multistage approach was taken to determine the method's efficacy. Our method demonstrated a significant improvement in segmentation performance when compared to manual segmentation and other automated methods. A true 3-D reconstruction technique without the need for training datasets or any user-driven segmentation has been developed. In this method, a novel combination of internal and external energy terms for active contour was utilized that exploits histogram matching for improving the segmentation performance. This method takes advantage of full volumetric imaging, does not rely on prior statistical knowledge, and employs a convex-hull interpolation to include the papillary muscles.

  2. Whole 3D shape reconstruction of vascular segments under pressure via fringe projection techniques

    Science.gov (United States)

    Genovese, Katia; Pappalettere, Carmine

    2006-12-01

    Understanding and modelling vascular wall mechanics is a primary issue in the study of circulatory diseases. Although theoretical and numerical studies on arteries compliance are continuously increasing, relatively little work has been documented on the use of non-invasive imaging techniques for monitoring 3D vascular wall deformations. Usually, 2D video dimension analyzer (VDA) systems recover diameter and length variations during inflation/extension tests by tracking position changes of few markers put on the blood vessel surface. Then, strain determination relies on the assumption of axisymmetric deformations. However, more rigorous evaluations of whole wall deformation map are required for properly modelling the highly anisotropic and inhomogeneous vascular tissue mechanical response. This paper describes the development and application of a fringe projection (FP)-based procedure for the 360° 3D shape reconstruction of tubular samples subjected to internal pressure. A specially designed fixture for mounting and inflating the tubular segment allows specimen rotation about its axis. Movement is controlled by a high-precision rotational stage. This yields accurate positioning of the surface to be investigated with respect to the viewing direction. Data point clouds obtained from multiple recorded images are then processed and merged in a CAD environment, thus providing the whole shape of the sample with very high spatial resolution. The entire procedure has successfully been applied to latex specimens and porcine vascular segments. Further improvements will make the present procedure suitable for in vitro tests under more closely reproduced physiological conditions.

  3. Segmentation, Reconstruction, and Analysis of Blood Thrombus Formation in 3D 2-Photon Microscopy Images

    Directory of Open Access Journals (Sweden)

    Xu Zhiliang

    2010-01-01

    Full Text Available We study the problem of segmenting, reconstructing, and analyzing the structure growth of thrombi (clots in blood vessels in vivo based on 2-photon microscopic image data. First, we develop an algorithm for segmenting clots in 3D microscopic images based on density-based clustering and methods for dealing with imaging artifacts. Next, we apply the union-of-balls (or alpha-shape algorithm to reconstruct the boundary of clots in 3D. Finally, we perform experimental studies and analysis on the reconstructed clots and obtain quantitative data of thrombus growth and structures. We conduct experiments on laser-induced injuries in vessels of two types of mice (the wild type and the type with low levels of coagulation factor VII and analyze and compare the developing clot structures based on their reconstructed clots from image data. The results we obtain are of biomedical significance. Our quantitative analysis of the clot composition leads to better understanding of the thrombus development, and is valuable to the modeling and verification of computational simulation of thrombogenesis.

  4. CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

    Science.gov (United States)

    2013-01-01

    The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening. PMID:23938087

  5. Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

    Science.gov (United States)

    Lu, Fang; Wu, Fa; Hu, Peijun; Peng, Zhiyi; Kong, Dexing

    2017-02-01

    Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

  6. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

    Science.gov (United States)

    Valverde, Sergi; Cabezas, Mariano; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Oliver, Arnau; Lladó, Xavier

    2017-07-15

    In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Pancreas segmentation from 3D abdominal CT images using patient-specific weighted subspatial probabilistic atlases

    Science.gov (United States)

    Karasawa, Kenichi; Oda, Masahiro; Hayashi, Yuichiro; Nimura, Yukitaka; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Rueckert, Daniel; Mori, Kensaku

    2015-03-01

    Abdominal organ segmentations from CT volumes are now widely used in the computer-aided diagnosis and surgery assistance systems. Among abdominal organs, the pancreas is especially difficult to segment because of its large individual differences of the shape and position. In this paper, we propose a new pancreas segmentation method from 3D abdominal CT volumes using patient-specific weighted-subspatial probabilistic atlases. First of all, we perform normalization of organ shapes in training volumes and an input volume. We extract the Volume Of Interest (VOI) of the pancreas from the training volumes and an input volume. We divide each training VOI and input VOI into some cubic regions. We use a nonrigid registration method to register these cubic regions of the training VOI to corresponding regions of the input VOI. Based on the registration results, we calculate similarities between each cubic region of the training VOI and corresponding region of the input VOI. We select cubic regions of training volumes having the top N similarities in each cubic region. We subspatially construct probabilistic atlases weighted by the similarities in each cubic region. After integrating these probabilistic atlases in cubic regions into one, we perform a rough-to-precise segmentation of the pancreas using the atlas. The results of the experiments showed that utilization of the training volumes having the top N similarities in each cubic region led good results of the pancreas segmentation. The Jaccard Index and the average surface distance of the result were 58.9% and 2.04mm on average, respectively.

  8. Learning structured models for segmentation of 2-D and 3-D imagery.

    Science.gov (United States)

    Lucchi, Aurelien; Marquez-Neila, Pablo; Becker, Carlos; Li, Yunpeng; Smith, Kevin; Knott, Graham; Fua, Pascal

    2015-05-01

    Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal performance. A popular choice is to learn them using a large-margin framework and more specifically structured support vector machines (SSVM). Although SSVMs are appealing, they suffer from certain limitations. First, they are restricted in practice to linear kernels because the more powerful nonlinear kernels cause the learning to become prohibitively expensive. Second, they require iteratively finding the most violated constraints, which is often intractable for the loopy graphical models used in image segmentation. This requires approximation that can lead to reduced quality of learning. In this paper, we propose three novel techniques to overcome these limitations. We first introduce a method to "kernelize" the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring much additional computational cost. Moreover, we employ a working set of constraints to increase the reliability of approximate subgradient methods and introduce a new way to select a suitable step size at each iteration. We demonstrate the strength of our approach on both 2-D and 3-D electron microscopic (EM) image data and show consistent performance improvement over state-of-the-art approaches.

  9. Semi-automatic 3D lung nodule segmentation in CT using dynamic programming

    Science.gov (United States)

    Sargent, Dustin; Park, Sun Young

    2017-02-01

    We present a method for semi-automatic segmentation of lung nodules in chest CT that can be extended to general lesion segmentation in multiple modalities. Most semi-automatic algorithms for lesion segmentation or similar tasks use region-growing or edge-based contour finding methods such as level-set. However, lung nodules and other lesions are often connected to surrounding tissues, which makes these algorithms prone to growing the nodule boundary into the surrounding tissue. To solve this problem, we apply a 3D extension of the 2D edge linking method with dynamic programming to find a closed surface in a spherical representation of the nodule ROI. The algorithm requires a user to draw a maximal diameter across the nodule in the slice in which the nodule cross section is the largest. We report the lesion volume estimation accuracy of our algorithm on the FDA lung phantom dataset, and the RECIST diameter estimation accuracy on the lung nodule dataset from the SPIE 2016 lung nodule classification challenge. The phantom results in particular demonstrate that our algorithm has the potential to mitigate the disparity in measurements performed by different radiologists on the same lesions, which could improve the accuracy of disease progression tracking.

  10. Segmentation process significantly influences the accuracy of 3D surface models derived from cone beam computed tomography

    NARCIS (Netherlands)

    Fourie, Zacharias; Damstra, Janalt; Schepers, Rutger H; Gerrits, Pieter; Ren, Yijin

    AIMS: To assess the accuracy of surface models derived from 3D cone beam computed tomography (CBCT) with two different segmentation protocols. MATERIALS AND METHODS: Seven fresh-frozen cadaver heads were used. There was no conflict of interests in this study. CBCT scans were made of the heads and 3D

  11. 3D Compressible Melt Transport with Adaptive Mesh Refinement

    Science.gov (United States)

    Dannberg, Juliane; Heister, Timo

    2015-04-01

    Melt generation and migration have been the subject of numerous investigations, but their typical time and length-scales are vastly different from mantle convection, which makes it difficult to study these processes in a unified framework. The equations that describe coupled Stokes-Darcy flow have been derived a long time ago and they have been successfully implemented and applied in numerical models (Keller et al., 2013). However, modelling magma dynamics poses the challenge of highly non-linear and spatially variable material properties, in particular the viscosity. Applying adaptive mesh refinement to this type of problems is particularly advantageous, as the resolution can be increased in mesh cells where melt is present and viscosity gradients are high, whereas a lower resolution is sufficient in regions without melt. In addition, previous models neglect the compressibility of both the solid and the fluid phase. However, experiments have shown that the melt density change from the depth of melt generation to the surface leads to a volume increase of up to 20%. Considering these volume changes in both phases also ensures self-consistency of models that strive to link melt generation to processes in the deeper mantle, where the compressibility of the solid phase becomes more important. We describe our extension of the finite-element mantle convection code ASPECT (Kronbichler et al., 2012) that allows for solving additional equations describing the behaviour of silicate melt percolating through and interacting with a viscously deforming host rock. We use the original compressible formulation of the McKenzie equations, augmented by an equation for the conservation of energy. This approach includes both melt migration and melt generation with the accompanying latent heat effects. We evaluate the functionality and potential of this method using a series of simple model setups and benchmarks, comparing results of the compressible and incompressible formulation and

  12. 3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilation

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Kishimoto, Jessica; Chen, Yimin; de Ribaupierre, Sandrine; Chiu, Bernard; Fenster, Aaron

    2015-03-01

    Intraventricular hemorrhage (IVH) or bleed within the brain is a common condition among pre-term infants that occurs in very low birth weight preterm neonates. The prognosis is further worsened by the development of progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilation (PHVD), which occurs in 10-30% of IVH patients. In practice, predicting PHVD accurately and determining if that specific patient with ventricular dilatation requires the ability to measure accurately ventricular volume. While monitoring of PHVD in infants is typically done by repeated US and not MRI, once the patient has been treated, the follow-up over the lifetime of the patient is done by MRI. While manual segmentation is still seen as a gold standard, it is extremely time consuming, and therefore not feasible in a clinical context, and it also has a large inter- and intra-observer variability. This paper proposes a segmentation algorithm to extract the cerebral ventricles from 3D T1- weighted MR images of pre-term infants with PHVD. The proposed segmentation algorithm makes use of the convex optimization technique combined with the learned priors of image intensities and label probabilistic map, which is built from a multi-atlas registration scheme. The leave-one-out cross validation using 7 PHVD patient T1 weighted MR images showed that the proposed method yielded a mean DSC of 89.7% +/- 4.2%, a MAD of 2.6 +/- 1.1 mm, a MAXD of 17.8 +/- 6.2 mm, and a VD of 11.6% +/- 5.9%, suggesting a good agreement with manual segmentations.

  13. Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron

    2014-04-01

    We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

  14. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

    Science.gov (United States)

    Liu, Fang; Zhou, Zhaoye; Jang, Hyungseok; Samsonov, Alexey; Zhao, Gengyan; Kijowski, Richard

    2017-07-21

    To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  15. Electro-bending characterization of adaptive 3D fiber reinforced plastics based on shape memory alloys

    Science.gov (United States)

    Ashir, Moniruddoza; Hahn, Lars; Kluge, Axel; Nocke, Andreas; Cherif, Chokri

    2016-03-01

    The industrial importance of fiber reinforced plastics (FRPs) is growing steadily in recent years, which are mostly used in different niche products, has been growing steadily in recent years. The integration of sensors and actuators in FRP is potentially valuable for creating innovative applications and therefore the market acceptance of adaptive FRP is increasing. In particular, in the field of highly stressed FRP, structural integrated systems for continuous component parts monitoring play an important role. This presented work focuses on the electro-mechanical characterization of adaptive three-dimensional (3D)FRP with integrated textile-based actuators. Here, the friction spun hybrid yarn, consisting of shape memory alloy (SMA) in wire form as core, serves as an actuator. Because of the shape memory effect, the SMA-hybrid yarn returns to its original shape upon heating that also causes the deformation of adaptive 3D FRP. In order to investigate the influences of the deformation behavior of the adaptive 3D FRP, investigations in this research are varied according to the structural parameters such as radius of curvature of the adaptive 3D FRP, fabric types and number of layers of the fabric in the composite. Results show that reproducible deformations can be realized with adaptive 3D FRP and that structural parameters have a significant impact on the deformation capability.

  16. REGISTRATION OF OVERLAPPING 3D POINT CLO UDS USING EXTRACTED LINE SEGMENTS

    Directory of Open Access Journals (Sweden)

    Poręba Martyna

    2014-12-01

    Full Text Available The registration of 3D point clouds collected from different scanner positions is necessary in order to avoid occlusions, ensure a full coverage of areas, and collect useful data for analyzing an d documenting the surrounding environment. This procedure involves three main stages: 1 choosing appropriate features, which can be reliably extracted; 2 matching conjugate primitives; 3 estimating the transformation parameters. Currently, points and spheres are most frequently chosen as the registration features. However, due to limited point cloud resolution, proper identification and precise measurement of a common point within the overlapping laser data is almost impossible. One possible solution to this problem may be a registration process based on the Iterative Closest Point (ICP algorithm or its variation. Alternatively, planar and linear feature - based registration techniques can also be applied. In this paper, we propose the use of line segments obtained from intersecting planes modelled within individual scans. Such primitives can be easily extracted even from low - density point clouds. Working with synthetic data, several existing line - based registration methods are evaluated according to their robustness to noise and the precision of the estimated transformation parameters. For the purpose of quantitative assessment, an accuracy criterion based on a modified Hausdorff distance is defined. Since a n automated matching of segments is a challenging task that influences the correctness of the transformation parameters, a correspondence - finding algorithm is developed. The tests show that our matching algorithm provides a correct pairing with an accuracy of 99 % at least, and about 8% of omitted line pairs.

  17. Real-time 3D adaptive filtering for portable imaging systems

    Science.gov (United States)

    Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark

    2015-03-01

    Portable imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often not able to run with sufficient performance on a portable platform. In recent years, advanced multicore DSPs have been introduced that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms like 3D adaptive filtering, improving the image quality of portable medical imaging devices. In this study, the performance of a 3D adaptive filtering algorithm on a digital signal processor (DSP) is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec.

  18. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

    Directory of Open Access Journals (Sweden)

    Weiwei Wu

    2017-01-01

    Full Text Available Three-dimensional (3D liver tumor segmentation from Computed Tomography (CT images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM and graph cuts. With a single seed point, the tumor volume of interest (VOI was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb, which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE of 29.04% and Dice similarity coefficient (DICE of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.

  19. 3D-SoftChip: A Novel Architecture for Next-Generation Adaptive Computing Systems

    Directory of Open Access Journals (Sweden)

    Lee Mike Myung-Ok

    2006-01-01

    Full Text Available This paper introduces a novel architecture for next-generation adaptive computing systems, which we term 3D-SoftChip. The 3D-SoftChip is a 3-dimensional (3D vertically integrated adaptive computing system combining state-of-the-art processing and 3D interconnection technology. It comprises the vertical integration of two chips (a configurable array processor and an intelligent configurable switch through an indium bump interconnection array (IBIA. The configurable array processor (CAP is an array of heterogeneous processing elements (PEs, while the intelligent configurable switch (ICS comprises a switch block, 32-bit dedicated RISC processor for control, on-chip program/data memory, data frame buffer, along with a direct memory access (DMA controller. This paper introduces the novel 3D-SoftChip architecture for real-time communication and multimedia signal processing as a next-generation computing system. The paper further describes the advanced HW/SW codesign and verification methodology, including high-level system modeling of the 3D-SoftChip using SystemC, being used to determine the optimum hardware specification in the early design stage.

  20. In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation.

    Science.gov (United States)

    Xia, Chunlei; Wang, Longtan; Chung, Bu-Keun; Lee, Jang-Myung

    2015-08-19

    In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions.

  1. In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation

    Directory of Open Access Journals (Sweden)

    Chunlei Xia

    2015-08-01

    Full Text Available In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions.

  2. Knowledge-based 3D segmentation of the brain in MR images for quantitative multiple sclerosis lesion tracking

    Science.gov (United States)

    Fisher, Elizabeth; Cothren, Robert M., Jr.; Tkach, Jean A.; Masaryk, Thomas J.; Cornhill, J. Fredrick

    1997-04-01

    Brain segmentation in magnetic resonance (MR) images is an important step in quantitative analysis applications, including the characterization of multiple sclerosis (MS) lesions over time. Our approach is based on a priori knowledge of the intensity and three-dimensional (3D) spatial relationships of structures in MR images of the head. Optimal thresholding and connected-components analysis are used to generate a starting point for segmentation. A 3D radial search is then performed to locate probable locations of the intra-cranial cavity (ICC). Missing portions of the ICC surface are interpolated in order to exclude connected structures. Partial volume effects and inter-slice intensity variations in the image are accounted for automatically. Several studies were conducted to validate the segmentation. Accuracy was tested by calculating the segmented volume and comparing to known volumes of a standard MR phantom. Reliability was tested by comparing calculated volumes of individual segmentation results from multiple images of the same subject. The segmentation results were also compared to manual tracings. The average error in volume measurements for the phantom was 1.5% and the average coefficient of variation of brain volume measurements of the same subject was 1.2%. Since the new algorithm requires minimal user interaction, variability introduced by manual tracing and interactive threshold or region selection was eliminated. Overall, the new algorithm was shown to produce a more accurate and reliable brain segmentation than existing manual and semi-automated techniques.

  3. Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models.

    Science.gov (United States)

    Neubert, A; Fripp, J; Engstrom, C; Schwarz, R; Lauer, L; Salvado, O; Crozier, S

    2012-12-21

    Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

  4. 3D human shape model adaptation by automatic frame selection and batch-mode optimization

    NARCIS (Netherlands)

    Hofmann, M.; Gavrila, D.M.

    2011-01-01

    We present a novel approach for 3D human body shape model adaptation to a sequence of multi-view images, given an initial shape model and initial pose sequence. In a first step, the most informative frames are determined by optimization of an objective function that maximizes a shape-texture

  5. 3D Human model adaptation by frame selection and shape–texture optimization

    NARCIS (Netherlands)

    Hofmann, K.M.; Gavrila, D.M.

    2011-01-01

    We present a novel approach for 3D human body shape model adaptation to a sequence of multi-view images, given an initial shape model and initial pose sequence. In a first step, the most informative frames are determined by optimization of an objective function that maximizes a shape–texture

  6. Segmentation of complex objects with non-spherical topologies from volumetric medical images using 3D livewire

    Science.gov (United States)

    Poon, Kelvin; Hamarneh, Ghassan; Abugharbieh, Rafeef

    2007-03-01

    Segmentation of 3D data is one of the most challenging tasks in medical image analysis. While reliable automatic methods are typically preferred, their success is often hindered by poor image quality and significant variations in anatomy. Recent years have thus seen an increasing interest in the development of semi-automated segmentation methods that combine computational tools with intuitive, minimal user interaction. In an earlier work, we introduced a highly-automated technique for medical image segmentation, where a 3D extension of the traditional 2D Livewire was proposed. In this paper, we present an enhanced and more powerful 3D Livewire-based segmentation approach with new features designed to primarily enable the handling of complex object topologies that are common in biological structures. The point ordering algorithm we proposed earlier, which automatically pairs up seedpoints in 3D, is improved in this work such that multiple sets of points are allowed to simultaneously exist. Point sets can now be automatically merged and split to accommodate for the presence of concavities, protrusions, and non-spherical topologies. The robustness of the method is further improved by extending the 'turtle algorithm', presented earlier, by using a turtle-path pruning step. Tests on both synthetic and real medical images demonstrate the efficiency, reproducibility, accuracy, and robustness of the proposed approach. Among the examples illustrated is the segmentation of the left and right ventricles from a T1-weighted MRI scan, where an average task time reduction of 84.7% was achieved when compared to a user performing 2D Livewire segmentation on every slice.

  7. User-guided segmentation of preterm neonate ventricular system from 3-D ultrasound images using convex optimization.

    Science.gov (United States)

    Qiu, Wu; Yuan, Jing; Kishimoto, Jessica; McLeod, Jonathan; Chen, Yimin; de Ribaupierre, Sandrine; Fenster, Aaron

    2015-02-01

    A three-dimensional (3-D) ultrasound (US) system has been developed to monitor the intracranial ventricular system of preterm neonates with intraventricular hemorrhage (IVH) and the resultant dilation of the ventricles (ventriculomegaly). To measure ventricular volume from 3-D US images, a semi-automatic convex optimization-based approach is proposed for segmentation of the cerebral ventricular system in preterm neonates with IVH from 3-D US images. The proposed semi-automatic segmentation method makes use of the convex optimization technique supervised by user-initialized information. Experiments using 58 patient 3-D US images reveal that our proposed approach yielded a mean Dice similarity coefficient of 78.2% compared with the surfaces that were manually contoured, suggesting good agreement between these two segmentations. Additional metrics, the mean absolute distance of 0.65 mm and the maximum absolute distance of 3.2 mm, indicated small distance errors for a voxel spacing of 0.22 × 0.22 × 0.22 mm(3). The Pearson correlation coefficient (r = 0.97, p < 0.001) indicated a significant correlation of algorithm-generated ventricular system volume (VSV) with the manually generated VSV. The calculated minimal detectable difference in ventricular volume change indicated that the proposed segmentation approach with 3-D US images is capable of detecting a VSV difference of 6.5 cm(3) with 95% confidence, suggesting that this approach might be used for monitoring IVH patients' ventricular changes using 3-D US imaging. The mean segmentation times of the graphics processing unit (GPU)- and central processing unit-implemented algorithms were 50 ± 2 and 205 ± 5 s for one 3-D US image, respectively, in addition to 120 ± 10 s for initialization, less than the approximately 35 min required by manual segmentation. In addition, repeatability experiments indicated that the intra-observer variability ranges from 6.5% to 7.5%, and the inter-observer variability is 8.5% in terms

  8. Pairwise domain adaptation module for CNN-based 2-D/3-D registration.

    Science.gov (United States)

    Zheng, Jiannan; Miao, Shun; Jane Wang, Z; Liao, Rui

    2018-04-01

    Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D registration formulate the problem as a learning-based approach and exploit the modeling power of convolutional neural networks (CNN) to significantly improve the accuracy and efficiency of 2-D/3-D registration. However, for surgery-related applications, collecting a large clinical dataset with accurate annotations for training can be very challenging or impractical. Therefore, deep learning-based 2-D/3-D registration methods are often trained with synthetically generated data, and a performance gap is often observed when testing the trained model on clinical data. We propose a pairwise domain adaptation (PDA) module to adapt the model trained on source domain (i.e., synthetic data) to target domain (i.e., clinical data) by learning domain invariant features with only a few paired real and synthetic data. The PDA module is designed to be flexible for different deep learning-based 2-D/3-D registration frameworks, and it can be plugged into any pretrained CNN model such as a simple Batch-Norm layer. The proposed PDA module has been quantitatively evaluated on two clinical applications using different frameworks of deep networks, demonstrating its significant advantages of generalizability and flexibility for 2-D/3-D medical image registration when a small number of paired real-synthetic data can be obtained.

  9. Adaptation of Zerotrees Using Signed Binary Digit Representations for 3D Image Coding

    Directory of Open Access Journals (Sweden)

    Mailhes Corinne

    2007-01-01

    Full Text Available Zerotrees of wavelet coefficients have shown a good adaptability for the compression of three-dimensional images. EZW, the original algorithm using zerotree, shows good performance and was successfully adapted to 3D image compression. This paper focuses on the adaptation of EZW for the compression of hyperspectral images. The subordinate pass is suppressed to remove the necessity to keep the significant pixels in memory. To compensate the loss due to this removal, signed binary digit representations are used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.

  10. A hybrid framework of multiple active appearance models and global registration for 3D prostate segmentation in MRI

    Science.gov (United States)

    Ghose, Soumya; Oliver, Arnau; Martí, Robert; Lladó, Xavier; Freixenet, Jordi; Mitra, Jhimli; Vilanova, Joan C.; Meriaudeau, Fabrice

    2012-02-01

    Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of the prostate reduces computational complexity and improves the multimodal registration accuracy. However, accurate and computationally efficient 3D segmentation of the prostate in MR images could be a challenging task due to inter-patient shape and intensity variability of the prostate gland. In this work, we propose to use multiple statistical shape and appearance models to segment the prostate in 2D and a global registration framework to impose shape restriction in 3D. Multiple mean parametric models of the shape and appearance corresponding to the apex, central and base regions of the prostate gland are derived from principal component analysis (PCA) of prior shape and intensity information of the prostate from the training data. The estimated parameters are then modified with the prior knowledge of the optimization space to achieve segmentation in 2D. The 2D segmented slices are then rigidly registered with the average 3D model produced by affine registration of the ground truth of the training datasets to minimize pose variations and impose 3D shape restriction. The proposed method achieves a mean Dice similarity coefficient (DSC) value of 0.88+/-0.11, and mean Hausdorff distance (HD) of 3.38+/-2.81 mm when validated with 15 prostate volumes of a public dataset in leave-one-out validation framework. The results achieved are better compared to some of the works in the literature.

  11. Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation

    OpenAIRE

    Beers, Andrew; Chang, Ken; Brown, James; Sartor, Emmett; Mammen, CP; Gerstner, Elizabeth; Rosen, Bruce; Kalpathy-Cramer, Jayashree

    2017-01-01

    Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentation tissues, and instead rely on the neural network's optimizer to develop such associations de novo. We present a novel method for applying deep neural networks to the problem of glioma tissue segmentation that takes into account the structured nature of gliomas - edematous tissue surrounding mutually-exclusive reg...

  12. Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas.

    Science.gov (United States)

    Novosel, Jelena; Vermeer, Koenraad A; de Jong, Jan H; Ziyuan Wang; van Vliet, Lucas J

    2017-06-01

    Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers, thereby allowing the approach to handle local intensity variations. The accuracy of the proposed method of both layer and lesion segmentation has been evaluated on eyes affected by central serous retinopathy and age-related macular degeneration. In addition, layer segmentation of the proposed approach was evaluated on eyes without topology-disrupting retinal diseases. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found for all data types. The mean unsigned error for all interfaces varied between 2.3 and 11.9 μm (0.6-3.1 pixels). Furthermore, lesion segmentation showed a Dice coefficient of 0.68 for drusen and 0.89 for fluid pockets. Overall, the method provides a flexible and accurate solution to jointly segment lesions and retinal layers.

  13. Individual muscle segmentation in MR images: A 3D propagation through 2D non-linear registration approaches.

    Science.gov (United States)

    Ogier, Augustin; Sdika, Michael; Foure, Alexandre; Le Troter, Arnaud; Bendahan, David

    2017-07-01

    Manual and automated segmentation of individual muscles in magnetic resonance images have been recognized as challenging given the high variability of shapes between muscles and subjects and the discontinuity or lack of visible boundaries between muscles. In the present study, we proposed an original algorithm allowing a semi-automatic transversal propagation of manually-drawn masks. Our strategy was based on several ascending and descending non-linear registration approaches which is similar to the estimation of a Lagrangian trajectory applied to manual masks. Using several manually-segmented slices, we have evaluated our algorithm on the four muscles of the quadriceps femoris group. We mainly showed that our 3D propagated segmentation was very accurate with an averaged Dice similarity coefficient value higher than 0.91 for the minimal manual input of only two manually-segmented slices.

  14. A fast convex optimization approach to segmenting 3D scar tissue from delayed-enhancement cardiac MR images.

    Science.gov (United States)

    Rajchl, Martin; Yuan, Jing; White, James A; Nambakhsh, Cyrus; Ukwatta, Eranga; Li, Feng; Stirrat, John; Peters, Terry M

    2012-01-01

    We propose a novel multi-region segmentation approach through a partially-ordered ports (POP) model to segment myocardial scar tissue solely from 3D cardiac delayed-enhancement MR images (DE-MRI). The algorithm makes use of prior knowledge of anatomical spatial consistency and employs customized label ordering to constrain the segmentation without prior knowledge of geometric representation. The proposed method eliminates the need for regional constraint segmentations, thus reduces processing time and potential sources of error. We solve the proposed optimization problem by means of convex relaxation and introduce its duality: the hierarchical continuous max-flow (HMF) model, which amounts to an efficient numerical solver to the resulting convex optimization problem. Experiments are performed over ten DE-MRI data sets. The results are compared to a FWHM (full-width at half-maximum) method and the inter- and intra-operator variabilities assessed.

  15. Simultaneous Multi-Structure Segmentation and 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery.

    Science.gov (United States)

    Nosrati, Masoud S; Abugharbieh, Rafeef; Peyrat, Jean-Marc; Abinahed, Julien; Al-Alao, Osama; Al-Ansari, Abdulla; Hamarneh, Ghassan

    2016-01-01

    In image-guided robotic surgery, segmenting the endoscopic video stream into meaningful parts provides important contextual information that surgeons can exploit to enhance their perception of the surgical scene. This information provides surgeons with real-time decision-making guidance before initiating critical tasks such as tissue cutting. Segmenting endoscopic video is a challenging problem due to a variety of complications including significant noise attributed to bleeding and smoke from cutting, poor appearance contrast between different tissue types, occluding surgical tools, and limited visibility of the objects' geometries on the projected camera views. In this paper, we propose a multi-modal approach to segmentation where preoperative 3D computed tomography scans and intraoperative stereo-endoscopic video data are jointly analyzed. The idea is to segment multiple poorly visible structures in the stereo/multichannel endoscopic videos by fusing reliable prior knowledge captured from the preoperative 3D scans. More specifically, we estimate and track the pose of the preoperative models in 3D and consider the models' non-rigid deformations to match with corresponding visual cues in multi-channel endoscopic video and segment the objects of interest. Further, contrary to most augmented reality frameworks in endoscopic surgery that assume known camera parameters, an assumption that is often violated during surgery due to non-optimal camera calibration and changes in camera focus/zoom, our method embeds these parameters into the optimization hence correcting the calibration parameters within the segmentation process. We evaluate our technique on synthetic data, ex vivo lamb kidney datasets, and in vivo clinical partial nephrectomy surgery with results demonstrating high accuracy and robustness.

  16. Image Quality and Radiation Dose of CT Coronary Angiography with Automatic Tube Current Modulation and Strong Adaptive Iterative Dose Reduction Three-Dimensional (AIDR3D.

    Directory of Open Access Journals (Sweden)

    Hesong Shen

    Full Text Available To investigate image quality and radiation dose of CT coronary angiography (CTCA scanned using automatic tube current modulation (ATCM and reconstructed by strong adaptive iterative dose reduction three-dimensional (AIDR3D.Eighty-four consecutive CTCA patients were collected for the study. All patients were scanned using ATCM and reconstructed with strong AIDR3D, standard AIDR3D and filtered back-projection (FBP respectively. Two radiologists who were blinded to the patients' clinical data and reconstruction methods evaluated image quality. Quantitative image quality evaluation included image noise, signal-to-noise ratio (SNR, and contrast-to-noise ratio (CNR. To evaluate image quality qualitatively, coronary artery is classified into 15 segments based on the modified guidelines of the American Heart Association. Qualitative image quality was evaluated using a 4-point scale. Radiation dose was calculated based on dose-length product.Compared with standard AIDR3D, strong AIDR3D had lower image noise, higher SNR and CNR, their differences were all statistically significant (P<0.05; compared with FBP, strong AIDR3D decreased image noise by 46.1%, increased SNR by 84.7%, and improved CNR by 82.2%, their differences were all statistically significant (P<0.05 or 0.001. Segments with diagnostic image quality for strong AIDR3D were 336 (100.0%, 486 (96.4%, and 394 (93.8% in proximal, middle, and distal part respectively; whereas those for standard AIDR3D were 332 (98.8%, 472 (93.7%, 378 (90.0%, respectively; those for FBP were 217 (64.6%, 173 (34.3%, 114 (27.1%, respectively; total segments with diagnostic image quality in strong AIDR3D (1216, 96.5% were higher than those of standard AIDR3D (1182, 93.8% and FBP (504, 40.0%; the differences between strong AIDR3D and standard AIDR3D, strong AIDR3D and FBP were all statistically significant (P<0.05 or 0.001. The mean effective radiation dose was (2.55±1.21 mSv.Compared with standard AIDR3D and FBP, CTCA

  17. Multi-domain, higher order level set scheme for 3D image segmentation on the GPU

    DEFF Research Database (Denmark)

    Sharma, Ojaswa; Zhang, Qin; Anton, François

    2010-01-01

    to evaluate level set surfaces that are $C^2$ continuous, but are slow due to high computational burden. In this paper, we provide a higher order GPU based solver for fast and efficient segmentation of large volumetric images. We also extend the higher order method to multi-domain segmentation. Our streaming...

  18. 3D segmentation and quantification of a masticatory muscle from MR data using patient-specific models and matching distributions.

    Science.gov (United States)

    Ng, H P; Ong, S H; Liu, J; Huang, S; Foong, K W C; Goh, P S; Nowinski, W L

    2009-10-01

    A method is proposed for 3D segmentation and quantification of the masseter muscle from magnetic resonance (MR) images, which is often performed in pre-surgical planning and diagnosis. Because of a lack of suitable automatic techniques, a common practice is for clinicians to manually trace out all relevant regions from the image slices which is extremely time-consuming. The proposed method allows significant time savings. In the proposed method, a patient-specific masseter model is built from a test dataset after determining the dominant slices that represent the salient features of the 3D muscle shape from training datasets. Segmentation is carried out only on these slices in the test dataset, with shape-based interpolation then applied to build the patient-specific model, which serves as a coarse segmentation of the masseter. This is first refined by matching the intensity distribution within the masseter volume against the distribution estimated from the segmentations in the dominant slices, and further refined through boundary analysis where the homogeneity of the intensities of the boundary pixels is analyzed and outliers removed. It was observed that the left and right masseter muscles' volumes in young adults (28.54 and 27.72 cm(3)) are higher than those of older (ethnic group removed) adults (23.16 and 22.13 cm(3)). Evaluation indicates good agreement between the segmentations and manual tracings, with average overlap indexes for the left and right masseters at 86.6% and 87.5% respectively.

  19. Estimation of regeneration coverage in a temperate forest by 3D segmentation using airborne laser scanning data

    Science.gov (United States)

    Amiri, Nina; Yao, Wei; Heurich, Marco; Krzystek, Peter; Skidmore, Andrew K.

    2016-10-01

    Forest understory and regeneration are important factors in sustainable forest management. However, understanding their spatial distribution in multilayered forests requires accurate and continuously updated field data, which are difficult and time-consuming to obtain. Therefore, cost-efficient inventory methods are required, and airborne laser scanning (ALS) is a promising tool for obtaining such information. In this study, we examine a clustering-based 3D segmentation in combination with ALS data for regeneration coverage estimation in a multilayered temperate forest. The core of our method is a two-tiered segmentation of the 3D point clouds into segments associated with regeneration trees. First, small parts of trees (super-voxels) are constructed through mean shift clustering, a nonparametric procedure for finding the local maxima of a density function. In the second step, we form a graph based on the mean shift clusters and merge them into larger segments using the normalized cut algorithm. These segments are used to obtain regeneration coverage of the target plot. Results show that, based on validation data from field inventory and terrestrial laser scanning (TLS), our approach correctly estimates up to 70% of regeneration coverage across the plots with different properties, such as tree height and tree species. The proposed method is negatively impacted by the density of the overstory because of decreasing ground point density. In addition, the estimated coverage has a strong relationship with the overstory tree species composition.

  20. Improving Segmentation of 3D Retina Layers Based on Graph Theory Approach for Low Quality OCT Images

    Directory of Open Access Journals (Sweden)

    Stankiewicz Agnieszka

    2016-06-01

    Full Text Available This paper presents signal processing aspects for automatic segmentation of retinal layers of the human eye. The paper draws attention to the problems that occur during the computer image processing of images obtained with the use of the Spectral Domain Optical Coherence Tomography (SD OCT. Accuracy of the retinal layer segmentation for a set of typical 3D scans with a rather low quality was shown. Some possible ways to improve quality of the final results are pointed out. The experimental studies were performed using the so-called B-scans obtained with the OCT Copernicus HR device.

  1. A density-based segmentation for 3D images, an application for X-ray micro-tomography

    Energy Technology Data Exchange (ETDEWEB)

    Tran, Thanh N., E-mail: thanh.tran@merck.com [Center for Mathematical Sciences Merck, MSD Molenstraat 110, 5342 CC Oss, PO Box 20, 5340 BH Oss (Netherlands); Nguyen, Thanh T.; Willemsz, Tofan A. [Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, Groningen (Netherlands); Pharmaceutical Sciences and Clinical Supplies, Merck MSD, PO Box 20, 5340 BH Oss (Netherlands); Kessel, Gijs van [Center for Mathematical Sciences Merck, MSD Molenstraat 110, 5342 CC Oss, PO Box 20, 5340 BH Oss (Netherlands); Frijlink, Henderik W. [Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, Groningen (Netherlands); Voort Maarschalk, Kees van der [Department of Pharmaceutical Technology and Biopharmacy, University of Groningen, Groningen (Netherlands); Competence Center Process Technology, Purac Biochem, Gorinchem (Netherlands)

    2012-05-06

    Highlights: Black-Right-Pointing-Pointer We revised the DBSCAN algorithm for segmentation and clustering of large 3D image dataset and classified multivariate image. Black-Right-Pointing-Pointer The algorithm takes into account the coordinate system of the image data to improve the computational performance. Black-Right-Pointing-Pointer The algorithm solved the instability problem in boundaries detection of the original DBSCAN. Black-Right-Pointing-Pointer The segmentation results were successfully validated with synthetic 3D image and 3D XMT image of a pharmaceutical powder. - Abstract: Density-based spatial clustering of applications with noise (DBSCAN) is an unsupervised classification algorithm which has been widely used in many areas with its simplicity and its ability to deal with hidden clusters of different sizes and shapes and with noise. However, the computational issue of the distance table and the non-stability in detecting the boundaries of adjacent clusters limit the application of the original algorithm to large datasets such as images. In this paper, the DBSCAN algorithm was revised and improved for image clustering and segmentation. The proposed clustering algorithm presents two major advantages over the original one. Firstly, the revised DBSCAN algorithm made it applicable for large 3D image dataset (often with millions of pixels) by using the coordinate system of the image data. Secondly, the revised algorithm solved the non-stability issue of boundary detection in the original DBSCAN. For broader applications, the image dataset can be ordinary 3D images or in general, it can also be a classification result of other type of image data e.g. a multivariate image.

  2. Segmentation propagation using a 3D embryo atlas for high-throughput MRI phenotyping: comparison and validation with manual segmentation.

    Science.gov (United States)

    Norris, Francesca C; Modat, Marc; Cleary, Jon O; Price, Anthony N; McCue, Karen; Scambler, Peter J; Ourselin, Sebastien; Lythgoe, Mark F

    2013-03-01

    Effective methods for high-throughput screening and morphometric analysis are crucial for phenotyping the increasing number of mouse mutants that are being generated. Automated segmentation propagation for embryo phenotyping is an emerging application that enables noninvasive and rapid quantification of substructure volumetric data for morphometric analysis. We present a study to assess and validate the accuracy of brain and kidney volumes generated via segmentation propagation in an ex vivo mouse embryo MRI atlas comprising three different groups against the current "gold standard"--manual segmentation. Morphometric assessment showed good agreement between automatically and manually segmented volumes, demonstrating that it is possible to assess volumes for phenotyping a population of embryos using segmentation propagation with the same variation as manual segmentation. As part of this study, we have made our average atlas and segmented volumes freely available to the community for use in mouse embryo phenotyping studies. These MRI datasets and automated methods of analyses will be essential for meeting the challenge of high-throughput, automated embryo phenotyping. Copyright © 2012 Wiley Periodicals, Inc.

  3. Patient-adapted respiratory training: Effect on navigator-triggered 3D MRCP in painful pancreatobiliary disorders.

    Science.gov (United States)

    Zhu, Liang; Sun, Zhao-Yong; Xue, Hua-Dan; Liu, Dong; Qian, Tian-Yi; Asbach, Patrick; Jin, Zheng-Yu

    2018-01-01

    To compare the image quality of navigator-triggered (NT) 3D MR cholangiopancreatography (MRCP) with and without a patient-adapted respiratory training, in clinical patients with painful pancreatobiliary disorders. With institutional review board approval, hospitalized patients with painful pancreatobiliary disorders who were scheduled for MRCP study were prospectively enrolled. The numerical rating scale (NRS) of abdominal pain during the examination was recorded. Special patient-adapted respiratory training was conducted before the examination. A control group of patients was enrolled with the same criteria, who received ordinary instructions only (n=60 for each group). A subgroup of patients (n=10) underwent MRCP studies with ordinary instructions first and with patient-adapted training later. Acquisition time was recorded. General image quality, degree of artifacts and visualization of 12 segments of the pancreatobiliary tree were rated on a five-point scale and compared between the groups. Both groups had similar NRS of pain. There was a significant improvement in image quality (p<0.01) as well as visualization of right posterior hepatic duct (p=0.045), left lateral hepatic duct (p=0.037), and pancreatic duct (p<0.01 for head, body and tail segments) in patients receiving respiratory training. The other segments showed no significant differences. The percentage of patients with severe and extensive imaging artifacts decreased from 18.3%(11/60) to 8.3%(5/60). The acquisition time was shorter (175±54s vs 249±67s, p<0.01) in patients with respiratory training. Patient-adapted respiratory training improves the image quality of NT-MRCP in patients with painful pancreatobiliary disorders. Copyright © 2017. Published by Elsevier Inc.

  4. Combining population and patient-specific characteristics for prostate segmentation on 3D CT images

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Venkataraman, Rajesh; Sarkar, Saradwata; Liu, Xiabi; Tade, Funmilayo; Schuster, David M.; Fei, Baowei

    2016-03-01

    Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.

  5. Model-based 3-D segmentation of multiple sclerosis lesions in dual-echo MRI data

    Science.gov (United States)

    Kamber, Micheline; Collins, D. Louis; Shinghal, Rajjan; Francis, G. S.; Evans, Alan C.

    1992-09-01

    This paper describes the development and use of a brain tissue probability model for the segmentation of multiple sclerosis lesions in magnetic resonance (MR) images of the human brain. Based on MR data obtained from a group of healthy volunteers, the model was constructed to provide prior probabilities of grey matter, white matter, ventricular cerebrospinal fluid (CSF), and external CSF distribution per unit voxel in a standardized 3- dimensional `brain space.' In comparison to purely data-driven segmentation, the use of the model to guide the segmentation of multiple sclerosis lesions reduced the volume of false positive lesions by 50%.

  6. Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis

    Science.gov (United States)

    Shahedi, Maysam; Fenster, Aaron; Cool, Derek W.; Romagnoli, Cesare; Ward, Aaron D.

    2013-03-01

    3D segmentation of the prostate in medical images is useful to prostate cancer diagnosis and therapy guidance, but is time-consuming to perform manually. Clinical translation of computer-assisted segmentation algorithms for this purpose requires a comprehensive and complementary set of evaluation metrics that are informative to the clinical end user. We have developed an interactive 3D prostate segmentation method for 1.5T and 3.0T T2-weighted magnetic resonance imaging (T2W MRI) acquired using an endorectal coil. We evaluated our method against manual segmentations of 36 3D images using complementary boundary-based (mean absolute distance; MAD), regional overlap (Dice similarity coefficient; DSC) and volume difference (ΔV) metrics. Our technique is based on inter-subject prostate shape and local boundary appearance similarity. In the training phase, we calculated a point distribution model (PDM) and a set of local mean intensity patches centered on the prostate border to capture shape and appearance variability. To segment an unseen image, we defined a set of rays - one corresponding to each of the mean intensity patches computed in training - emanating from the prostate centre. We used a radial-based search strategy and translated each mean intensity patch along its corresponding ray, selecting as a candidate the boundary point with the highest normalized cross correlation along each ray. These boundary points were then regularized using the PDM. For the whole gland, we measured a mean+/-std MAD of 2.5+/-0.7 mm, DSC of 80+/-4%, and ΔV of 1.1+/-8.8 cc. We also provided an anatomic breakdown of these metrics within the prostatic base, mid-gland, and apex.

  7. Improvements to the ShipIR/NTCS adaptive track gate algorithm and 3D flare particle model

    Science.gov (United States)

    Ramaswamy, Srinivasan; Vaitekunas, David A.; Gunter, Willem H.; February, Faith J.

    2017-05-01

    A key component in any image-based tracking system is the adaptive tracking algorithm used to segment the image into potential targets, rank-and-select the best candidate target, and gate the selected target to further improve tracker performance. Similarly, a key component in any soft-kill response to an incoming guided missile is the flare/chaff decoy used to distract or seduce the seeker homing system away from the naval platform. This paper describes the recent improvements to the naval threat countermeasure simulator (NTCS) of the NATO-standard ship signature model (ShipIR). Efforts to analyse and match the 3D flare particle model against actual IR measurements of the Chemring TALOS IR round resulted in further refinement of the 3D flare particle distribution. The changes in the flare model characteristics were significant enough to require an overhaul to the adaptive track gate (ATG) algorithm in the way it detects the presence of flare decoys and reacquires the target after flare separation. A series of test scenarios are used to demonstrate the impact of the new flare and ATG on IR tactics simulation.

  8. Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery.

    Science.gov (United States)

    Penza, Veronica; Ortiz, Jesús; Mattos, Leonardo S; Forgione, Antonello; De Momi, Elena

    2016-02-01

    Single-incision laparoscopic surgery decreases postoperative infections, but introduces limitations in the surgeon's maneuverability and in the surgical field of view. This work aims at enhancing intra-operative surgical visualization by exploiting the 3D information about the surgical site. An interactive guidance system is proposed wherein the pose of preoperative tissue models is updated online. A critical process involves the intra-operative acquisition of tissue surfaces. It can be achieved using stereoscopic imaging and 3D reconstruction techniques. This work contributes to this process by proposing new methods for improved dense 3D reconstruction of soft tissues, which allows a more accurate deformation identification and facilitates the registration process. Two methods for soft tissue 3D reconstruction are proposed: Method 1 follows the traditional approach of the block matching algorithm. Method 2 performs a nonparametric modified census transform to be more robust to illumination variation. The simple linear iterative clustering (SLIC) super-pixel algorithm is exploited for disparity refinement by filling holes in the disparity images. The methods were validated using two video datasets from the Hamlyn Centre, achieving an accuracy of 2.95 and 1.66 mm, respectively. A comparison with ground-truth data demonstrated the disparity refinement procedure: (1) increases the number of reconstructed points by up to 43 % and (2) does not affect the accuracy of the 3D reconstructions significantly. Both methods give results that compare favorably with the state-of-the-art methods. The computational time constraints their applicability in real time, but can be greatly improved by using a GPU implementation.

  9. A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy.

    Science.gov (United States)

    Zhou, Jinghao; Kim, Sung; Jabbour, Salma; Goyal, Sharad; Haffty, Bruce; Chen, Ting; Levinson, Lydia; Metaxas, Dimitris; Yue, Ning J

    2010-03-01

    In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation-and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. The ACRASM segmentation algorithm was compared to the original active shape mode (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to 6.54 mm for ASM. The volume

  10. Performance analysis of a compact electro-optical 3D adapter with a wide capturing angle

    Science.gov (United States)

    Kim, Seung-Cheol; Lee, Dong-Hwi; Lee, Jong-Gil; Kim, Eun-Soo

    2006-02-01

    In this paper, a new 3D adapter system with a lens unit interposed between a capturing lens and an adapter housing for alternately passing right and left video images of an object there through, wherein the lens unit has an entrance pupil point formed outside the lens unit, the lens unit has a magnification of 1:1, and the lens unit comprises a plurality of symmetrically arranged lenses for reversing the video images, whereby it is possible to capture video images with wide picture angles without increasing the size of the adapter housing, and to prevent occurrence of any distortion in the resulting video images comprised of the integrated right an left images of the object. From some experimental result, the conventional 3D adapter system has the standard deviation of x axis is 3.92 pixels and the standard deviation of y axis is 2.92 pixels. But in the used camera system, the standard deviation of x axis is 1.11 pixels and the standard deviation of y axis is 0.39 pixels. Thus the errors in pixel of proposed system are smaller than the conventional system.

  11. 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

  12. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images

    Science.gov (United States)

    Castro-Mateos, Isaac; Pozo, Jose M.; Eltes, Peter E.; Del Rio, Luis; Lazary, Aron; Frangi, Alejandro F.

    2014-12-01

    Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy. The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

  13. Simulating streamer discharges in 3D with the parallel adaptive Afivo framework

    Science.gov (United States)

    Teunissen, Jannis; Ebert, Ute

    2017-11-01

    We present an open-source plasma fluid code for 2D, cylindrical and 3D simulations of streamer discharges. The code is based on the Afivo framework, which features adaptive mesh refinement on quadtree/octree grids, geometric multigrid methods for Poisson’s equation, and OpenMP parallelism. We describe the numerical implementation of a fluid model of the drift-diffusion-reaction type, combined with the local field approximation. Then we demonstrate its functionality with 3D simulations of long positive streamers in nitrogen in undervolted gaps. Three examples are presented. The first one shows how a stochastic background density affects streamer propagation and branching. The second one focuses on the interaction of a streamer with preionized regions, and the third one investigates the interaction between two streamers. The simulations use up to 108 grid cells and run in less than a day; without mesh refinement they would require more than 1012 grid cells.

  14. Understanding Spatially Complex Segmental and Branch Anatomy Using 3D Printing: Liver, Lung, Prostate, Coronary Arteries, and Circle of Willis.

    Science.gov (United States)

    Javan, Ramin; Herrin, Douglas; Tangestanipoor, Ardalan

    2016-09-01

    Three-dimensional (3D) manufacturing is shaping personalized medicine, in which radiologists can play a significant role, be it as consultants to surgeons for surgical planning or by creating powerful visual aids for communicating with patients, physicians, and trainees. This report illustrates the steps in development of custom 3D models that enhance the understanding of complex anatomy. We graphically designed 3D meshes or modified imported data from cross-sectional imaging to develop physical models targeted specifically for teaching complex segmental and branch anatomy. The 3D printing itself is easily accessible through online commercial services, and the models are made of polyamide or gypsum. Anatomic models of the liver, lungs, prostate, coronary arteries, and the Circle of Willis were created. These models have advantages that include customizable detail, relative low cost, full control of design focusing on subsegments, color-coding potential, and the utilization of cross-sectional imaging combined with graphic design. Radiologists have an opportunity to serve as leaders in medical education and clinical care with 3D printed models that provide beneficial interaction with patients, clinicians, and trainees across all specialties by proactively taking on the educator's role. Complex models can be developed to show normal anatomy or common pathology for medical educational purposes. There is a need for randomized trials, which radiologists can design, to demonstrate the utility and effectiveness of 3D printed models for teaching simple and complex anatomy, simulating interventions, measuring patient satisfaction, and improving clinical care. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  15. Development of adaptive bust for female soft body armour using three dimensional (3D) warp interlock fabrics: Three dimensional (3D) design process

    Science.gov (United States)

    Abtew, M. A.; Bruniaux, P.; Boussu, F.

    2017-10-01

    The traditional two dimensional (2D) pattern making method for developing female body armour has a negative effect on the ballistic protective performance as well as the comfort of the wearer. This is due to, unlike the male body armour, the female body armour manufacturing involves different darts to accommodate the natural curvature of the female body, i.e. bust area, which will reveals the weak parts at the seam and stitch area while ballistic impact. Moreover, the proper bra size also plays an important role not only in bra design but also in the design of a women’s ballistic vest. The present research study tried to propose the novel 3D designing approach for developing different volumes of breast using feature points (both bust surface and outline points) in the specific 3D adaptive mannequin. Later the flattened 3D bra patterns of this method has been also compare with the 2D standard pattern making in order to modify and match with 2D traditional method. The result indicated that the proposed method which conceives the 3D patterns on the 3D bust is easier to implement and can generate patterns with satisfactory fit and comfort as compared to 2D patterns.

  16. Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

    Science.gov (United States)

    Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

    2016-12-01

    This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  17. Aeroacoustic Simulation of Nose Landing Gear on Adaptive Unstructured Grids With FUN3D

    Science.gov (United States)

    Vatsa, Veer N.; Khorrami, Mehdi R.; Park, Michael A.; Lockard, David P.

    2013-01-01

    Numerical simulations have been performed for a partially-dressed, cavity-closed nose landing gear configuration that was tested in NASA Langley s closed-wall Basic Aerodynamic Research Tunnel (BART) and in the University of Florida's open-jet acoustic facility known as the UFAFF. The unstructured-grid flow solver FUN3D, developed at NASA Langley Research center, is used to compute the unsteady flow field for this configuration. Starting with a coarse grid, a series of successively finer grids were generated using the adaptive gridding methodology available in the FUN3D code. A hybrid Reynolds-averaged Navier-Stokes/large eddy simulation (RANS/LES) turbulence model is used for these computations. Time-averaged and instantaneous solutions obtained on these grids are compared with the measured data. In general, the correlation with the experimental data improves with grid refinement. A similar trend is observed for sound pressure levels obtained by using these CFD solutions as input to a FfowcsWilliams-Hawkings noise propagation code to compute the farfield noise levels. In general, the numerical solutions obtained on adapted grids compare well with the hand-tuned enriched fine grid solutions and experimental data. In addition, the grid adaption strategy discussed here simplifies the grid generation process, and results in improved computational efficiency of CFD simulations.

  18. Automatic training and reliability estimation for 3D ASM applied to cardiac MRI segmentation

    Science.gov (United States)

    Tobon-Gomez, Catalina; Sukno, Federico M.; Butakoff, Constantine; Huguet, Marina; Frangi, Alejandro F.

    2012-07-01

    Training active shape models requires collecting manual ground-truth meshes in a large image database. While shape information can be reused across multiple imaging modalities, intensity information needs to be imaging modality and protocol specific. In this context, this study has two main purposes: (1) to test the potential of using intensity models learned from MRI simulated datasets and (2) to test the potential of including a measure of reliability during the matching process to increase robustness. We used a population of 400 virtual subjects (XCAT phantom), and two clinical populations of 40 and 45 subjects. Virtual subjects were used to generate simulated datasets (MRISIM simulator). Intensity models were trained both on simulated and real datasets. The trained models were used to segment the left ventricle (LV) and right ventricle (RV) from real datasets. Segmentations were also obtained with and without reliability information. Performance was evaluated with point-to-surface and volume errors. Simulated intensity models obtained average accuracy comparable to inter-observer variability for LV segmentation. The inclusion of reliability information reduced volume errors in hypertrophic patients (EF errors from 17 ± 57% to 10 ± 18% LV MASS errors from -27 ± 22 g to -14 ± 25 g), and in heart failure patients (EF errors from -8 ± 42% to -5 ± 14%). The RV model of the simulated images needs further improvement to better resemble image intensities around the myocardial edges. Both for real and simulated models, reliability information increased segmentation robustness without penalizing accuracy.

  19. Segmentation and reconstruction of the 3D geometry of the middle and inner ear

    Directory of Open Access Journals (Sweden)

    Lu Yanfei

    2017-01-01

    Full Text Available The anatomical model of the ear is of great importance in the design of ossicular prosthesis, cochlear implant electrodes, as well as for the preoperative planning and navigation of surgery. By means of micro-computed tomography (micro-CT and technology of 3D reconstruction, an anatomical model of the middle and inner ear was built. Region of interest includes the ossicular chain (malleus, incus, and stapes, cochlea (scala vestibule-ST, scala tympani-ST, basilar membrane-BM, spiral ligament-SL and osseous spiral lamina-OSL, tympanic membrane-TM, oval window membrane-OWM, round window membrane-OWM and stapedial annular ligament-SAL. The micro-CT images of a cadaver’s temporal bone were acquired by “SkyScan 1076” (Kontich, Belgium, www.skyscan.be and then reconstructed to cross-section images by SkyScan NRecon™ (v1.6.10.4. The image processing and 3D geometry reconstruction of temporal bone were performed by software Mimics® (v14.0, Materialise NV, Leuven, Belgium. The obtained structures are measured and validated against literature data and the results are in good agreement.

  20. A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation.

    Science.gov (United States)

    Heimann, Tobias; Münzing, Sascha; Meinzer, Hans-Peter; Wolf, Ivo

    2007-01-01

    We present a novel method for the segmentation of volumetric images, which is especially suitable for highly variable soft tissue structures. Core of the algorithm is a statistical shape model (SSM) of the structure of interest. A global search with an evolutionary algorithm is employed to detect suitable initial parameters for the model, which are subsequently optimized by a local search similar to the Active Shape mechanism. After that, a deformable mesh with the same topology as the SSM is used for the final segmentation: While external forces strive to maximize the posterior probability of the mesh given the local appearance around the boundary, internal forces governed by tension and rigidity terms keep the shape similar to the underlying SSM. To prevent outliers and increase robustness, we determine the applied external forces by an algorithm for optimal surface detection with smoothness constraints. The approach is evaluated on 54 CT images of the liver and reaches an average surface distance of 1.6 +/- 0.5 mm in comparison to manual reference segmentations.

  1. Automatic histogram-based segmentation of white matter hyperintensities using 3D FLAIR images

    Science.gov (United States)

    Simões, Rita; Slump, Cornelis; Moenninghoff, Christoph; Wanke, Isabel; Dlugaj, Martha; Weimar, Christian

    2012-03-01

    White matter hyperintensities are known to play a role in the cognitive decline experienced by patients suffering from neurological diseases. Therefore, accurately detecting and monitoring these lesions is of importance. Automatic methods for segmenting white matter lesions typically use multimodal MRI data. Furthermore, many methods use a training set to perform a classification task or to determine necessary parameters. In this work, we describe and evaluate an unsupervised segmentation method that is based solely on the histogram of FLAIR images. It approximates the histogram by a mixture of three Gaussians in order to find an appropriate threshold for white matter hyperintensities. We use a context-sensitive Expectation-Maximization method to determine the Gaussian mixture parameters. The segmentation is subsequently corrected for false positives using the knowledge of the location of typical FLAIR artifacts. A preliminary validation with the ground truth on 6 patients revealed a Similarity Index of 0.73 +/- 0.10, indicating that the method is comparable to others in the literature which require multimodal MRI and/or a preliminary training step.

  2. Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling.

    Science.gov (United States)

    Şener, Emre; Mumcuoglu, Erkan U; Hamcan, Salih

    2016-02-01

    Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification. In this study, a Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin. The method presented incorporates information fusion from multiple modalities, modelling of image resolution (measurement blurring), image noise, two priors helping to reduce noise and partial volume. Image resolution modelling employed facilitates resolution enhancement and superresolution capabilities during image segmentation. Regularization based on isotropic and directional Markov Random Field priors is integrated. The Bayesian model is solved iteratively yielding tissue class labels at every voxel of the image. Sub-methods as variations of the main method are generated by using a combination of the models. Testing of the sub-methods is performed on two patients using single modality three-dimensional (3D) image (magnetic resonance, MR or computerized tomography, CT) as well as registered MR-CT images with information fusion. Numerical, visual and statistical analyses of the methods are conducted. High segmentation accuracy values are obtained by the use of image resolution and partial volume models as well as information fusion from MR and CT images. The methods are also compared with our Bayesian segmentation method proposed in a previous study. The performance is found to be similar to our previous Bayesian approach, but the presented methods here eliminates ad hoc parameter tuning needed by the previous approach which is system and data acquisition setting dependent. The Bayesian approach presented provides resolution enhanced segmentation of very thin structures of the human head. Meanwhile, free parameters of the algorithm can be adjusted for different imaging systems and data acquisition settings in a more

  3. SEGMENTATION OF 2D AND 3D TEXTURES FROM ESTIMATES OF THE LOCAL ORIENTATION

    Directory of Open Access Journals (Sweden)

    Dominique Jeulin

    2011-05-01

    Full Text Available We use a method to estimate local orientations in the n-dimensional space from the covariance matrix of the gradient, which can be implemented either in the image space or in the Fourier space. In a second step, two methods allow us to detect sudden changes of orientation in images. The first one uses an index of confidence of the estimated orientation, and the second one the detection of minima of scalar products in a neighbourhood. This is illustrated on 2D Transmission Electrons Microscope images of cellulose cryofracture (to display the organisation of cellulose whiskers and the points of germination, and to 3D images of a TA6V alloy (lamellar microstructure obtained by microtomography.

  4. The Impact of Different Levels of Adaptive Iterative Dose Reduction 3D on Image Quality of 320-Row Coronary CT Angiography: A Clinical Trial.

    Directory of Open Access Journals (Sweden)

    Sarah Feger

    Full Text Available The aim of this study was the systematic image quality evaluation of coronary CT angiography (CTA, reconstructed with the 3 different levels of adaptive iterative dose reduction (AIDR 3D and compared to filtered back projection (FBP with quantum denoising software (QDS.Standard-dose CTA raw data of 30 patients with mean radiation dose of 3.2 ± 2.6 mSv were reconstructed using AIDR 3D mild, standard, strong and compared to FBP/QDS. Objective image quality comparison (signal, noise, signal-to-noise ratio (SNR, contrast-to-noise ratio (CNR, contour sharpness was performed using 21 measurement points per patient, including measurements in each coronary artery from proximal to distal.Objective image quality parameters improved with increasing levels of AIDR 3D. Noise was lowest in AIDR 3D strong (p ≤ 0.001 at 20/21 measurement points; compared with FBP/QDS. Signal and contour sharpness analysis showed no significant difference between the reconstruction algorithms for most measurement points. Best coronary SNR and CNR were achieved with AIDR 3D strong. No loss of SNR or CNR in distal segments was seen with AIDR 3D as compared to FBP.On standard-dose coronary CTA images, AIDR 3D strong showed higher objective image quality than FBP/QDS without reducing contour sharpness.Clinicaltrials.gov NCT00967876.

  5. Segmentation, surface rendering, and surface simplification of 3-D skull images for the repair of a large skull defect

    Science.gov (United States)

    Wan, Weibing; Shi, Pengfei; Li, Shuguang

    2009-10-01

    Given the potential demonstrated by research into bone-tissue engineering, the use of medical image data for the rapid prototyping (RP) of scaffolds is a subject worthy of research. Computer-aided design and manufacture and medical imaging have created new possibilities for RP. Accurate and efficient design and fabrication of anatomic models is critical to these applications. We explore the application of RP computational methods to the repair of a pediatric skull defect. The focus of this study is the segmentation of the defect region seen in computerized tomography (CT) slice images of this patient's skull and the three-dimensional (3-D) surface rendering of the patient's CT-scan data. We see if our segmentation and surface rendering software can improve the generation of an implant model to fill a skull defect.

  6. Automated torso organ segmentation from 3D CT images using structured perceptron and dual decomposition

    Science.gov (United States)

    Nimura, Yukitaka; Hayashi, Yuichiro; Kitasaka, Takayuki; Mori, Kensaku

    2015-03-01

    This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coefficients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.

  7. 3D adaptive grid MHD simulations of the global heliosphere with self- consistent fluid neutral hydrogen

    Science.gov (United States)

    Opher, M.; Liewer, P.; Gombosi, T.; Manchester, W.; Dezeeuw, D.; Powell, K.; Sokolov, I.; Toth, G.

    A three dimensional adaptive grid magnetohydrodynamic (MHD) model of the interaction of the solar wind with the local interstellar medium is presented. The code used is the BATS-R-US time-dependent adaptive grid three-dimensional magnetohydrodynamic, which is similar to the code used by Linde et al. JGR, 103, 1889 (1998). The magnetic field of both the solar wind and the interstellar medium are included. The latitute dependence of the solar wind is also taken into account. The neutral atoms are included self-consistently as a fluid, without assuming constant the density, velocity or temperature as previous 3D MHD studies. The location of the termination shock and heliopause in the steady state solution for different values and directions of interstellar magnetic field are presented and compared with previous results. We also present results where we isolated the effects of neutrals and magnetic field showing their relative importance, in particular the heliopause.

  8. Comparative study of diverse model building strategies for 3D-ASM segmentation of dynamic gated SPECT data

    Science.gov (United States)

    Tobon-Gomez, C.; Butakoff, C.; Ordas, S.; Aguade, S.; Frangi, A. F.

    2007-03-01

    Over the course of the last two decades, myocardial perfusion with Single Photon Emission Computed Tomography (SPECT) has emerged as an established and well-validated method for assessing myocardial ischemia, viability, and function. Gated-SPECT imaging integrates traditional perfusion information along with global left ventricular function. Despite of these advantages, inherent limitations of SPECT imaging yield a challenging segmentation problem, since an error of only one voxel along the chamber surface may generate a huge difference in volume calculation. In previous works we implemented a 3-D statistical model-based algorithm for Left Ventricle (LV) segmentation of in dynamic perfusion SPECT studies. The present work evaluates the relevance of training a different Active Shape Model (ASM) for each frame of the gated SPECT imaging acquisition in terms of their subsequent segmentation accuracy. Models are subsequently employed to segment the LV cavity of gated SPECT studies of a virtual population. The evaluation is accomplished by comparing point-to-surface (P2S) and volume errors, both against a proper Gold Standard. The dataset comprised 40 voxel phantoms (NCAT, Johns Hopkins, University of of North Carolina). Monte-Carlo simulations were generated with SIMIND (Lund University) and reconstructed to tomographic slices with ASPIRE (University of Michigan).

  9. 3D multimodal spatial fuzzy segmentation of intramuscular connective and adipose tissue from ultrashort TE MR images of calf muscle.

    Science.gov (United States)

    Ugarte, Vincent; Sinha, Usha; Malis, Vadim; Csapo, Robert; Sinha, Shantanu

    2017-02-01

    To develop and evaluate an automated algorithm to segment intramuscular adipose (IMAT) and connective (IMCT) tissue from musculoskeletal MRI images acquired with a dual echo Ultrashort TE (UTE) sequence. The dual echo images and calculated structure tensor images are the inputs to the multichannel fuzzy cluster mean (MCFCM) algorithm. Modifications to the basic multichannel fuzzy cluster mean include an adaptive spatial term and bias shading correction. The algorithm was tested on digital phantoms simulating IMAT/IMCT tissue under varying conditions of image noise and bias and on ten subjects with varying amounts of IMAT/IMCT. The MCFCM including the adaptive spatial term and bias shading correction performed better than the original MCFCM and adaptive spatial MCFCM algorithms. IMAT/IMCT was segmented from the unsmoothed simulated phantom data with a mean Dice coefficient of 0.933 ±0.001 when contrast-to-noise (CNR) was 140 and bias was varied between 30% and 65%. The algorithm yielded accurate in vivo segmentations of IMAT/IMCT with a mean Dice coefficient of 0.977 ±0.066. The proposed algorithm is completely automated and yielded accurate segmentation of intramuscular adipose and connective tissue in the digital phantom and in human calf data. Magn Reson Med 77:870-883, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  10. 3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images.

    Science.gov (United States)

    Li, Xiaomeng; Dou, Qi; Chen, Hao; Fu, Chi-Wing; Qi, Xiaojuan; Belavý, Daniel L; Armbrecht, Gabriele; Felsenberg, Dieter; Zheng, Guoyan; Heng, Pheng-Ann

    2018-02-01

    Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The localization and segmentation of IVDs are important for spine disease diagnosis and measurement quantification. However, manual annotation is time-consuming and error-prone with limited reproducibility, particularly for volumetric data. In this work, our goal is to develop an automatic and accurate method based on fully convolutional networks (FCN) for the localization and segmentation of IVDs from multi-modality 3D MR data. Compared with single modality data, multi-modality MR images provide complementary contextual information, which contributes to better recognition performance. However, how to effectively integrate such multi-modality information to generate accurate segmentation results remains to be further explored. In this paper, we present a novel multi-scale and modality dropout learning framework to locate and segment IVDs from four-modality MR images. First, we design a 3D multi-scale context fully convolutional network, which processes the input data in multiple scales of context and then merges the high-level features to enhance the representation capability of the network for handling the scale variation of anatomical structures. Second, to harness the complementary information from different modalities, we present a random modality voxel dropout strategy which alleviates the co-adaption issue and increases the discriminative capability of the network. Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm. We further conduct extensive experiments on the extended dataset to validate our method. We demonstrate that the proposed modality dropout strategy with multi-modality images as contextual information improved the segmentation accuracy significantly. Furthermore, experiments conducted on

  11. An adaptive mean-shift framework for MRI brain segmentation.

    Science.gov (United States)

    Mayer, Arnaldo; Greenspan, Hayit

    2009-08-01

    An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and Cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.

  12. Ellipsoid Segmentation Model for Analyzing Light-Attenuated 3D Confocal Image Stacks of Fluorescent Multi-Cellular Spheroids

    Science.gov (United States)

    Barbier, Michaël; Jaensch, Steffen; Cornelissen, Frans; Vidic, Suzana; Gjerde, Kjersti; de Hoogt, Ronald; Graeser, Ralph; Gustin, Emmanuel; Chong, Yolanda T.

    2016-01-01

    In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation. PMID:27303813

  13. NCC-RANSAC: A Fast Plane Extraction Method for 3-D Range Data Segmentation

    Science.gov (United States)

    Qian, Xiangfei; Ye, Cang

    2015-01-01

    This paper presents a new plane extraction (PE) method based on the random sample consensus (RANSAC) approach. The generic RANSAC-based PE algorithm may over-extract a plane, and it may fail in case of a multistep scene where the RANSAC procedure results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC PE algorithm successfully overcomes the latter limitation if the inlier patches are separate. However, it fails if the inlier patches are connected. A typical scenario is a stairway with a stair wall where the RANSAC plane-fitting procedure results in inliers patches in the tread, riser, and stair wall planes. They connect together and form a plane. The proposed method, called normal-coherence CC-RANSAC (NCC-RANSAC), performs a normal coherence check to all data points of the inlier patches and removes the data points whose normal directions are contradictory to that of the fitted plane. This process results in separate inlier patches, each of which is treated as a candidate plane. A recursive plane clustering process is then executed to grow each of the candidate planes until all planes are extracted in their entireties. The RANSAC plane-fitting and the recursive plane clustering processes are repeated until no more planes are found. A probabilistic model is introduced to predict the success probability of the NCC-RANSAC algorithm and validated with real data of a 3-D time-of-flight camera–SwissRanger SR4000. Experimental results demonstrate that the proposed method extracts more accurate planes with less computational time than the existing RANSAC-based methods. PMID:24771605

  14. Geometric and topological feature extraction of linear segments from 2D cross-section data of 3D point clouds

    Science.gov (United States)

    Ramamurthy, Rajesh; Harding, Kevin; Du, Xiaoming; Lucas, Vincent; Liao, Yi; Paul, Ratnadeep; Jia, Tao

    2015-05-01

    Optical measurement techniques are often employed to digitally capture three dimensional shapes of components. The digital data density output from these probes range from a few discrete points to exceeding millions of points in the point cloud. The point cloud taken as a whole represents a discretized measurement of the actual 3D shape of the surface of the component inspected to the measurement resolution of the sensor. Embedded within the measurement are the various features of the part that make up its overall shape. Part designers are often interested in the feature information since those relate directly to part function and to the analytical models used to develop the part design. Furthermore, tolerances are added to these dimensional features, making their extraction a requirement for the manufacturing quality plan of the product. The task of "extracting" these design features from the point cloud is a post processing task. Due to measurement repeatability and cycle time requirements often automated feature extraction from measurement data is required. The presence of non-ideal features such as high frequency optical noise and surface roughness can significantly complicate this feature extraction process. This research describes a robust process for extracting linear and arc segments from general 2D point clouds, to a prescribed tolerance. The feature extraction process generates the topology, specifically the number of linear and arc segments, and the geometry equations of the linear and arc segments automatically from the input 2D point clouds. This general feature extraction methodology has been employed as an integral part of the automated post processing algorithms of 3D data of fine features.

  15. CT and MRI assessment and characterization using segmentation and 3D modeling techniques: applications to muscle, bone and brain

    Directory of Open Access Journals (Sweden)

    Paolo Gargiulo

    2014-03-01

    Full Text Available This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain.This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN denervation, 2. muscle recovery as induced by functional electrical stimulation (FES, 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological

  16. CT and MRI Assessment and Characterization Using Segmentation and 3D Modeling Techniques: Applications to Muscle, Bone and Brain.

    Science.gov (United States)

    Gargiulo, Paolo; Helgason, Thordur; Ramon, Ceon; Jr, Halldór Jónsson; Carraro, Ugo

    2014-03-31

    This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain. This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN) denervation, 2. muscle recovery as induced by functional electrical stimulation (FES), 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD) and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological conditions by

  17. Automatic segmentation of colon in 3D CT images and removal of opacified fluid using cascade feed forward neural network.

    Science.gov (United States)

    Gayathri Devi, K; Radhakrishnan, R

    2015-01-01

    Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT) images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result.

  18. 3D design and electric simulation of a silicon drift detector using a spiral biasing adapter

    Science.gov (United States)

    Li, Yu-yun; Xiong, Bo; Li, Zheng

    2016-09-01

    The detector system of combining a spiral biasing adapter (SBA) with a silicon drift detector (SBA-SDD) is largely different from the traditional silicon drift detector (SDD), including the spiral SDD. It has a spiral biasing adapter of the same design as a traditional spiral SDD and an SDD with concentric rings having the same radius. Compared with the traditional spiral SDD, the SBA-SDD separates the spiral's functions of biasing adapter and the p-n junction definition. In this paper, the SBA-SDD is simulated using a Sentaurus TCAD tool, which is a full 3D device simulation tool. The simulated electric characteristics include electric potential, electric field, electron concentration, and single event effect. Because of the special design of the SBA-SDD, the SBA can generate an optimum drift electric field in the SDD, comparable with the conventional spiral SDD, while the SDD can be designed with concentric rings to reduce surface area. Also the current and heat generated in the SBA are separated from the SDD. To study the single event response, we simulated the induced current caused by incident heavy ions (20 and 50 μm penetration length) with different linear energy transfer (LET). The SBA-SDD can be used just like a conventional SDD, such as X-ray detector for energy spectroscopy and imaging, etc.

  19. A novel 3-D jerk chaotic system with three quadratic nonlinearities and its adaptive control

    Directory of Open Access Journals (Sweden)

    Vaidyanathan Sundarapandian

    2016-03-01

    Full Text Available This paper announces an eight-term novel 3-D jerk chaotic system with three quadratic nonlinearities. The phase portraits of the novel jerk chaotic system are displayed and the qualitative properties of the jerk system are described. The novel jerk chaotic system has two equilibrium points, which are saddle-foci and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1 = 0.20572,L2 = 0 and L3 = −1.20824. Since the sum of the Lyapunov exponents of the jerk chaotic system is negative, we conclude that the chaotic system is dissipative. The Kaplan-Yorke dimension of the novel jerk chaotic system is derived as DKY = 2.17026. Next, an adaptive controller is designed via backstepping control method to globally stabilize the novel jerk chaotic system with unknown parameters. Moreover, an adaptive controller is also designed via backstepping control method to achieve global chaos synchronization of the identical jerk chaotic systems with unknown parameters. The backstepping control method is a recursive procedure that links the choice of a Lyapunov function with the design of a controller and guarantees global asymptotic stability of strict feedback systems. MATLAB simulations have been depicted to illustrate the phase portraits of the novel jerk chaotic system and also the adaptive backstepping control results.

  20. Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room.

    Science.gov (United States)

    Lo Presti, Giuseppe; Carbone, Marina; Ciriaci, Damiano; Aramini, Daniele; Ferrari, Mauro; Ferrari, Vincenzo

    2015-10-01

    Patient-specific 3D models obtained by the segmentation of volumetric diagnostic images play an increasingly important role in surgical planning. Surgeons use the virtual models reconstructed through segmentation to plan challenging surgeries. Many solutions exist for the different anatomical districts and surgical interventions. The possibility to bring the 3D virtual reconstructions with native radiological images in the operating room is essential for fostering the use of intraoperative planning. To the best of our knowledge, current DICOM viewers are not able to simultaneously connect to the picture archiving and communication system (PACS) and import 3D models generated by external platforms to allow a straight integration in the operating room. A total of 26 DICOM viewers were evaluated: 22 open source and four commercial. Two DICOM viewers can connect to PACS and import segmentations achieved by other applications: Synapse 3D® by Fujifilm and OsiriX by University of Geneva. We developed a software network that converts diffuse visual tool kit (VTK) format 3D model segmentations, obtained by any software platform, to a DICOM format that can be displayed using OsiriX or Synapse 3D. Both OsiriX and Synapse 3D were suitable for our purposes and had comparable performance. Although Synapse 3D loads native images and segmentations faster, the main benefits of OsiriX are its user-friendly loading of elaborated images and it being both free of charge and open source.

  1. Adaptive Probabilistic Tracking Embedded in Smart Cameras for Distributed Surveillance in a 3D Model

    Directory of Open Access Journals (Sweden)

    Fleck Sven

    2007-01-01

    Full Text Available Tracking applications based on distributed and embedded sensor networks are emerging today, both in the fields of surveillance and industrial vision. Traditional centralized approaches have several drawbacks, due to limited communication bandwidth, computational requirements, and thus limited spatial camera resolution and frame rate. In this article, we present network-enabled smart cameras for probabilistic tracking. They are capable of tracking objects adaptively in real time and offer a very bandwidthconservative approach, as the whole computation is performed embedded in each smart camera and only the tracking results are transmitted, which are on a higher level of abstraction. Based on this, we present a distributed surveillance system. The smart cameras' tracking results are embedded in an integrated 3D environment as live textures and can be viewed from arbitrary perspectives. Also a georeferenced live visualization embedded in Google Earth is presented.

  2. Adaptive Probabilistic Tracking Embedded in Smart Cameras for Distributed Surveillance in a 3D Model

    Directory of Open Access Journals (Sweden)

    Sven Fleck

    2006-12-01

    Full Text Available Tracking applications based on distributed and embedded sensor networks are emerging today, both in the fields of surveillance and industrial vision. Traditional centralized approaches have several drawbacks, due to limited communication bandwidth, computational requirements, and thus limited spatial camera resolution and frame rate. In this article, we present network-enabled smart cameras for probabilistic tracking. They are capable of tracking objects adaptively in real time and offer a very bandwidthconservative approach, as the whole computation is performed embedded in each smart camera and only the tracking results are transmitted, which are on a higher level of abstraction. Based on this, we present a distributed surveillance system. The smart cameras' tracking results are embedded in an integrated 3D environment as live textures and can be viewed from arbitrary perspectives. Also a georeferenced live visualization embedded in Google Earth is presented.

  3. Compressible magma/mantle dynamics: 3-D, adaptive simulations in ASPECT

    Science.gov (United States)

    Dannberg, Juliane; Heister, Timo

    2016-12-01

    Melt generation and migration are an important link between surface processes and the thermal and chemical evolution of the Earth's interior. However, their vastly different timescales make it difficult to study mantle convection and melt migration in a unified framework, especially for 3-D global models. And although experiments suggest an increase in melt volume of up to 20 per cent from the depth of melt generation to the surface, previous computations have neglected the individual compressibilities of the solid and the fluid phase. Here, we describe our extension of the finite element mantle convection code ASPECT that adds melt generation and migration. We use the original compressible formulation of the McKenzie equations, augmented by an equation for the conservation of energy. Applying adaptive mesh refinement to this type of problems is particularly advantageous, as the resolution can be increased in areas where melt is present and viscosity gradients are high, whereas a lower resolution is sufficient in regions without melt. Together with a high-performance, massively parallel implementation, this allows for high-resolution, 3-D, compressible, global mantle convection simulations coupled with melt migration. We evaluate the functionality and potential of this method using a series of benchmarks and model setups, compare results of the compressible and incompressible formulation, and show the effectiveness of adaptive mesh refinement when applied to melt migration. Our model of magma dynamics provides a framework for modelling processes on different scales and investigating links between processes occurring in the deep mantle and melt generation and migration. This approach could prove particularly useful applied to modelling the generation of komatiites or other melts originating in greater depths. The implementation is available in the Open Source ASPECT repository.

  4. Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancementa)

    Science.gov (United States)

    Munbodh, Reshma; Jaffray, David A.; Moseley, Douglas J.; Chen, Zhe; Knisely, Jonathan P. S.; Cathier, Pascal; Duncan, James S.

    2009-01-01

    The objective of this study was to develop a fully automated two-dimensional (2D)–three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with “sticks,” short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine. PMID:16752576

  5. Adaptive enhancement and visualization techniques for 3D THz images of breast cancer tumors

    Science.gov (United States)

    Wu, Yuhao; Bowman, Tyler; Gauch, John; El-Shenawee, Magda

    2016-03-01

    This paper evaluates image enhancement and visualization techniques for pulsed terahertz (THz) images of tissue samples. Specifically, our research objective is to effectively differentiate between heterogeneous regions of breast tissues that contain tumors diagnosed as triple negative infiltrating ductal carcinoma (IDC). Tissue slices and blocks of varying thicknesses were prepared and scanned using our lab's THz pulsed imaging system. One of the challenges we have encountered in visualizing the obtained images and differentiating between healthy and cancerous regions of the tissues is that most THz images have a low level of details and narrow contrast, making it difficult to accurately identify and visualize the margins around the IDC. To overcome this problem, we have applied and evaluated a number of image processing techniques to the scanned 3D THz images. In particular, we employed various spatial filtering and intensity transformation techniques to emphasize the small details in the images and adjust the image contrast. For each of these methods, we investigated how varying filter sizes and parameters affect the amount of enhancement applied to the images. Our experimentation shows that several image processing techniques are effective in producing THz images of breast tissue samples that contain distinguishable details, making further segmentation of the different image regions promising.

  6. A new 3-D jerk chaotic system with two cubic nonlinearities and its adaptive backstepping control

    Directory of Open Access Journals (Sweden)

    Vaidyanathan Sundarapandian

    2017-09-01

    Full Text Available This paper presents a new seven-term 3-D jerk chaotic system with two cubic nonlinearities. The phase portraits of the novel jerk chaotic system are displayed and the qualitative properties of the jerk system are described. The novel jerk chaotic system has a unique equilibrium at the origin, which is a saddle-focus and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1 = 0:2974, L2 = 0 and L3 = −3:8974. Since the sum of the Lyapunov exponents of the jerk chaotic system is negative, we conclude that the chaotic system is dissipative. The Kaplan-Yorke dimension of the new jerk chaotic system is found as DKY = 2:0763. Next, an adaptive backstepping controller is designed to globally stabilize the new jerk chaotic system with unknown parameters. Moreover, an adaptive backstepping controller is also designed to achieve global chaos synchronization of the identical jerk chaotic systems with unknown parameters. The backstepping control method is a recursive procedure that links the choice of a Lyapunov function with the design of a controller and guarantees global asymptotic stability of strict feedback systems. MATLAB simulations are shown to illustrate all the main results derived in this work.

  7. Adaptive Multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model

    CERN Document Server

    Navarro, C A; Deng, Youjin

    2015-01-01

    The study of disordered spin systems through Monte Carlo simulations has proven to be a hard task due to the adverse energy landscape present at the low temperature regime, making it difficult for the simulation to escape from a local minimum. Replica based algorithms such as the Exchange Monte Carlo (also known as parallel tempering) are effective at overcoming this problem, reaching equilibrium on disordered spin systems such as the Spin Glass or Random Field models, by exchanging information between replicas of neighbor temperatures. In this work we present a multi-GPU Exchange Monte Carlo method designed for the simulation of the 3D Random Field Model. The implementation is based on a two-level parallelization scheme that allows the method to scale its performance in the presence of faster and GPUs as well as multiple GPUs. In addition, we modified the original algorithm by adapting the set of temperatures according to the exchange rate observed from short trial runs, leading to an increased exchange rate...

  8. A Rapid and Efficient 2D/3D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell Image Data

    Directory of Open Access Journals (Sweden)

    Xinghua Lou

    2014-03-01

    Full Text Available Segmentation is a fundamental problem that dominates the success of microscopic image analysis. In almost 25 years of cell detection software development, there is still no single piece of commercial software that works well in practice when applied to early mouse embryo or stem cell image data. To address this need, we developed MINS (modular interactive nuclear segmentation as a MATLAB/C++-based segmentation tool tailored for counting cells and fluorescent intensity measurements of 2D and 3D image data. Our aim was to develop a tool that is accurate and efficient yet straightforward and user friendly. The MINS pipeline comprises three major cascaded modules: detection, segmentation, and cell position classification. An extensive evaluation of MINS on both 2D and 3D images, and comparison to related tools, reveals improvements in segmentation accuracy and usability. Thus, its accuracy and ease of use will allow MINS to be implemented for routine single-cell-level image analyses.

  9. Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D improves chest CT image quality and reduces radiation exposure.

    Directory of Open Access Journals (Sweden)

    Tsuneo Yamashiro

    Full Text Available To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D for image quality improvement and dose reduction for chest computed tomography (CT.Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D. Using a 5-point scale from 1 (non-diagnostic to 5 (excellent, three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease, and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts. Differences in these scores were assessed by Scheffe's test.At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001 and all mediastinal measurements (p<0.01. For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001, and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA.For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.

  10. Stereoscopic-3D display design: a new paradigm with Intel Adaptive Stable Image Technology [IA-SIT

    Science.gov (United States)

    Jain, Sunil

    2012-03-01

    Stereoscopic-3D (S3D) proliferation on personal computers (PC) is mired by several technical and business challenges: a) viewing discomfort due to cross-talk amongst stereo images; b) high system cost; and c) restricted content availability. Users expect S3D visual quality to be better than, or at least equal to, what they are used to enjoying on 2D in terms of resolution, pixel density, color, and interactivity. Intel Adaptive Stable Image Technology (IA-SIT) is a foundational technology, successfully developed to resolve S3D system design challenges and deliver high quality 3D visualization at PC price points. Optimizations in display driver, panel timing firmware, backlight hardware, eyewear optical stack, and synch mechanism combined can help accomplish this goal. Agnostic to refresh rate, IA-SIT will scale with shrinking of display transistors and improvements in liquid crystal and LED materials. Industry could profusely benefit from the following calls to action:- 1) Adopt 'IA-SIT S3D Mode' in panel specs (via VESA) to help panel makers monetize S3D; 2) Adopt 'IA-SIT Eyewear Universal Optical Stack' and algorithm (via CEA) to help PC peripheral makers develop stylish glasses; 3) Adopt 'IA-SIT Real Time Profile' for sub-100uS latency control (via BT Sig) to extend BT into S3D; and 4) Adopt 'IA-SIT Architecture' for Monitors and TVs to monetize via PC attach.

  11. A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system

    Directory of Open Access Journals (Sweden)

    N Byrne

    2016-04-01

    Full Text Available Background Shortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes. Methods A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ and segmentation software were recorded. Results Totally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports. The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992–2015. The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced. Conclusions and implication of key findings Predominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods.

  12. Auto-context and its application to high-level vision tasks and 3D brain image segmentation.

    Science.gov (United States)

    Tu, Zhuowen; Bai, Xiang

    2010-10-01

    The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with an image appearance model, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design in which the modeling and computing stages are studied in isolation. In this paper, we propose a learning algorithm, auto-context. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps created by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates until convergence. Auto-context integrates low-level and context information by fusing a large number of low-level appearance features with context and implicit shape information. The resulting discriminative algorithm is general and easy to implement. Under nearly the same parameter settings in training, we apply the algorithm to three challenging vision applications: foreground/background segregation, human body configuration estimation, and scene region labeling. Moreover, context also plays a very important role in medical/brain images where the anatomical structures are mostly constrained to relatively fixed positions. With only some slight changes resulting from using 3D instead of 2D features, the auto-context algorithm applied to brain MRI image segmentation is shown to outperform state-of-the-art algorithms specifically designed for this domain. Furthermore, the scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems.

  13. Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

    Directory of Open Access Journals (Sweden)

    Trong-Ngoc Le

    2016-01-01

    Full Text Available Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN, which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the “ground truth.” Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.

  14. Registration of 3D tracked ultrasonic spinal images to segmented CT images for technology-guided therapy

    Science.gov (United States)

    Muratore, Diane M.; Herring, Jeannette L.; Dawant, Benoit M.; Galloway, Robert L., Jr.

    2000-04-01

    As a prerequisite to performing minimally-invasive spinal surgery (MISS) with technology-guided therapy (TGT), researchers at Vanderbilt University have proposed to mathematically align the physical space of the patient with preoperative images through a surface-based registration. In order to support closed-back spinal surgeries, we have selected a non-invasive, portable imaging modality for obtaining intra-operative images, namely ultrasound (U/S). The preliminary work for the application of TGT to spinal cases has been performed on a spine phantom, scanned with an optically-tracked U/S transducer. The lumbar vertebral surface was extracted from the U/S images, and the surface pixels were converted into 3D physical-space coordinates. This set of U/S surface points was divided into a test set and a target set to be used in registration and error measurement, respectively. The test set of U/S points was registered to segmented CT spinal images of the same phantom spine using a modification of the Besl-McKay Iterative Closest Point algorithm. In a qualitative analysis of the registration, the results look favorable. The U/S points closely align with the corresponding CT surface in every image slice. By incorporating TGT into minimally-invasive spinal surgeries, the procedures are expected to yield reduced injury to normal spinal tissue and hence quicker recovery time for the patient.

  15. Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3D segmentation algorithms

    Science.gov (United States)

    2011-01-01

    Background Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods. Results We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation. Conclusions We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss/. PMID:21668958

  16. Initial experience with adaptive iterative dose reduction 3D to reduce radiation dose in computed tomographic urography.

    Science.gov (United States)

    Juri, Hiroshi; Matsuki, Mitsuru; Itou, Yasushi; Inada, Yuki; Nakai, Go; Azuma, Haruhito; Narumi, Yoshifumi

    2013-01-01

    This study aimed to investigate the feasibility of low-dose computed tomographic (CT) urography with adaptive iterative dose reduction 3D (AIDR 3D). Thirty patients underwent routine-dose CT scans with filtered back projection and low-dose CT scans with AIDR 3D in the excretory phase of CT urography. Visual evaluations were performed with respect to internal image noises, sharpness, streak artifacts, and diagnostic acceptability. Quantitative measures of the image noise and radiation dose were also obtained. All results were compared on the basis of body mass index (BMI). At visual evaluations, streak artifacts in the urinary bladder were statistically weaker on low-dose CT than on routine-dose CT in the axial and coronal images (P urography with AIDR 3D allows 45% reduction of radiation dose without degenerating of the image quality in the excretory phase independently to a BMI.

  17. Reconstruction 3D des structures adjacentes de l'articulation de la hanche par une segmentation multi-structures a l'aide des maillages surfaciques triangulaires

    Science.gov (United States)

    Meghoufel, Brahim

    A new 3D reconstruction technique of the two adjacent structures forming the hip joint from the 3D CT-scans images has been developed. The femoral head and the acetabulum are reconstructed using a 3D multi-structure segmentation method for the adjacent surfaces which is based on the use of a 3D triangular surface meshes. This method begins with a preliminary hierarchical segmentation of the two structures, using one triangular mesh for each structure. The two resulting 3D meshes of the hierarchical segmentation are deployed into two planar 2D surfaces. We have used the umbrella deployment to deploy the femoral head mesh, and the parameterization 3D/2D to deploy the acetabulum mesh. The two planar generated surfaces are used to deploy the CT-scan volume around each structure. The surface of each structure is nearly planar in the corresponding deployed volume. The iterative method of minimal surfaces ensures the optimal identification of both sought surfaces from the deployed volumes. The last step of the 3D reconstruction method aims at detecting and correcting the overlap between the two structures. This 3D reconstruction method has been validated using a data base of 10 3D CT-scan images. The results of the 3D reconstructions seem satisfactory. The precision errors of these 3D reconstructions have been quantified by comparing the 3D reconstructions with an available manual gold standard. The errors resulting from the quantification are better than those available in the literature; the mean of those errors is 0,83 +/- 0,25 mm for acetabulum and 0,70 +/- 0,17 mm for the femoral head. The mean execution time of the 3D reconstruction of the two structures forming the hip joint has been estimated at approximately 3,0 +/- 0,3 min . The proposed method shows the potential of the solution which the image processing can provide to the surgeons in order to achieve their routine tasks. Such a method can be applied to every imaging modality.

  18. Multi-view 3D human pose estimation combining single-frame recovery, temporal integration and model adaptation

    NARCIS (Netherlands)

    Hofmann, M.; Gavrila, D.M.

    2009-01-01

    We present a system for the estimation of unconstrained 3D human upper body movement from multiple cameras. Its main novelty lies in the integration of three components: single frame pose recovery, temporal integration and model adaptation. Single frame pose recovery consists of a hypothesis

  19. Adaptive geometric tessellation for 3D reconstruction of anisotropically developing cells in multilayer tissues from sparse volumetric microscopy images.

    Directory of Open Access Journals (Sweden)

    Anirban Chakraborty

    Full Text Available The need for quantification of cell growth patterns in a multilayer, multi-cellular tissue necessitates the development of a 3D reconstruction technique that can estimate 3D shapes and sizes of individual cells from Confocal Microscopy (CLSM image slices. However, the current methods of 3D reconstruction using CLSM imaging require large number of image slices per cell. But, in case of Live Cell Imaging of an actively developing tissue, large depth resolution is not feasible in order to avoid damage to cells from prolonged exposure to laser radiation. In the present work, we have proposed an anisotropic Voronoi tessellation based 3D reconstruction framework for a tightly packed multilayer tissue with extreme z-sparsity (2-4 slices/cell and wide range of cell shapes and sizes. The proposed method, named as the 'Adaptive Quadratic Voronoi Tessellation' (AQVT, is capable of handling both the sparsity problem and the non-uniformity in cell shapes by estimating the tessellation parameters for each cell from the sparse data-points on its boundaries. We have tested the proposed 3D reconstruction method on time-lapse CLSM image stacks of the Arabidopsis Shoot Apical Meristem (SAM and have shown that the AQVT based reconstruction method can correctly estimate the 3D shapes of a large number of SAM cells.

  20. The two sides of complement C3d: evolution of electrostatics in a link between innate and adaptive immunity.

    Directory of Open Access Journals (Sweden)

    Chris A Kieslich

    Full Text Available The interaction between complement fragment C3d and complement receptor 2 (CR2 is a key aspect of complement immune system activation, and is a component in a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on Earth for the last 600 million years. However, the link between the complement system and adaptive immunity, which is formed through the association of the B-cell co-receptor complex, including the C3d-CR2 interaction, is a much more recent adaptation. Human C3d and CR2 have net charges of -1 and +7 respectively, and are believed to have evolved favoring the role of electrostatics in their functions. To investigate the role of electrostatics in the function and evolution of human C3d and CR2, we have applied electrostatic similarity methods to identify regions of evolutionarily conserved electrostatic potential based on 24 homologues of complement C3d and 4 homologues of CR2. We also examine the effects of structural perturbation, as introduced through molecular dynamics and mutations, on spatial distributions of electrostatic potential to identify perturbation resistant regions, generated by so-called electrostatic "hot-spots". Distributions of electrostatic similarity based on families of perturbed structures illustrate the presence of electrostatic "hot-spots" at the two functional sites of C3d, while the surface of CR2 lacks electrostatic "hot-spots" despite its excessively positive nature. We propose that the electrostatic "hot-spots" of C3d have evolved to optimize its dual-functionality (covalently attaching to pathogen surfaces and interaction with CR2, which are both necessary for the formation B-cell co-receptor complexes. Comparison of the perturbation resistance of the electrostatic character of the homologues of C3d suggests that there was an emergence of a new role of electrostatics, and a transition in the function of C3

  1. The two sides of complement C3d: evolution of electrostatics in a link between innate and adaptive immunity.

    Science.gov (United States)

    Kieslich, Chris A; Morikis, Dimitrios

    2012-01-01

    The interaction between complement fragment C3d and complement receptor 2 (CR2) is a key aspect of complement immune system activation, and is a component in a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on Earth for the last 600 million years. However, the link between the complement system and adaptive immunity, which is formed through the association of the B-cell co-receptor complex, including the C3d-CR2 interaction, is a much more recent adaptation. Human C3d and CR2 have net charges of -1 and +7 respectively, and are believed to have evolved favoring the role of electrostatics in their functions. To investigate the role of electrostatics in the function and evolution of human C3d and CR2, we have applied electrostatic similarity methods to identify regions of evolutionarily conserved electrostatic potential based on 24 homologues of complement C3d and 4 homologues of CR2. We also examine the effects of structural perturbation, as introduced through molecular dynamics and mutations, on spatial distributions of electrostatic potential to identify perturbation resistant regions, generated by so-called electrostatic "hot-spots". Distributions of electrostatic similarity based on families of perturbed structures illustrate the presence of electrostatic "hot-spots" at the two functional sites of C3d, while the surface of CR2 lacks electrostatic "hot-spots" despite its excessively positive nature. We propose that the electrostatic "hot-spots" of C3d have evolved to optimize its dual-functionality (covalently attaching to pathogen surfaces and interaction with CR2), which are both necessary for the formation B-cell co-receptor complexes. Comparison of the perturbation resistance of the electrostatic character of the homologues of C3d suggests that there was an emergence of a new role of electrostatics, and a transition in the function of C3d, after the

  2. SU-F-J-93: Automated Segmentation of High-Resolution 3D WholeBrain Spectroscopic MRI for Glioblastoma Treatment Planning

    Energy Technology Data Exchange (ETDEWEB)

    Schreibmann, E; Shu, H [Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA (United States); Cordova, J; Gurbani, S; Holder, C; Cooper, L; Shim, H [Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA (United States)

    2016-06-15

    Purpose: We report on an automated segmentation algorithm for defining radiation therapy target volumes using spectroscopic MR images (sMRI) acquired at nominal voxel resolution of 100 microliters. Methods: Wholebrain sMRI combining 3D echo-planar spectroscopic imaging, generalized auto-calibrating partially-parallel acquisitions, and elliptical k-space encoding were conducted on 3T MRI scanner with 32-channel head coil array creating images. Metabolite maps generated include choline (Cho), creatine (Cr), and N-acetylaspartate (NAA), as well as Cho/NAA, Cho/Cr, and NAA/Cr ratio maps. Automated segmentation was achieved by concomitantly considering sMRI metabolite maps with standard contrast enhancing (CE) imaging in a pipeline that first uses the water signal for skull stripping. Subsequently, an initial blob of tumor region is identified by searching for regions of FLAIR abnormalities that also display reduced NAA activity using a mean ratio correlation and morphological filters. These regions are used as starting point for a geodesic level-set refinement that adapts the initial blob to the fine details specific to each metabolite. Results: Accuracy of the segmentation model was tested on a cohort of 12 patients that had sMRI datasets acquired pre, mid and post-treatment, providing a broad range of enhancement patterns. Compared to classical imaging, where heterogeneity in the tumor appearance and shape across posed a greater challenge to the algorithm, sMRI’s regions of abnormal activity were easily detected in the sMRI metabolite maps when combining the detail available in the standard imaging with the local enhancement produced by the metabolites. Results can be imported in the treatment planning, leading in general increase in the target volumes (GTV60) when using sMRI+CE MRI compared to the standard CE MRI alone. Conclusion: Integration of automated segmentation of sMRI metabolite maps into planning is feasible and will likely streamline acceptance of this

  3. Image-guided depth propagation for 2-D-to-3-D video conversion using superpixel matching and adaptive autoregressive model

    Science.gov (United States)

    Cai, Jiji; Jung, Cheolkon

    2017-09-01

    We propose image-guided depth propagation for two-dimensional (2-D)-to-three-dimensional (3-D) video conversion using superpixel matching and the adaptive autoregressive (AR) model. We adopt key frame-based depth propagation that propagates the depth map in the key frame to nonkey frames. Moreover, we use the adaptive AR model for depth refinement to penalize depth-color inconsistency. First, we perform superpixel matching to estimate motion vectors at the superpixel level instead of block matching based on the fixed block size. Then, we conduct depth compensation based on motion vectors to generate the depth map in the nonkey frame. However, the size of two superpixels is not exactly the same due to the segment-based matching, which causes matching errors in the compensated depth map. Thus, we introduce an adaptive image-guided AR model to minimize matching errors and produce the final depth map by minimizing AR prediction errors. Finally, we employ depth-image-based rendering to generate stereoscopic views from 2-D videos and their depth maps. Experimental results demonstrate that the proposed method successfully performs depth propagation and produces high-quality depth maps for 2-D-to-3-D video conversion.

  4. Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

    Science.gov (United States)

    Zhou, Xiangrong; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

    2017-02-01

    We have proposed an end-to-end learning approach that trained a deep convolutional neural network (CNN) for automatic CT image segmentation, which accomplished a voxel-wised multiple classification to directly map each voxel on 3D CT images to an anatomical label automatically. The novelties of our proposed method were (1) transforming the anatomical structures segmentation on 3D CT images into a majority voting of the results of 2D semantic image segmentation on a number of 2D-slices from different image orientations, and (2) using "convolution" and "deconvolution" networks to achieve the conventional "coarse recognition" and "fine extraction" functions which were integrated into a compact all-in-one deep CNN for CT image segmentation. The advantage comparing to previous works was its capability to accomplish real-time image segmentations on 2D slices of arbitrary CT-scan-range (e.g. body, chest, abdomen) and produced correspondingly-sized output. In this paper, we propose an improvement of our proposed approach by adding an organ localization module to limit CT image range for training and testing deep CNNs. A database consisting of 240 3D CT scans and a human annotated ground truth was used for training (228 cases) and testing (the remaining 12 cases). We applied the improved method to segment pancreas and left kidney regions, respectively. The preliminary results showed that the accuracies of the segmentation results were improved significantly (pancreas was 34% and kidney was 8% increased in Jaccard index from our previous results). The effectiveness and usefulness of proposed improvement for CT image segmentations were confirmed.

  5. Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla.

    Science.gov (United States)

    Fallah, Faezeh; Machann, Jürgen; Martirosian, Petros; Bamberg, Fabian; Schick, Fritz; Yang, Bin

    2017-04-01

    To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE). The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m2 (30.02 ± 6.63 kg/m2) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes. Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images. Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.

  6. Adaptive 3D Virtual Learning Environments--A Review of the Literature

    Science.gov (United States)

    Scott, Ezequiel; Soria, Alvaro; Campo, Marcelo

    2017-01-01

    New ways of learning have emerged in the last years by using computers in education. For instance, many Virtual Learning Environments have been widely adopted by educators, obtaining promising outcomes. Recently, these environments have evolved into more advanced ones using 3D technologies and taking into account the individual learner needs and…

  7. µCT-3D visualization analysis of resin composite polymerization and dye penetration test of composite adaptation.

    Science.gov (United States)

    Yoshikawa, Takako; Sadr, Alireza; Tagami, Junji

    2017-08-25

    This study evaluated the effects of the light curing methods and resin composite composition on composite polymerization contraction behavior and resin composite adaptation to the cavity wall using μCT-3D visualization analysis and dye penetration test. Cylindrical cavities were restored using Clearfil tri-S Bond ND Quick adhesive and filled with Clearfil AP-X or Clearfil Photo Bright composite. The composites were cured using the conventional or the slow-start curing method. The light-cured resin composite, which had increased contrast ratio during polymerization, improved adaptation to the cavity wall using the slow-start curing method. In the μCT-3D visualization method, the slow-start curing method reduced polymerization shrinkage volume of resin composite restoration to half of that produced by the conventional curing method in the cavity with adhesive for both composites. Moreover, μCT-3D visualization method can be used to detect and analyze resin composite polymerization contraction behavior and shrinkage volume as 3D image in the cavity.

  8. Gd-EOB enhanced MRI T1-weighted 3D-GRE with and without elevated flip angle modulation for threshold-based liver volume segmentation.

    Science.gov (United States)

    Grieser, Christian; Denecke, Timm; Rothe, Jan-Holger; Geisel, Dominik; Stelter, Lars; Cannon Walter, Thula; Seehofer, Daniel; Steffen, Ingo G

    2015-12-01

    Despite novel software solutions, liver volume segmentation is still a time-consuming procedure and often requires further manual optimization. With the high signal intensity of the liver parenchyma in Gd-EOB enhanced magnetic resonance imaging (MRI), liver volume segmentation may be improved. To evaluate the practicability of threshold-based segmentation of the liver volume using Gd-EOB-enhanced MRI including a customized three-dimensional (3D) sequence. A total of 20 patients examined with Gd-EOB MRI (hepatobiliary phase T1-weighted (T1W) 3D sequence [VIBE]; flip angle [FA], 10° and 30°) were enrolled in this retrospective study. The datasets were independently processed by two blinded observers (O1 and O2) in two ways: manual (man) and threshold-based (thresh; study method) segmentation of the liver each followed by an optimization step (man+opt and thresh+opt; man+opt [FA10°] served as reference method). Resulting liver volumes and segmentation times were compared. A liver conversion factor was calculated in percent, describing the non-hepatocellular fraction of the total liver volume, i.e. bile ducts and vessels. Thresh+opt (FA10°) was significantly faster compared to the reference method leading to a median volume overestimation of 4%/8% (P segmentation was even faster (P  0.2). The liver conversion factor was found to be 10%. Threshold-based liver segmentation employing Gd-EOB-enhanced hepatobiliary phase standard T1W 3D sequence is accurate and time-saving. The performance of this approach can be further improved by increasing the FA. © The Foundation Acta Radiologica 2014.

  9. Stream-based Active Learning for Efficient and Adaptive Classification of 3D Objects

    OpenAIRE

    Narr, Alexander; Triebel, Rudolph; Cremers, Daniel

    2016-01-01

    We present a new Active Learning approach for classifying objects from streams of 3D point cloud data. The major problems here are the non-uniform occurence of class instances and the unbalanced numbers of samples per class. We show that standard online learning methods based on decision trees perform comparably bad for such data streams, which are however particularly relevant for mobile robots that need to learn semantics persistently. To address this, we use Mondrian forests (MF), a recent...

  10. MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence

    Energy Technology Data Exchange (ETDEWEB)

    Ai, H; Pan, T [The University of Texas MD Anderson Cancer Center, Houston, TX (United States); The University of Texas Graduate School of Biomedical Science, Houston, TX (United States); Hwang, K [GE Healthcare, Houston, TX (United States)

    2014-06-15

    Purpose: To determine the feasibility of identifying cortical bone on MR images with a short-TE 3D fast-GRE sequence for attenuation correction of PET data in PET/MR. Methods: A water-fat-bone phantom was constructed with two pieces of beef shank. MR scans were performed on a 3T MR scanner (GE Discovery™ MR750). A 3D GRE sequence was first employed to measure the level of residual signal in cortical bone (TE{sub 1}/TE{sub 2}/TE{sub 3}=2.2/4.4/6.6ms, TR=20ms, flip angle=25°). For cortical bone segmentation, a 3D fast-GRE sequence (TE/TR=0.7/1.9ms, acquisition voxel size=2.5×2.5×3mm{sup 3}) was implemented along with a 3D Dixon sequence (TE{sub 1}/TE{sub 2}/TR=1.2/2.3/4.0ms, acquisition voxel size=1.25×1.25×3mm{sup 3}) for water/fat imaging. Flip angle (10°), acquisition bandwidth (250kHz), FOV (480×480×144mm{sup 3}) and reconstructed voxel size (0.94×0.94×1.5mm{sup 3}) were kept the same for both sequences. Soft tissue and fat tissue were first segmented on the reconstructed water/fat image. A tissue mask was created by combining the segmented water/fat masks, which was then applied on the fast-GRE image (MRFGRE). A second mask was created to remove the Gibbs artifacts present in regions in close vicinity to the phantom. MRFGRE data was smoothed with a 3D anisotropic diffusion filter for noise reduction, after which cortical bone and air was separated using a threshold determined from the histogram. Results: There is signal in the cortical bone region in the 3D GRE images, indicating the possibility of separating cortical bone and air based on signal intensity from short-TE MR image. The acquisition time for the 3D fast-GRE sequence was 17s, which can be reduced to less than 10s with parallel imaging. The attenuation image created from water-fat-bone segmentation is visually similar compared to reference CT. Conclusion: Cortical bone and air can be separated based on intensity in MR image with a short-TE 3D fast-GRE sequence. Further research is required

  11. PHISICS/RELAP5-3D Adaptive Time-Step Method Demonstrated for the HTTR LOFC#1 Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Baker, Robin Ivey [Idaho National Lab. (INL), Idaho Falls, ID (United States); Balestra, Paolo [Univ. of Rome (Italy); Strydom, Gerhard [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2017-05-01

    A collaborative effort between Japan Atomic Energy Agency (JAEA) and Idaho National Laboratory (INL) as part of the Civil Nuclear Energy Working Group is underway to model the high temperature engineering test reactor (HTTR) loss of forced cooling (LOFC) transient that was performed in December 2010. The coupled version of RELAP5-3D, a thermal fluids code, and PHISICS, a neutronics code, were used to model the transient. The focus of this report is to summarize the changes made to the PHISICS-RELAP5-3D code for implementing an adaptive time step methodology into the code for the first time, and to test it using the full HTTR PHISICS/RELAP5-3D model developed by JAEA and INL and the LOFC simulation. Various adaptive schemes are available based on flux or power convergence criteria that allow significantly larger time steps to be taken by the neutronics module. The report includes a description of the HTTR and the associated PHISICS/RELAP5-3D model test results as well as the University of Rome sub-contractor report documenting the adaptive time step theory and methodology implemented in PHISICS/RELAP5-3D. Two versions of the HTTR model were tested using 8 and 26 energy groups. It was found that most of the new adaptive methods lead to significant improvements in the LOFC simulation time required without significant accuracy penalties in the prediction of the fission power and the fuel temperature. In the best performing 8 group model scenarios, a LOFC simulation of 20 hours could be completed in real-time, or even less than real-time, compared with the previous version of the code that completed the same transient 3-8 times slower than real-time. A few of the user choice combinations between the methodologies available and the tolerance settings did however result in unacceptably high errors or insignificant gains in simulation time. The study is concluded with recommendations on which methods to use for this HTTR model. An important caveat is that these findings

  12. A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin.

    Science.gov (United States)

    Ghanta, Sindhu; Jordan, Michael I; Kose, Kivanc; Brooks, Dana H; Rajadhyaksha, Milind; Dy, Jennifer G

    2017-01-01

    Segmenting objects of interest from 3D data sets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution, and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, the shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance, and unknown locations. The driving application that inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear, and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease, and cancer usually start. Detecting the DEJ is challenging, because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped "peaks and valleys." In addition, RCM imaging resolution, contrast, and intensity vary with depth. Thus, a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson

  13. A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin

    Science.gov (United States)

    Ghanta, Sindhu; Jordan, Michael I.; Kose, Kivanc; Brooks, Dana H.; Rajadhyaksha, Milind; Dy, Jennifer G.

    2016-01-01

    Segmenting objects of interest from 3D datasets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance and unknown locations. The driving application which inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease and cancer usually start. Detecting the DEJ is challenging because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped “peaks and valleys”. In addition, RCM imaging resolution, contrast and intensity vary with depth. Thus a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process

  14. A new 3-D jerk chaotic system with two cubic nonlinearities and its adaptive backstepping control

    OpenAIRE

    Vaidyanathan Sundarapandian

    2017-01-01

    This paper presents a new seven-term 3-D jerk chaotic system with two cubic nonlinearities. The phase portraits of the novel jerk chaotic system are displayed and the qualitative properties of the jerk system are described. The novel jerk chaotic system has a unique equilibrium at the origin, which is a saddle-focus and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1 = 0:2974, L2 = 0 and L3 = −3:8974. Since the sum of the Lyapunov exponents of the jerk cha...

  15. A novel 3-D jerk chaotic system with three quadratic nonlinearities and its adaptive control

    OpenAIRE

    Vaidyanathan Sundarapandian

    2016-01-01

    This paper announces an eight-term novel 3-D jerk chaotic system with three quadratic nonlinearities. The phase portraits of the novel jerk chaotic system are displayed and the qualitative properties of the jerk system are described. The novel jerk chaotic system has two equilibrium points, which are saddle-foci and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1 = 0.20572,L2 = 0 and L3 = −1.20824. Since the sum of the Lyapunov exponents of the jerk chaoti...

  16. 2D/3D video content adaptation decision engine based on content classification and user assessment

    Science.gov (United States)

    Fernandes, Rui; Andrade, M. T.

    2017-07-01

    Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

  17. A novel 3-D jerk chaotic system with three quadratic nonlinearities and its adaptive control

    National Research Council Canada - National Science Library

    Sundarapandian Vaidyanathan

    2016-01-01

    .... The Kaplan-Yorke dimension of the novel jerk chaotic system is derived as D = 2.17026. Next, an adaptive controller is designed via backstepping control method to globally stabilize the novel jerk chaotic system with unknown parameters...

  18. Adaptive numerical solutions of the Euler equations in 3D using finite elements

    Science.gov (United States)

    Peraire, J.; Peiro, J.; Formaggia, L.; Morgan, K.

    1989-01-01

    The development of an adaptive mesh solution for a flow involving shock interaction on a swept cylinder and an initial solution for a flow past a complex fighter configuration is reported. The finite element solution algorithm, the mesh generation, and the adaptivity of the solution are described. Sample results for the flow past an F-18 configuration at Mach 0.9 and alpha of 3 deg and for shock interaction on a swept cylinder at Mach 8.04 are summarized.

  19. Deep structure of the Lofoten-Vesterålen segment of the Mid-Norwegian continental margin and adjacent areas derived from 3-D density modeling

    Science.gov (United States)

    Maystrenko, Y. P.; Olesen, O.; Gernigon, L.; Gradmann, S.

    2017-02-01

    To understand the major structural features of the sedimentary cover and crystalline crust of the Lofoten-Vesterålen margin and the northern part of the Vøring segment of the Mid-Norwegian continental margin, a lithosphere-scale 3-D structural model has been constructed. This model extends from the exposed crystalline rocks of the Fennoscandian Shield in the east to the Cenozoic oceanic domain of the Norwegian-Greenland Sea in the west, covering the Vestfjorden, Ribban, and Røst Basins and the northern parts of the Vøring Basin and Trøndelag Platform. All available published and/or released data have been used to set the initial 3-D model which has been validated by means of 3-D density forward modeling to obtain a gravity-consistent 3-D structural/density model. Results from the 3-D density modeling reveal that relatively thick sedimentary rocks are present in the distal Røst Basin below the lava flows. The presence of a low-density more than 20 km thick granitic body has been modeled within the middle-upper crystalline crust beneath the eastern part of the Vestfjorden Basin and the adjacent mainland. Moreover, the results of the 3-D density modeling indicate the presence of an atypical low-density lithospheric mantle beneath a large part of the Lofoten-Vesterålen margin which is required to fit the regional component of the modeled gravity with the observed one. The pronounced crustal feature within the model area is the Bivrost Lineament that appears to be the deeply seated lithosphere-scale boundary that delineates clearly the Lofoten-Vesterålen segment from the Vøring margin showing contrasting densities and crustal thicknesses.

  20. Deep Segmentation from 2D Forward Modeling and 3D Tomography of the Maranhão-Barreirinhas-Ceará Margin, NW Brazil

    Science.gov (United States)

    Afonso Dias, Nuno; Afilhado, Alexandra; Schnürle, Philippe; Gallais, Flora; Soares, José; Fuck, Reinhardt; Cupertino, José; Viana, Adriano; Moulin, Maryline; Aslanian, Daniel; Matias, Luís; Evain, Mikael; Loureiro, Afonso

    2017-04-01

    The deep crustal structure of the North-East equatorial Brazilian margin, was investigated during the MAGIC (Margins of brAzil, Ghana and Ivory Coast) joint project, conducted in 2012. The main goal set to understand the fundamental processes leading to the thinning and finally breakup of the continental crust, in a context of a Pull-apart system with two strike-slip borders. The offshore Barreirinhas Basin, was probed by a set of 5 intersecting deep seismic wide-angle profiles, with the deployment of short-period OBS's from IFREMER and land stations from the Brazilian pool. The experiment was devoted to obtain the 2D structure along the directions of flow lines, parallel to margin segmentation and margin segmentation, from tomography and forward modeling. The OBS's deployed recorded also lateral shooting along some profiles, allowing a 3D tomography inversion complementing the results of 2D modeling. Due to the large variation of the water column thickness, heterogeneous crustal structure and Moho depth, several approaches were tested to generate initial input models, to set the grid parameterization and inversion parameters. The assessment of the 3D model was performed by standard synthetic tests and comparison with the obtained 2D forward models. The results evidence a NW-SE segmentation of the margin, following the opening direction of this pull-apart basin, and N-S segmentation that marks the passage between Basins II-III. The signature of the segmentation is evident in the tomograms, where the shallowing of the basement from Basin II towards the oceanic domain is well marked by a NW-SE velocity gradient. Both 2D forward modeling and 3D tomographic inversion indicate a N-S segmentation in the proto-oceanic and oceanic domains, at least at the shallow mantle level. In the southern area the mantle is much faster than on the north. In all profiles crossing Basin II, a deep layer with velocities of 7-4-7.6 km/s generates both refracted as well as reflected phases

  1. Accessible bioprinting: adaptation of a low-cost 3D-printer for precise cell placement and stem cell differentiation.

    Science.gov (United States)

    Reid, John A; Mollica, Peter A; Johnson, Garett D; Ogle, Roy C; Bruno, Robert D; Sachs, Patrick C

    2016-06-07

    The precision and repeatability offered by computer-aided design and computer-numerically controlled techniques in biofabrication processes is quickly becoming an industry standard. However, many hurdles still exist before these techniques can be used in research laboratories for cellular and molecular biology applications. Extrusion-based bioprinting systems have been characterized by high development costs, injector clogging, difficulty achieving small cell number deposits, decreased cell viability, and altered cell function post-printing. To circumvent the high-price barrier to entry of conventional bioprinters, we designed and 3D printed components for the adaptation of an inexpensive 'off-the-shelf' commercially available 3D printer. We also demonstrate via goal based computer simulations that the needle geometries of conventional commercially standardized, 'luer-lock' syringe-needle systems cause many of the issues plaguing conventional bioprinters. To address these performance limitations we optimized flow within several microneedle geometries, which revealed a short tapered injector design with minimal cylindrical needle length was ideal to minimize cell strain and accretion. We then experimentally quantified these geometries using pulled glass microcapillary pipettes and our modified, low-cost 3D printer. This systems performance validated our models exhibiting: reduced clogging, single cell print resolution, and maintenance of cell viability without the use of a sacrificial vehicle. Using this system we show the successful printing of human induced pluripotent stem cells (hiPSCs) into Geltrex and note their retention of a pluripotent state 7 d post printing. We also show embryoid body differentiation of hiPSC by injection into differentiation conducive environments, wherein we observed continuous growth, emergence of various evaginations, and post-printing gene expression indicative of the presence of all three germ layers. These data demonstrate an

  2. Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences.

    Science.gov (United States)

    Gloger, Oliver; Bülow, Robin; Tönnies, Klaus; Völzke, Henry

    2017-11-24

    We aimed to develop the first fully automated 3D gallbladder segmentation approach to perform volumetric analysis in volume data of magnetic resonance (MR) cholangiopancreatography (MRCP) sequences. Volumetric gallbladder analysis is performed for non-contrast-enhanced and secretin-enhanced MRCP sequences. Native and secretin-enhanced MRCP volume data were produced with a 1.5-T MR system. Images of coronal maximum intensity projections (MIP) are used to automatically compute 2D characteristic shape features of the gallbladder in the MIP images. A gallbladder shape space is generated to derive 3D gallbladder shape features, which are then combined with 2D gallbladder shape features in a support vector machine approach to detect gallbladder regions in MRCP volume data. A region-based level set approach is used for fine segmentation. Volumetric analysis is performed for both sequences to calculate gallbladder volume differences between both sequences. The approach presented achieves segmentation results with mean Dice coefficients of 0.917 in non-contrast-enhanced sequences and 0.904 in secretin-enhanced sequences. This is the first approach developed to detect and segment gallbladders in MR-based volume data automatically in both sequences. It can be used to perform gallbladder volume determination in epidemiological studies and to detect abnormal gallbladder volumes or shapes. The positive volume differences between both sequences may indicate the quantity of the pancreatobiliary reflux.

  3. The image variations in mastoid segment of facial nerve and sinus tympani in congenital aural atresia by HRCT and 3D VR CT.

    Science.gov (United States)

    Wang, Zhen; Hou, Qian; Wang, Pu; Sun, Zhaoyong; Fan, Yue; Wang, Yun; Xue, Huadan; Jin, Zhengyu; Chen, Xiaowei

    2015-09-01

    To find the variations of middle ear structures including the spatial pattern of mastoid segment of facial nerve and the shapes of the sinus tympani in patients with congenital aural atresia (CAA) by using the high-resolution (HR) CT and 3D volume rendered (VR) CT images. HRCT was performed in 25 patients with congenital aural atresia including six bilateral atresia patients (n=25, 21 males, 4 females, mean age 13.8 years, range 6-19). Along the long axis of the posterior semicircular canal ampulla, the oblique axial multiplanar reconstruction (MPR) was set to view the depiction of the round window and the mastoid segment of facial nerve. Volumetric rending technique was used to demonstrate the morphologic features. HRCT and 3D VR findings in atresia ears were compared with those in 19 normal ears of the unilateral ears of atresia patients. On the basic plane, the horizontal line distances between the mastoid segment of the facial nerve and the round window (h-RF) in atresia ears significantly decreased compared to the control ears (Pvariation with a large area, which only appears in CAA group. There were a significant difference between the area of the boot-shaped group and the other two groups (Pvariations in mastoid segment of facial nerve and sinus tympani in CAA ears. And it may further help surgeons to make the correct decision for hearing rehabilitation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  4. Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data.

    Science.gov (United States)

    Beichel, Reinhard R; Smith, Brian J; Bauer, Christian; Ulrich, Ethan J; Ahmadvand, Payam; Budzevich, Mikalai M; Gillies, Robert J; Goldgof, Dmitry; Grkovski, Milan; Hamarneh, Ghassan; Huang, Qiao; Kinahan, Paul E; Laymon, Charles M; Mountz, James M; Muzi, John P; Muzi, Mark; Nehmeh, Sadek; Oborski, Matthew J; Tan, Yongqiang; Zhao, Binsheng; Sunderland, John J; Buatti, John M

    2017-02-01

    Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making. To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis. On the two test sets (phantom and HNC PET scans), the performance of the seven

  5. WE-EF-210-08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch-Based Anatomical Feature

    Energy Technology Data Exchange (ETDEWEB)

    Yang, X; Rossi, P; Jani, A; Ogunleye, T; Curran, W; Liu, T [Emory Univ, Atlanta, GA (United States)

    2015-06-15

    Purpose: Transrectal ultrasound (TRUS) is the standard imaging modality for the image-guided prostate-cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real-time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal-to-noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge. Methods: The proposed segmentation method consists of two major stages: the training stage and the segmentation stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well-trained KSVM and the output of this trained KSVM is the segmented prostate of this patient. Results: This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful

  6. An efficient content-adaptive motion-compensated 3-D DWT with enhanced spatial and temporal scalability.

    Science.gov (United States)

    Mehrseresht, Nagita; Taubman, David

    2006-06-01

    We propose a novel, content adaptive method for motion-compensated three-dimensional wavelet transformation (MC 3-D DWT) of video. The proposed method overcomes problems of ghosting and nonaligned aliasing artifacts which can arise in regions of motion model failure, when the video is reconstructed at reduced temporal or spatial resolutions. Previous MC 3-D DWT structures either take the form of MC temporal DWT followed by a spatial transform ("t+2D"), or perform the spatial transform first ("2D + t"), limiting the spatial frequencies which can be jointly compensated in the temporal transform, and hence limiting the compression efficiency. When the motion model fails, the "t + 2D" structure causes nonaligned aliasing artifacts in reduced spatial resolution sequences. Essentially, the proposed transform continuously adapts itself between the "t + 2D" and "2D + t" structures, based on information available within the compressed bit stream. Ghosting artifacts may also appear in reduced frame-rate sequences due to temporal low-pass filtering along invalid motion trajectories. To avoid the ghosting artifacts, we continuously select between different low-pass temporal filters, based on the estimated accuracy of the motion model. Experimental results indicate that the proposed adaptive transform preserves high compression efficiency while substantially improving the quality of reduced spatial and temporal resolution sequences.

  7. IFCPT S-Duct Grid-Adapted FUN3D Computations for the Third Propulsion Aerodynamics Works

    Science.gov (United States)

    Davis, Zach S.; Park, M. A.

    2017-01-01

    Contributions of the unstructured Reynolds-averaged Navier-Stokes code, FUN3D, to the 3rd AIAA Propulsion Aerodynamics Workshop are described for the diffusing IFCPT S-Duct. Using workshop-supplied grids, results for the baseline S-Duct, baseline S-Duct with Aerodynamic Interface Plane (AIP) rake hardware, and baseline S-Duct with flow control devices are compared with experimental data and results computed with output-based, off-body grid adaptation in FUN3D. Due to the absence of influential geometry components, total pressure recovery is overpredicted on the baseline S-Duct and S-Duct with flow control vanes when compared to experimental values. An estimate for the exact value of total pressure recovery is derived for these cases given an infinitely refined mesh. When results from output-based mesh adaptation are compared with those computed on workshop-supplied grids, a considerable improvement in predicting total pressure recovery is observed. By including more representative geometry, output-based mesh adaptation compares very favorably with experimental data in terms of predicting the total pressure recovery cost-function; whereas, results computed using the workshop-supplied grids are underpredicted.

  8. Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.

    Directory of Open Access Journals (Sweden)

    Chengwen Chu

    Full Text Available In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1 3D T2-weighted spine MR images from 23 patients and 2 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5, we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

  9. Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.

    Science.gov (United States)

    Chu, Chengwen; Belavý, Daniel L; Armbrecht, Gabriele; Bansmann, Martin; Felsenberg, Dieter; Zheng, Guoyan

    2015-01-01

    In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

  10. 3-D direct current resistivity anisotropic modelling by goal-oriented adaptive finite element methods

    Science.gov (United States)

    Ren, Zhengyong; Qiu, Lewen; Tang, Jingtian; Wu, Xiaoping; Xiao, Xiao; Zhou, Zilong

    2018-01-01

    Although accurate numerical solvers for 3-D direct current (DC) isotropic resistivity models are current available even for complicated models with topography, reliable numerical solvers for the anisotropic case are still an open question. This study aims to develop a novel and optimal numerical solver for accurately calculating the DC potentials for complicated models with arbitrary anisotropic conductivity structures in the Earth. First, a secondary potential boundary value problem is derived by considering the topography and the anisotropic conductivity. Then, two a posteriori error estimators with one using the gradient-recovery technique and one measuring the discontinuity of the normal component of current density are developed for the anisotropic cases. Combing the goal-oriented and non-goal-oriented mesh refinements and these two error estimators, four different solving strategies are developed for complicated DC anisotropic forward modelling problems. A synthetic anisotropic two-layer model with analytic solutions verified the accuracy of our algorithms. A half-space model with a buried anisotropic cube and a mountain-valley model are adopted to test the convergence rates of these four solving strategies. We found that the error estimator based on the discontinuity of current density shows better performance than the gradient-recovery based a posteriori error estimator for anisotropic models with conductivity contrasts. Both error estimators working together with goal-oriented concepts can offer optimal mesh density distributions and highly accurate solutions.

  11. Adaptive backstepping control, synchronization and circuit simulation of a 3-D novel jerk chaotic system with two hyperbolic sinusoidal nonlinearities

    OpenAIRE

    Vaidyanathan Sundarapandian; Volos Christos; Pham Viet-Thanh; Madhavan Kavitha; Idowu Babatunde A.

    2014-01-01

    In this research work, a six-term 3-D novel jerk chaotic system with two hyperbolic sinusoidal nonlinearities has been proposed, and its qualitative properties have been detailed. The Lyapunov exponents of the novel jerk system are obtained as L1 = 0.07765,L2 = 0, and L3 = −0.87912. The Kaplan-Yorke dimension of the novel jerk system is obtained as DKY = 2.08833. Next, an adaptive backstepping controller is designed to stabilize the novel jerk chaotic system with two unknown parameters. Moreo...

  12. Autonomous Image Segmentation using Density-Adaptive Dendritic Cell Algorithm

    OpenAIRE

    Vishwambhar Pathak; Praveen Dhyani; Prabhat Mahanti

    2013-01-01

    Contemporary image processing based applications like medical diagnosis automation and analysis of satellite imagery include autonomous image segmentation as inevitable facility. The research done shows the efficiency of an adaptive evolutionary algorithm based on immune system dynamics for the task of autonomous image segmentation. The recognition dynamics of immune-kernels modeled with infinite Gaussian mixture models exhibit the capability to automatically determine appropriate number of s...

  13. Adaptive geodesic transform for segmentation of vertebrae on CT images

    Science.gov (United States)

    Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

    2014-03-01

    Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

  14. Solving 2D/3D Heat Conduction Problems by Combining Topology Optimization and Anisotropic Mesh Adaptation

    DEFF Research Database (Denmark)

    Jensen, Kristian

    2018-01-01

    function is chosen such that the problem is self-adjoint. There is no way around the book keeping associated with mesh adaptation, so the whole 5527 line MATLAB code is published (https://github.com/kristianE86/trullekrul). The design variables as well as the sensitivities have to be interpolated between...... geometry/mesh setup and 50 for the forward problem. It is thus feasible to use the script as a platform for solving other problems or for investigating the details of the methodology itself....

  15. Adaptive multi-resolution 3D Hartree-Fock-Bogoliubov solver for nuclear structure

    Science.gov (United States)

    Pei, J. C.; Fann, G. I.; Harrison, R. J.; Nazarewicz, W.; Shi, Yue; Thornton, S.

    2014-08-01

    Background: Complex many-body systems, such as triaxial and reflection-asymmetric nuclei, weakly bound halo states, cluster configurations, nuclear fragments produced in heavy-ion fusion reactions, cold Fermi gases, and pasta phases in neutron star crust, are all characterized by large sizes and complex topologies in which many geometrical symmetries characteristic of ground-state configurations are broken. A tool of choice to study such complex forms of matter is an adaptive multi-resolution wavelet analysis. This method has generated much excitement since it provides a common framework linking many diversified methodologies across different fields, including signal processing, data compression, harmonic analysis and operator theory, fractals, and quantum field theory. Purpose: To describe complex superfluid many-fermion systems, we introduce an adaptive pseudospectral method for solving self-consistent equations of nuclear density functional theory in three dimensions, without symmetry restrictions. Methods: The numerical method is based on the multi-resolution and computational harmonic analysis techniques with a multi-wavelet basis. The application of state-of-the-art parallel programming techniques include sophisticated object-oriented templates which parse the high-level code into distributed parallel tasks with a multi-thread task queue scheduler for each multi-core node. The internode communications are asynchronous. The algorithm is variational and is capable of solving coupled complex-geometric systems of equations adaptively, with functional and boundary constraints, in a finite spatial domain of very large size, limited by existing parallel computer memory. For smooth functions, user-defined finite precision is guaranteed. Results: The new adaptive multi-resolution Hartree-Fock-Bogoliubov (HFB) solver madness-hfb is benchmarked against a two-dimensional coordinate-space solver hfb-ax that is based on the B-spline technique and a three-dimensional solver

  16. Graph-based Active Learning of Agglomeration (GALA: a Python library to segment 2D and 3D neuroimages

    Directory of Open Access Journals (Sweden)

    Juan eNunez-Iglesias

    2014-04-01

    Full Text Available The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM. Thus, a common approach is to perform automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration, improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others. We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the limitations of the gala library and how we intend to address them.

  17. Adaptive backstepping control, synchronization and circuit simulation of a 3-D novel jerk chaotic system with two hyperbolic sinusoidal nonlinearities

    Directory of Open Access Journals (Sweden)

    Vaidyanathan Sundarapandian

    2014-09-01

    Full Text Available In this research work, a six-term 3-D novel jerk chaotic system with two hyperbolic sinusoidal nonlinearities has been proposed, and its qualitative properties have been detailed. The Lyapunov exponents of the novel jerk system are obtained as L1 = 0.07765,L2 = 0, and L3 = −0.87912. The Kaplan-Yorke dimension of the novel jerk system is obtained as DKY = 2.08833. Next, an adaptive backstepping controller is designed to stabilize the novel jerk chaotic system with two unknown parameters. Moreover, an adaptive backstepping controller is designed to achieve complete chaos synchronization of the identical novel jerk chaotic systems with two unknown parameters. Finally, an electronic circuit realization of the novel jerk chaotic system using Spice is presented in detail to confirm the feasibility of the theoretical model

  18. Adaptive laser focusing head design for the 3D free surface in parallel kinematic machining

    Science.gov (United States)

    Ye, K. H.; Lee, B. S.; Choi, H. W.

    2017-04-01

    An adaptive laser head was designed for microscale patterning and welding applications. An optical design for laser head was performed by using ray tracing technique with commercial software. Setting the focal length of the lens at 100mm the calculated focus beam diameter was offset to be 10 μm for the CFRP welding applications. To perform an auto-focusing calibration an LRF(Laser Range Finder) distance sensor was used to measure the distance to the target surface. Using a DC Motor with PID control loop, the distance was kept at constant between the laser head and the target material. In this paper, we propose an algorithm for auto-focusing calibration function and detail schematics of laser head design..

  19. Non-destructive 3D imaging of composite restorations using optical coherence tomography: marginal adaptation of self-etch adhesives.

    Science.gov (United States)

    Makishi, Patricia; Shimada, Yasushi; Sadr, Alireza; Tagami, Junji; Sumi, Yasunori

    2011-04-01

    To investigate the potential use of swept-source optical coherence tomography (SS-OCT) as a new tool to evaluate marginal adaptation of composite restorations in class I cavities. Round-shaped class I cavities (3mm diameter × 1.5mm depth) were prepared on buccal enamel of bovine teeth with cavity floor located in dentine. The cavities were restored with a flowable resin composite (Clearfil Majesty LV) using two-step self-etch adhesive (SE Bond), all-in-one self-etch adhesive (G-Bond) or no adhesive (Control). The specimens were subjected to water storage (37 °C, 24 h) or thermal stress challenge (5000 cycles, 5 °C and 55 °C). 3D scans (4 mm×4 mm×2.6 mm obtained in 4 s) of the restoration were obtained using SS-OCT before and after immersion into a contrast agent. 2D images obtained from the 3D scans (n=30/group) were analysed to evaluate marginal adaptation. Area fraction of pixels with high brightness values at the interfacial zone was calculated using a digital image analysis software. The results were statistically compared with statistical significance defined as p≤0.05. Wilcoxon signed ranks test showed that there was no statistically significant difference in the results of SS-OCT before and after infiltration of the contrast agent when a ranking transformation was applied on to the data (p>0.05). A significant positive linear correlation was found between the two SS-OCT images. Confocal laser scanning photomicrographs of samples cut after silver infiltration confirmed the presence of gap. 3D imaging by SS-OCT can be considered as a non-invasive technique for fast detection of gaps at the restoration interface. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study

    Energy Technology Data Exchange (ETDEWEB)

    Way, Ted W; Chan, H-P; Goodsitt, Mitchell M; Sahiner, Berkman; Hadjiiski, Lubomir M; Zhou Chuan; Chughtai, Aamer [Department of Radiology, University of Michigan, Ann Arbor, MI 48109 (United States)], E-mail: tway@umich.edu

    2008-03-07

    The purpose of this study is to investigate the effects of CT scanning and reconstruction parameters on automated segmentation and volumetric measurements of nodules in CT images. Phantom nodules of known sizes were used so that segmentation accuracy could be quantified in comparison to ground-truth volumes. Spherical nodules having 4.8, 9.5 and 16 mm diameters and 50 and 100 mg cc{sup -1} calcium contents were embedded in lung-tissue-simulating foam which was inserted in the thoracic cavity of a chest section phantom. CT scans of the phantom were acquired with a 16-slice scanner at various tube currents, pitches, fields-of-view and slice thicknesses. Scans were also taken using identical techniques either within the same day or five months apart for study of reproducibility. The phantom nodules were segmented with a three-dimensional active contour (3DAC) model that we previously developed for use on patient nodules. The percentage volume errors relative to the ground-truth volumes were estimated under the various imaging conditions. There was no statistically significant difference in volume error for repeated CT scans or scans taken with techniques where only pitch, field of view, or tube current (mA) were changed. However, the slice thickness significantly (p < 0.05) affected the volume error. Therefore, to evaluate nodule growth, consistent imaging conditions and high resolution should be used for acquisition of the serial CT scans, especially for smaller nodules. Understanding the effects of scanning and reconstruction parameters on volume measurements by 3DAC allows better interpretation of data and assessment of growth. Tracking nodule growth with computerized segmentation methods would reduce inter- and intraobserver variabilities.

  1. Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study

    Science.gov (United States)

    Way, Ted W.; Chan, Heang-Ping; Goodsitt, Mitchell M.; Sahiner, Berkman; Hadjiiski, Lubomir M.; Zhou, Chuan; Chughtai, Aamer

    2008-03-01

    The purpose of this study is to investigate the effects of CT scanning and reconstruction parameters on automated segmentation and volumetric measurements of nodules in CT images. Phantom nodules of known sizes were used so that segmentation accuracy could be quantified in comparison to ground-truth volumes. Spherical nodules having 4.8, 9.5 and 16 mm diameters and 50 and 100 mg cc-1 calcium contents were embedded in lung-tissue-simulating foam which was inserted in the thoracic cavity of a chest section phantom. CT scans of the phantom were acquired with a 16-slice scanner at various tube currents, pitches, fields-of-view and slice thicknesses. Scans were also taken using identical techniques either within the same day or five months apart for study of reproducibility. The phantom nodules were segmented with a three-dimensional active contour (3DAC) model that we previously developed for use on patient nodules. The percentage volume errors relative to the ground-truth volumes were estimated under the various imaging conditions. There was no statistically significant difference in volume error for repeated CT scans or scans taken with techniques where only pitch, field of view, or tube current (mA) were changed. However, the slice thickness significantly (p < 0.05) affected the volume error. Therefore, to evaluate nodule growth, consistent imaging conditions and high resolution should be used for acquisition of the serial CT scans, especially for smaller nodules. Understanding the effects of scanning and reconstruction parameters on volume measurements by 3DAC allows better interpretation of data and assessment of growth. Tracking nodule growth with computerized segmentation methods would reduce inter- and intraobserver variabilities.

  2. A convolution-adapted ratio-TAR algorithm for 3D photon beam treatment planning.

    Science.gov (United States)

    Zhu, X R; Low, D A; Harms, W B; Purdy, J A

    1995-08-01

    A convolution-adapted ratio of tissue-air ratios (CARTAR) method of dose calculation has been developed at the Mallinckrodt Institute of Radiology. This photon pencil-beam algorithm has been developed and implemented specifically for three-dimensional treatment planning. In a standard ratio of tissue-air ratios (RTAR) algorithm, doses to points in irregular field geometries are not adequately modeled. This is inconsistent with the advent of conformal therapy, the goal of which is to conform the dose distribution to the target volume while sparing neighboring sensitive normal critical structures. This motivated us to develop an algorithm that can model the beam penumbra near irregular field edges, while retaining much of the speed for the original RTAR algorithm. The dose calculation algorithm uses two-dimensional (2D) convolutions, computed by 2D fast Fourier transform, of pencil-beam kernels with a beam transmission array to calculate 2D off-axis profiles at a series of depths. These profiles are used to replace the product of the transmission function and measured square-field boundary factors used in the standard RTAR calculation. The 2D pencil-beam kernels were derived from measured data for each modality using commonly available dosimetry equipment. The CARTAR algorithm is capable of modeling the penumbra near block edges as well as the loss of primary and scattered beam in partially blocked regions. This paper describes the dose calculation algorithm, implementation, and verification.

  3. Body segment differences in surface area, skin temperature and 3D displacement and the estimation of heat balance during locomotion in hominins.

    Science.gov (United States)

    Cross, Alan; Collard, Mark; Nelson, Andrew

    2008-06-18

    The conventional method of estimating heat balance during locomotion in humans and other hominins treats the body as an undifferentiated mass. This is problematic because the segments of the body differ with respect to several variables that can affect thermoregulation. Here, we report a study that investigated the impact on heat balance during locomotion of inter-segment differences in three of these variables: surface area, skin temperature and rate of movement. The approach adopted in the study was to generate heat balance estimates with the conventional method and then compare them with heat balance estimates generated with a method that takes into account inter-segment differences in surface area, skin temperature and rate of movement. We reasoned that, if the hypothesis that inter-segment differences in surface area, skin temperature and rate of movement affect heat balance during locomotion is correct, the estimates yielded by the two methods should be statistically significantly different. Anthropometric data were collected on seven adult male volunteers. The volunteers then walked on a treadmill at 1.2 m/s while 3D motion capture cameras recorded their movements. Next, the conventional and segmented methods were used to estimate the volunteers' heat balance while walking in four ambient temperatures. Lastly, the estimates produced with the two methods were compared with the paired t-test. The estimates of heat balance during locomotion yielded by the two methods are significantly different. Those yielded by the segmented method are significantly lower than those produced by the conventional method. Accordingly, the study supports the hypothesis that inter-segment differences in surface area, skin temperature and rate of movement impact heat balance during locomotion. This has important implications not only for current understanding of heat balance during locomotion in hominins but also for how future research on this topic should be approached.

  4. 3D multi-slab diffusion-weighted readout-segmented EPI with real-time cardiac-reordered K-space acquisition.

    Science.gov (United States)

    Frost, Robert; Miller, Karla L; Tijssen, Rob H N; Porter, David A; Jezzard, Peter

    2014-12-01

    The aim of this study was to develop, implement, and demonstrate a three-dimensional (3D) extension of the readout-segmented echo-planar imaging (rs-EPI) sequence for diffusion imaging. Potential k-space acquisition schemes were assessed by simulating their associated spatial point spread functions. Motion-induced phase artifacts were also simulated to test navigator corrections and a real-time reordering of the k-space acquisition relative to the cardiac cycle. The cardiac reordering strategy preferentially chooses readout segments closer to the center of 3D k-space during diastole. Motion-induced phase artifacts were quantified by calculating the voxel-wise temporal variation in a set of repeated diffusion-weighted acquisitions. Based on the results of these simulations, a 2D navigated multi-slab rs-EPI sequence with real-time cardiac reordering was implemented. The multi-slab implementation enables signal-to-noise ratio-optimal repetition times of 1-2 s. Cardiac reordering was validated in simulations and in vivo using the multi-slab rs-EPI sequence. In comparisons with standard k-space acquisitions, cardiac reordering was shown to reduce the variability due to motion-induced phase artifacts by 30-50%. High-resolution diffusion tensor imaging data acquired with the cardiac-reordered multi-slab rs-EPI sequence are presented. A 3D multi-slab rs-EPI sequence with cardiac reordering has been demonstrated in vivo and is shown to provide high-quality 3D diffusion-weighted data sets. © 2013 Wiley Periodicals, Inc.

  5. Classification and segmentation of orbital space based objects against terrestrial distractors for the purpose of finding holes in shape from motion 3D reconstruction

    Science.gov (United States)

    Mundhenk, T. Nathan; Flores, Arturo; Hoffman, Heiko

    2013-12-01

    3D reconstruction of objects via Shape from Motion (SFM) has made great strides recently. Utilizing images from a variety of poses, objects can be reconstructed in 3D without knowing a priori the camera pose. These feature points can then be bundled together to create large scale scene reconstructions automatically. A shortcoming of current methods of SFM reconstruction is in dealing with specular or flat low feature surfaces. The inability of SFM to handle these places creates holes in a 3D reconstruction. This can cause problems when the 3D reconstruction is used for proximity detection and collision avoidance by a space vehicle working around another space vehicle. As such, we would like the automatic ability to recognize when a hole in a 3D reconstruction is in fact not a hole, but is a place where reconstruction has failed. Once we know about such a location, methods can be used to try to either more vigorously fill in that region or to instruct a space vehicle to proceed with more caution around that area. Detecting such areas in earth orbiting objects is non-trivial since we need to parse out complex vehicle features from complex earth features, particularly when the observing vehicle is overhead the target vehicle. To do this, we have created a Space Object Classifier and Segmenter (SOCS) hole finder. The general principle we use is to classify image features into three categories (earth, man-made, space). Classified regions are then clustered into probabilistic regions which can then be segmented out. Our categorization method uses an augmentation of a state of the art bag of visual words method for object categorization. This method works by first extracting PHOW (dense SIFT like) features which are computed over an image and then quantized via KD Tree. The quantization results are then binned into histograms and results classified by the PEGASOS support vector machine solver. This gives a probability that a patch in the image corresponds to one of three

  6. HIFI-C: a robust and fast method for determining NMR couplings from adaptive 3D to 2D projections

    Energy Technology Data Exchange (ETDEWEB)

    Cornilescu, Gabriel, E-mail: gabrielc@nmrfam.wisc.edu; Bahrami, Arash; Tonelli, Marco; Markley, John L.; Eghbalnia, Hamid R. [University of Wisconsin-Madison, National Magnetic Resonance Facility at Madison (United States)], E-mail: eghbalni@nmrfam.wisc.edu

    2007-08-15

    We describe a novel method for the robust, rapid, and reliable determination of J couplings in multi-dimensional NMR coupling data, including small couplings from larger proteins. The method, 'High-resolution Iterative Frequency Identification of Couplings' (HIFI-C) is an extension of the adaptive and intelligent data collection approach introduced earlier in HIFI-NMR. HIFI-C collects one or more optimally tilted two-dimensional (2D) planes of a 3D experiment, identifies peaks, and determines couplings with high resolution and precision. The HIFI-C approach, demonstrated here for the 3D quantitative J method, offers vital features that advance the goal of rapid and robust collection of NMR coupling data. (1) Tilted plane residual dipolar couplings (RDC) data are collected adaptively in order to offer an intelligent trade off between data collection time and accuracy. (2) Data from independent planes can provide a statistical measure of reliability for each measured coupling. (3) Fast data collection enables measurements in cases where sample stability is a limiting factor (for example in the presence of an orienting medium required for residual dipolar coupling measurements). (4) For samples that are stable, or in experiments involving relatively stronger couplings, robust data collection enables more reliable determinations of couplings in shorter time, particularly for larger biomolecules. As a proof of principle, we have applied the HIFI-C approach to the 3D quantitative J experiment to determine N-C' RDC values for three proteins ranging from 56 to 159 residues (including a homodimer with 111 residues in each subunit). A number of factors influence the robustness and speed of data collection. These factors include the size of the protein, the experimental set up, and the coupling being measured, among others. To exhibit a lower bound on robustness and the potential for time saving, the measurement of dipolar couplings for the N-C' vector

  7. A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

    Science.gov (United States)

    Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

    2017-04-01

    Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing 3 dimensional (3D) information, and classify the tooth by employing unsupervised learning i.e., k-means++ method. In order to evaluate the proposed method, the experiments are conducted on the sufficient and extensive datasets of mandibular molars. The experimental results show that our method can achieve higher accuracy and robustness compared to other three clustering methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Evaluation of multimodal segmentation based on 3D T1-, T2- and FLAIR-weighted images - the difficulty of choosing.

    Science.gov (United States)

    Lindig, Tobias; Kotikalapudi, Raviteja; Schweikardt, Daniel; Martin, Pascal; Bender, Friedemann; Klose, Uwe; Ernemann, Ulrike; Focke, Niels K; Bender, Benjamin

    2017-02-07

    Voxel-based morphometry is still mainly based on T1-weighted MRI scans. Misclassification of vessels and dura mater as gray matter has been previously reported. Goal of the present work was to evaluate the effect of multimodal segmentation methods available in SPM12, and their influence on identification of age related atrophy and lesion detection in epilepsy patients. 3D T1-, T2- and FLAIR-images of 77 healthy adults (mean age 35.8 years, 19-66 years, 45 females), 7 patients with malformation of cortical development (MCD) (mean age 28.1 years,19-40 years, 3 females), and 5 patients with left hippocampal sclerosis (LHS) (mean age 49.0 years, 25-67 years, 3 females) from a 3T scanner were evaluated. Segmentation based on T1-only, T1+T2, T1+FLAIR, T2+FLAIR, and T1+T2+FLAIR were compared in the healthy subjects. Clinical VBM results based on the different segmentation approaches for MCD and for LHS were compared. T1-only segmentation overestimated total intracranial volume by about 80ml compared to the other segmentation methods. This was due to misclassification of dura mater and vessels as GM and CSF. Significant differences were found for several anatomical regions: the occipital lobe, the basal ganglia/thalamus, the pre- and postcentral gyrus, the cerebellum, and the brainstem. None of the segmentation methods yielded completely satisfying results for the basal ganglia/thalamus and the brainstem. The best correlation with age could be found for the multimodal T1+T2+FLAIR segmentation. Highest T-scores for identification of LHS were found for T1+T2 segmentation, while highest T-scores for MCD were dependent on lesion and anatomical location. Multimodal segmentation is superior to T1-only segmentation and reduces the misclassification of dura mater and vessels as GM and CSF. Depending on the anatomical region and the pathology of interest (atrophy, lesion detection, etc.), different combinations of T1, T2 and FLAIR yield optimal results. Copyright © 2017 Elsevier

  9. 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.

  10. Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

    DEFF Research Database (Denmark)

    Katsarakis, Nikolaos; Pnevmatikakis, Aristodemos; Tan, Zheng-Hua

    2016-01-01

    elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated......Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic...

  11. 3D dento-maxillary osteolytic lesion and active contour segmentation pilot study in CBCT: semi-automatic vs manual methods.

    Science.gov (United States)

    Vallaeys, K; Kacem, A; Legoux, H; Le Tenier, M; Hamitouche, C; Arbab-Chirani, R

    2015-01-01

    This study was designed to evaluate the reliability of a semi-automatic segmentation tool for dento-maxillary osteolytic image analysis compared with manually defined segmentation in CBCT scans. Five CBCT scans were selected from patients for whom periapical radiolucency images were available. All images were obtained using a ProMax® 3D Mid Planmeca (Planmeca Oy, Helsinki, Finland) and were acquired with 200-μm voxel size. Two clinicians performed the manual segmentations. Four operators applied three different semi-automatic procedures. The volumes of the lesions were measured. An analysis of dispersion was made for each procedure and each case. An ANOVA was used to evaluate the operator effect. Non-paired t-tests were used to compare semi-automatic procedures with the manual procedure. Statistical significance was set at α = 0.01. The coefficients of variation for the manual procedure were 2.5-3.5% on average. There was no statistical difference between the two operators. The results of manual procedures can be used as a reference. For the semi-automatic procedures, the dispersion around the mean can be elevated depending on the operator and case. ANOVA revealed significant differences between the operators for the three techniques according to cases. Region-based segmentation was only comparable with the manual procedure for delineating a circumscribed osteolytic dento-maxillary lesion. The semi-automatic segmentations tested are interesting but are limited to complex surface structures. A methodology that combines the strengths of both methods could be of interest and should be tested. The improvement in the image analysis that is possible through the segmentation procedure and CBCT image quality could be of value.

  12. Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

    Science.gov (United States)

    Langmore, Ian; Davis, Anthony B.; Bal, Guillaume; Marzouk, Youssef M.

    2012-01-01

    We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

  13. Automating measurement of subtle changes in articular cartilage from MRI of the knee by combining 3D image registration and segmentation

    Science.gov (United States)

    Lynch, John A.; Zaim, Souhil; Zhao, Jenny; Peterfy, Charles G.; Genant, Harry K.

    2001-07-01

    In osteoarthritis, articular cartilage loses integrity and becomes thinned. This usually occurs at sites which bear weight during normal use. Measurement of such loss from MRI scans, requires precise and reproducible techniques, which can overcome the difficulties of patient repositioning within the scanner. In this study, we combine a previously described technique for segmentation of cartilage from MRI of the knee, with a technique for 3D image registration that matches localized regions of interest at followup and baseline. Two patients, who had recently undergone meniscal surgery, and developed lesions during the 12 month followup period were examined. Image registration matched regions of interest (ROI) between baseline and followup, and changes within the cartilage lesions were estimate to be about a 16% reduction in cartilage volume within each ROI. This was more than 5 times the reproducibility of the measurement, but only represented a change of between 1 and 2% in total femoral cartilage volume. Changes in total cartilage volume may be insensitive for quantifying changes in cartilage morphology. A combined used of automated image segmentation, with 3D image registration could be a useful tool for the precise and sensitive measurement of localized changes in cartilage from MRI of the knee.

  14. Assessment of a Microsoft Kinect-based 3D scanning system for taking body segment girth measurements: a comparison to ISAK and ISO standards.

    Science.gov (United States)

    Clarkson, Sean; Wheat, Jon; Heller, Ben; Choppin, Simon

    2016-01-01

    Use of anthropometric data to infer sporting performance is increasing in popularity, particularly within elite sport programmes. Measurement typically follows standards set by the International Society for the Advancement of Kinanthropometry (ISAK). However, such techniques are time consuming, which reduces their practicality. Schranz et al. recently suggested 3D body scanners could replace current measurement techniques; however, current systems are costly. Recent interest in natural user interaction has led to a range of low-cost depth cameras capable of producing 3D body scans, from which anthropometrics can be calculated. A scanning system comprising 4 depth cameras was used to scan 4 cylinders, representative of the body segments. Girth measurements were calculated from the 3D scans and compared to gold standard measurements. Requirements of a Level 1 ISAK practitioner were met in all 4 cylinders, and ISO standards for scan-derived girth measurements were met in the 2 larger cylinders only. A fixed measurement bias was identified that could be corrected with a simple offset factor. Further work is required to determine comparable performance across a wider range of measurements performed upon living participants. Nevertheless, findings of the study suggest such a system offers many advantages over current techniques, having a range of potential applications.

  15. Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

    Science.gov (United States)

    Zhou, Xiangrong; Kano, Takuya; Koyasu, Hiromi; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Matsuo, Masayuki; Fujita, Hiroshi

    2017-03-01

    This paper describes a novel approach for the automatic assessment of breast density in non-contrast three-dimensional computed tomography (3D CT) images. The proposed approach trains and uses a deep convolutional neural network (CNN) from scratch to classify breast tissue density directly from CT images without segmenting the anatomical structures, which creates a bottleneck in conventional approaches. Our scheme determines breast density in a 3D breast region by decomposing the 3D region into several radial 2D-sections from the nipple, and measuring the distribution of breast tissue densities on each 2D section from different orientations. The whole scheme is designed as a compact network without the need for post-processing and provides high robustness and computational efficiency in clinical settings. We applied this scheme to a dataset of 463 non-contrast CT scans obtained from 30- to 45-year-old-women in Japan. The density of breast tissue in each CT scan was assigned to one of four categories (glandular tissue within the breast 75%) by a radiologist as ground truth. We used 405 CT scans for training a deep CNN and the remaining 58 CT scans for testing the performance. The experimental results demonstrated that the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples. These results demonstrate the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images.

  16. Inhomogeneity of rat vertebrae trabecular architecture by high-field 3D mu-magnetic resonance imaging and variable threshold image segmentation.

    Science.gov (United States)

    Palombarini, Marcella; Gombia, Mirko; Fantazzini, Paola; Giardino, Roberto; Giavaresi, Gianluca; Parrilli, Annapaola; Vittur, Franco; Guillot, Genevieve

    2009-10-01

    To analyze the 3D microarchitecture of rat lumbar vertebrae by micro-magnetic resonance imaging (micro-MRI). micro-MR images (20 x 20 x 20 microm(3) apparent voxel size) were acquired with a three-dimensional spin-echo pulse sequence on four lumbar vertebrae of two rats. Apparent microarchitectural parameters like trabecular bone fraction (BV/TV), specific bone surface (BS/TV), mean intercept length (MIL), and Euler number per unit volume (Euler density, E(V)) were calculated using a novel semiquantitative variable threshold segmentation technique. The threshold value T was obtained as a point of minimum or maximum of the function E(V) = E(V)(T). Quantitative 3D analysis of micro-MRI images revealed a higher connectivity in the peripheral regions (E(V) = -570 +/- 70 mm(-3)) than in the central regions (E(V) = -130 +/- 50 mm(-3)) of the analyzed rat lumbar vertebrae. Smaller intertrabecular cavities and larger bone volume fractions were observed in peripheral regions as compared to central ones (MIL = 0.18 +/- 0.01 mm and 0.26 +/- 0.01 mm; BV/TV = 34 +/- 3% and 29 +/- 3%, respectively). The quantitative 3D study of MIL showed a structural anisotropy of the trabeculae along the longitudinal axis seen on the images. The inhomogeneity of the bone architecture was validated by micro-computed tomography (micro-CT) images at the same spatial resolution. 3D high-field micro-MRI is a suitable technique for the assessment of bone quality in experimental animal models. (c) 2009 Wiley-Liss, Inc.

  17. Size-adaptive hepatocellular carcinoma detection from 3D CT images based on the level set method

    Science.gov (United States)

    Yui, Shuntaro; Miyakoshi, Junichi; Matsuzaki, Kazuki; Irie, Toshiyuki; Kurazume, Ryo

    2012-03-01

    Automatic detection of hepatocellular carcinoma (HCC) from 3D CT images effectively reduces interpretation work. Several detection methods have been proposed. However, there still remains a tough problem of adaptation detection methods to a wide range of tumor sizes, especially to small nodules, since it is difficult to distinguish tumors from other structures, including noise. Although the level set method (LS) is a powerful tool for detecting objects with arbitrary topology, it is still poor at detecting small nodules due to low contrast. To detect small nodules, early phase images are useful since low contrast in the late phase causes miss-detection of some small nodules. Nevertheless, conventional methods using early phase images face two problems: one is failure to extract small nodules due to low contrast even in early phase images, and the other is false-positive (FP) detection of vessels adjacent to tumors. In this paper, a new robust detection method adapted to the wide range of tumor sizes has been proposed that uses only early phase images. To overcome these two problems, our method consists of two techniques. One is regularizing surface evolution used in LS by applying a new HCC filter that can enhance both small nodules and large tumors. The other is regularizing the surface evolution by applying a Hessian-matrix-based filter that can enhance the vessel structures. Experimental results showed that the proposed method improves sensitivity by over 15% and decreases FP by over 20%, demonstrating that the proposed method is useful for detecting HCC accurately.

  18. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  19. MAMMOGRAM IMAGE SEGMENTATION USING AUTO ADAPTIVE FUZZY INDEX MEASURE

    Directory of Open Access Journals (Sweden)

    I. Laurence Aroquiaraj

    2011-08-01

    Full Text Available Breast Cancer involves the uncontrolled growth of abnormal cells that have mutated from normal tissues. A radiologist looks for certain signs and characteristics indicative of cancer when evaluating a mammogram. The main task is to obtain the locations of suspicious regions to assist radiologists in diagnosis. Image segmentation has been approached from a wide variety of perspectives: region-based approach, morphological operation, multi-scale analysis, fuzzy approaches and stochastic approaches have been used for mammogram image segmentation but with some limitations. In spite of the several methods available in the literature, image segmentation still a challenging problem in most of image processing applications. The challenge comes from the fuzziness of image objects and the overlapping of the different regions. In this paper we propose fast auto adaptive image segmentation algorithm for finding the optimal thresholds for segmenting gray scale images. The proposed method is based on fuzzy index which decreases the similarity between pixels increases. The system uses initial estimation of the parameters. The fuzzy subsets derived from the image histogram using weighted fuzzy entropy will shows the similar cost measure as in pixels of the same subset. Experimental results demonstrate the effectiveness of the proposed approach.

  20. 3-D segmentation of the rim and cup in spectral-domain optical coherence tomography volumes of the optic nerve head

    Science.gov (United States)

    Lee, Kyungmoo; Niemeijer, Meindert; Garvin, Mona K.; Kwon, Young H.; Sonka, Milan; Abràmoff, Michael D.

    2009-02-01

    Glaucoma is a group of diseases which can cause vision loss and blindness due to gradual damage to the optic nerve. The ratio of the optic disc cup to the optic disc is an important structural indicator for assessing the presence of glaucoma. The purpose of this study is to develop and evaluate a method which can segment the optic disc cup and neuroretinal rim in spectral-domain OCT scans centered on the optic nerve head. Our method starts by segmenting 3 intraretinal surfaces using a fast multiscale 3-D graph search method. Based on one of the segmented surfaces, the retina of the OCT volume is flattened to have a consistent shape across scans and patients. Selected features derived from OCT voxel intensities and intraretinal surfaces were used to train a k-NN classifier that can determine which A-scans in the OCT volume belong to the background, optic disc cup and neuroretinal rim. Through 3-fold cross validation with a training set of 20 optic nerve head-centered OCT scans (10 right eye scans and 10 left eye scans from 10 glaucoma patients) and a testing set of 10 OCT scans (5 right eye scans and 5 left eye scans from 5 different glaucoma patients), segmentation results of the optic disc cup and rim for all 30 OCT scans were obtained. The average unsigned errors of the optic disc cup and rim were 1.155 +/- 1.391 pixels (0.035 +/- 0.042 mm) and 1.295 +/- 0.816 pixels (0.039 +/- 0.024 mm), respectively.

  1. A fully automatic, threshold-based segmentation method for the estimation of the Metabolic Tumor Volume from PET images: validation on 3D printed anthropomorphic oncological lesions

    Science.gov (United States)

    Gallivanone, F.; Interlenghi, M.; Canervari, C.; Castiglioni, I.

    2016-01-01

    18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) is a standard functional diagnostic technique to in vivo image cancer. Different quantitative paramters can be extracted from PET images and used as in vivo cancer biomarkers. Between PET biomarkers Metabolic Tumor Volume (MTV) has gained an important role in particular considering the development of patient-personalized radiotherapy treatment for non-homogeneous dose delivery. Different imaging processing methods have been developed to define MTV. The different proposed PET segmentation strategies were validated in ideal condition (e.g. in spherical objects with uniform radioactivity concentration), while the majority of cancer lesions doesn't fulfill these requirements. In this context, this work has a twofold objective: 1) to implement and optimize a fully automatic, threshold-based segmentation method for the estimation of MTV, feasible in clinical practice 2) to develop a strategy to obtain anthropomorphic phantoms, including non-spherical and non-uniform objects, miming realistic oncological patient conditions. The developed PET segmentation algorithm combines an automatic threshold-based algorithm for the definition of MTV and a k-means clustering algorithm for the estimation of the background. The method is based on parameters always available in clinical studies and was calibrated using NEMA IQ Phantom. Validation of the method was performed both in ideal (e.g. in spherical objects with uniform radioactivity concentration) and non-ideal (e.g. in non-spherical objects with a non-uniform radioactivity concentration) conditions. The strategy to obtain a phantom with synthetic realistic lesions (e.g. with irregular shape and a non-homogeneous uptake) consisted into the combined use of standard anthropomorphic phantoms commercially and irregular molds generated using 3D printer technology and filled with a radioactive chromatic alginate. The proposed segmentation algorithm was feasible in a

  2. 3D heart reconstruction

    OpenAIRE

    Roxo, Diogo

    2011-01-01

    The purpose of this thesis was to achieve a 3D reconstruction of the four heart chambers using 2D echocardiographic images. A level set algorithm based on the phase symmetry approach and on a new logarithmic based stopping function was used to extract simultaneously the four heart cavities from these images in a fully automatic way. However to proceed to the 3D reconstruction using the segmented images, it was first necessary to satisfy clinical practise requirements. This means that the algo...

  3. 3D membrane segmentation and quantification of intact thick cells using cryo soft X-ray transmission microscopy: A pilot study.

    Science.gov (United States)

    Cárdenes, Rubén; Zhang, Chong; Klementieva, Oxana; Werner, Stephan; Guttmann, Peter; Pratsch, Christoph; Cladera, Josep; Bijnens, Bart H

    2017-01-01

    Structural analysis of biological membranes is important for understanding cell and sub-cellular organelle function as well as their interaction with the surrounding environment. Imaging of whole cells in three dimension at high spatial resolution remains a significant challenge, particularly for thick cells. Cryo-transmission soft X-ray microscopy (cryo-TXM) has recently gained popularity to image, in 3D, intact thick cells (∼10μm) with details of sub-cellular architecture and organization in near-native state. This paper reports a new tool to segment and quantify structural changes of biological membranes in 3D from cryo-TXM images by tracking an initial 2D contour along the third axis of the microscope, through a multi-scale ridge detection followed by an active contours-based model, with a subsequent refinement along the other two axes. A quantitative metric that assesses the grayscale profiles perpendicular to the membrane surfaces is introduced and shown to be linearly related to the membrane thickness. Our methodology has been validated on synthetic phantoms using realistic microscope properties and structure dimensions, as well as on real cryo-TXM data. Results demonstrate the validity of our algorithms for cryo-TXM data analysis.

  4. Comparison of 3D Adaptive Remeshing Strategies for Finite Element Simulations of Electromagnetic Heating of Gold Nanoparticles

    Directory of Open Access Journals (Sweden)

    Fadhil Mezghani

    2015-01-01

    Full Text Available The optical properties of metallic nanoparticles are well known, but the study of their thermal behavior is in its infancy. However the local heating of surrounding medium, induced by illuminated nanostructures, opens the way to new sensors and devices. Consequently the accurate calculation of the electromagnetically induced heating of nanostructures is of interest. The proposed multiphysics problem cannot be directly solved with the classical refinement method of Comsol Multiphysics and a 3D adaptive remeshing process based on an a posteriori error estimator is used. In this paper the efficiency of three remeshing strategies for solving the multiphysics problem is compared. The first strategy uses independent remeshing for each physical quantity to reach a given accuracy. The second strategy only controls the accuracy on temperature. The third strategy uses a linear combination of the two normalized targets (the electric field intensity and the temperature. The analysis of the performance of each strategy is based on the convergence of the remeshing process in terms of number of elements. The efficiency of each strategy is also characterized by the number of computation iterations, the number of elements, the CPU time, and the RAM required to achieve a given target accuracy.

  5. New High-Resolution 3D Imagery of Fault Deformation and Segmentation of the San Onofre and San Mateo Trends in the Inner California Borderlands

    Science.gov (United States)

    Holmes, J. J.; Driscoll, N. W.; Kent, G. M.; Bormann, J. M.; Harding, A. J.

    2015-12-01

    The Inner California Borderlands (ICB) is situated off the coast of southern California and northern Baja. The structural and geomorphic characteristics of the area record a middle Oligocene transition from subduction to microplate capture along the California coast. Marine stratigraphic evidence shows large-scale extension and rotation overprinted by modern strike-slip deformation. Geodetic and geologic observations indicate that approximately 6-8 mm/yr of Pacific-North American relative plate motion is accommodated by offshore strike-slip faulting in the ICB. The farthest inshore fault system, the Newport-Inglewood Rose Canyon (NIRC) fault complex is a dextral strike-slip system that extends primarily offshore approximately 120 km from San Diego to the San Joaquin Hills near Newport Beach, California. Based on trenching and well data, the NIRC fault system Holocene slip rate is 1.5-2.0 mm/yr to the south and 0.5-1.0 mm/yr along its northern extent. An earthquake rupturing the entire length of the system could produce an Mw 7.0 earthquake or larger. West of the main segments of the NIRC fault complex are the San Mateo and San Onofre fault trends along the continental slope. Previous work concluded that these were part of a strike-slip system that eventually merged with the NIRC complex. Others have interpreted these trends as deformation associated with the Oceanside Blind Thrust fault purported to underlie most of the region. In late 2013, we acquired the first high-resolution 3D P-Cable seismic surveys (3.125 m bin resolution) of the San Mateo and San Onofre trends as part of the Southern California Regional Fault Mapping project aboard the R/V New Horizon. Analysis of these volumes provides important new insights and constraints on the fault segmentation and transfer of deformation. Based on the new 3D sparker seismic data, our preferred interpretation for the San Mateo and San Onofre fault trends is they are transpressional features associated with westward

  6. Generalization of Hindi OCR Using Adaptive Segmentation and Font Files

    Science.gov (United States)

    Agrawal, Mudit; Ma, Huanfeng; Doermann, David

    In this chapter, we describe an adaptive Indic OCR system implemented as part of a rapidly retargetable language tool effort and extend work found in [20, 2]. The system includes script identification, character segmentation, training sample creation, and character recognition. For script identification, Hindi words are identified in bilingual or multilingual document images using features of the Devanagari script and support vector machine (SVM). Identified words are then segmented into individual characters, using a font-model-based intelligent character segmentation and recognition system. Using characteristics of structurally similar TrueType fonts, our system automatically builds a model to be used for the segmentation and recognition of the new script, independent of glyph composition. The key is a reliance on known font attributes. In our recognition system three feature extraction methods are used to demonstrate the importance of appropriate features for classification. The methods are tested on both Latin and non-Latin scripts. Results show that the character-level recognition accuracy exceeds 92% for non-Latin and 96% for Latin text on degraded documents. This work is a step toward the recognition of scripts of low-density languages which typically do not warrant the development of commercial OCR, yet often have complete TrueType font descriptions.

  7. Adaptive remodeling of trabecular bone core cultured in 3-D bioreactor providing cyclic loading: an acoustic microscopy study.

    Science.gov (United States)

    Rupin, Fabienne; Bossis, Dorothée; Vico, Laurence; Peyrin, Françoise; Raum, Kay; Laugier, Pascal; Saïed, Amena

    2010-06-01

    Scanning acoustic microscopy (SAM) provides high-resolution mapping of acoustic impedance related to tissue stiffness. This study investigates changes in tissue acoustic impedance resulting from mechanical loading in trabecular bone cores cultured in 3-D bioreactor. Trabecular bone cores were extracted from bovine sternum (n = 15) and ulna metaphysis (n = 15). From each bone, the samples were divided in three groups. The basal control (BC) group was fixed post-extraction, the control (C) and loaded (L) groups were maintained as viable in a controlled culture-loading cell over three weeks. Samples of L group underwent a dynamic compressive strain, whereas C samples were left free from loading. After three weeks, L and C samples were embedded in polymethylmethacrylate and all samples were explored with a 200-MHz SAM. For each specimen, the acoustic impedance distribution was obtained over flat and polished section of bone blocks prepared parallel to the loading axis. Our results showed that in basal controls, the acoustic impedance varied with bone anatomical location and was 15% higher in weight-bearing ulna compared with nonweight-bearing sternum. The comparison between loaded and nonloaded groups showed that sternum-only exhibited significant change in acoustic impedance (L vs. C sternum: +9%). This result suggests that when the applied load is comparable with the stress naturally experienced by a weight-bearing bone (ulna), the tissue material properties (manifested by acoustic impedance) remained unchanged. In conclusion, SAM is a potentially relevant tool for the assessment of subtle changes in intrinsic microelastic properties of bone induced by adaptive remodeling process in response to mechanical loading. Copyright 2010 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

  8. Rate Adaptive Selective Segment Assignment for Reliable Wireless Video Transmission

    Directory of Open Access Journals (Sweden)

    Sajid Nazir

    2012-01-01

    Full Text Available A reliable video communication system is proposed based on data partitioning feature of H.264/AVC, used to create a layered stream, and LT codes for erasure protection. The proposed scheme termed rate adaptive selective segment assignment (RASSA is an adaptive low-complexity solution to varying channel conditions. The comparison of the results of the proposed scheme is also provided for slice-partitioned H.264/AVC data. Simulation results show competitiveness of the proposed scheme compared to optimized unequal and equal error protection solutions. The simulation results also demonstrate that a high visual quality video transmission can be maintained despite the adverse effect of varying channel conditions and the number of decoding failures can be reduced.

  9. Which Fault Segments Ruptured in the 2008 Wenchuan Earthquake and Which Did Not? New Evidence from Near‐Fault 3D Surface Displacements Derived from SAR Image Offsets

    KAUST Repository

    Feng, Guangcai

    2017-03-15

    The 2008 Mw 7.9 Wenchuan earthquake ruptured a complex thrust‐faulting system at the eastern edge of the Tibetan plateau and west of Sichuan basin. Though the earthquake has been extensively studied, several details about the earthquake, such as which fault segments were activated in the earthquake, are still not clear. This is in part due to difficult field access to the fault zone and in part due to limited near‐fault observations in Interferometric Synthetic Aperture Radar (InSAR) observations because of decorrelation. In this study, we address this problem by estimating SAR image offsets that provide near‐fault ground displacement information and exhibit clear displacement discontinuities across activated fault segments. We begin by reanalyzing the coseismic InSAR observations of the earthquake and then mostly eliminate the strong ionospheric signals that were plaguing previous studies by using additional postevent images. We also estimate the SAR image offsets and use their results to retrieve the full 3D coseismic surface displacement field. The coseismic deformation from the InSAR and image‐offset measurements are compared with both Global Positioning System and field observations. The results indicate that our observations provide significantly better information than previous InSAR studies that were affected by ionospheric disturbances. We use the results to present details of the surface‐faulting offsets along the Beichuan fault from the southwest to the northeast and find that there is an obvious right‐lateral strike‐slip component (as well as thrust faulting) along the southern Beichuan fault (in Yingxiu County), which was strongly underestimated in earlier studies. Based on the results, we provide new evidence to show that the Qingchuan fault was not ruptured in the 2008 Wenchuan earthquake, a topic debated in field observation studies, but show instead that surface faulting occurred on a northward extension of the Beichuan fault during

  10. Automatic speech signal segmentation based on the innovation adaptive filter

    Directory of Open Access Journals (Sweden)

    Makowski Ryszard

    2014-06-01

    Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modified version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

  11. Component-based handprint segmentation using adaptive writing style model

    Science.gov (United States)

    Garris, Michael D.

    1997-04-01

    Building upon the utility of connected components, NIST has designed a new character segmentor based on statistically modeling the style of a person's handwriting. Simple spatial features capture the characteristics of a particular writer's style of handprint, enabling the new method to maintain a traditional character-level segmentation philosophy without the integration of recognition or the use of oversegmentation and linguistic postprocessing. Estimates for stroke width and character height are used to compute aspect ratio and standard stroke count features that adapt to the writer's style at the field level. The new method has been developed with a predetermined set of fuzzy rules making the segmentor much less fragile and much more adaptive, and the new method successfully reconstructs fragmented characters as well as splits touching characters. The new segmentor was integrated into the NIST public domain form-based handprint recognition systems and then tested on a set of 490 handwriting sample forms found in NIST special database 19. When compared to a simple component-based segmentor, the new adaptable method improved the overall recognition of handprinted digits by 3.4 percent and field level recognition by 6.9 percent, while effectively reducing deletion errors by 82 percent. The same program code and set of parameters successfully segments sequences of uppercase and lowercase characters without any context-based tuning. While not as dramatic as digits, the recognition of uppercase and lowercase characters improved by 1.7 percent and 1.3 percent respectively. The segmentor maintains a relatively straight-forward and logical process flow avoiding convolutions of encoded exceptions as is common in expert systems. As a result, the new segmentor operates very efficiently, and throughput as high as 362 characters per second can be achieved. Letters and numbers are constructed from a predetermined configuration of a relatively small number of strokes. Results

  12. Non-destructive analysis of flake properties in automotive paints with full-field optical coherence tomography and 3D segmentation.

    Science.gov (United States)

    Zhang, Jinke; Williams, Bryan M; Lawman, Samuel; Atkinson, David; Zhang, Zijian; Shen, Yaochun; Zheng, Yalin

    2017-08-07

    Automotive coating systems are designed to protect vehicle bodies from corrosion and enhance their aesthetic value. The number, size and orientation of small metallic flakes in the base coat of the paint has a significant effect on the appearance of automotive bodies. It is important for quality assurance (QA) to be able to measure the properties of these small flakes, which are approximately 10μm in radius, yet current QA techniques are limited to measuring layer thickness. We design and develop a time-domain (TD) full-field (FF) optical coherence tomography (OCT) system to scan automotive panels volumetrically, non-destructively and without contact. We develop and integrate a segmentation method to automatically distinguish flakes and allow measurement of their properties. We test our integrated system on nine sections of five panels and demonstrate that this integrated approach can characterise small flakes in automotive coating systems in 3D, calculating the number, size and orientation accurately and consistently. This has the potential to significantly impact QA testing in the automotive industry.

  13. Use of Anisotropy, 3D Segmented Atlas, and Computational Analysis to Identify Gray Matter Subcortical Lesions Common to Concussive Injury from Different Sites on the Cortex.

    Directory of Open Access Journals (Sweden)

    Praveen Kulkarni

    Full Text Available Traumatic brain injury (TBI can occur anywhere along the cortical mantel. While the cortical contusions may be random and disparate in their locations, the clinical outcomes are often similar and difficult to explain. Thus a question that arises is, do concussions at different sites on the cortex affect similar subcortical brain regions? To address this question we used a fluid percussion model to concuss the right caudal or rostral cortices in rats. Five days later, diffusion tensor MRI data were acquired for indices of anisotropy (IA for use in a novel method of analysis to detect changes in gray matter microarchitecture. IA values from over 20,000 voxels were registered into a 3D segmented, annotated rat atlas covering 150 brain areas. Comparisons between left and right hemispheres revealed a small population of subcortical sites with altered IA values. Rostral and caudal concussions were of striking similarity in the impacted subcortical locations, particularly the central nucleus of the amygdala, laterodorsal thalamus, and hippocampal complex. Subsequent immunohistochemical analysis of these sites showed significant neuroinflammation. This study presents three significant findings that advance our understanding and evaluation of TBI: 1 the introduction of a new method to identify highly localized disturbances in discrete gray matter, subcortical brain nuclei without postmortem histology, 2 the use of this method to demonstrate that separate injuries to the rostral and caudal cortex produce the same subcortical, disturbances, and 3 the central nucleus of the amygdala, critical in the regulation of emotion, is vulnerable to concussion.

  14. Streak image denoising and segmentation using adaptive Gaussian guided filter.

    Science.gov (United States)

    Jiang, Zhuocheng; Guo, Baoping

    2014-09-10

    In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance.

  15. Large 3D resistivity and induced polarization acquisition using the Fullwaver system: towards an adapted processing methodology

    Science.gov (United States)

    Truffert, Catherine; Leite, Orlando; Gance, Julien; Texier, Benoît; Bernard, Jean

    2017-04-01

    Driven by needs in the mineral exploration market for ever faster and ever easier set-up of large 3D resistivity and induced polarization, autonomous and cableless recorded systems come to the forefront. Opposite to the traditional centralized acquisition, this new system permits a complete random distribution of receivers on the survey area allowing to obtain a real 3D imaging. This work presents the results of a 3 km2 large experiment up to 600m of depth performed with a new type of autonomous distributed receivers: the I&V-Fullwaver. With such system, all usual drawbacks induced by long cable set up over large 3D areas - time consuming, lack of accessibility, heavy weight, electromagnetic induction, etc. - disappear. The V-Fullwavers record the entire time series of voltage on two perpendicular axes, for a good determination of the data quality although I-Fullwaver records injected current simultaneously. For this survey, despite good assessment of each individual signal quality, on each channel of the set of Fullwaver systems, a significant number of negative apparent resistivity and chargeability remains present in the dataset (around 15%). These values are commonly not taken into account in the inversion software although they may be due to complex geological structure of interest (e.g. linked to the presence of sulfides in the earth). Taking into account that such distributed recording system aims to restitute the best 3D resistivity and IP tomography, how can 3D inversion be improved? In this work, we present the dataset, the processing chain and quality control of a large 3D survey. We show that the quality of the data selected is good enough to include it into the inversion processing. We propose a second way of processing based on the modulus of the apparent resistivity that stabilizes the inversion. We then discuss the results of both processing. We conclude that an effort could be made on the inclusion of negative apparent resistivity in the inversion

  16. Adaptation of pharmaceutical excipients to FDM 3D printing for the fabrication of patient-tailored immediate release tablets.

    Science.gov (United States)

    Sadia, Muzna; Sośnicka, Agata; Arafat, Basel; Isreb, Abdullah; Ahmed, Waqar; Kelarakis, Antonios; Alhnan, Mohamed A

    2016-11-20

    This work aims to employ fused deposition modelling 3D printing to fabricate immediate release pharmaceutical tablets with several model drugs. It investigates the addition of non-melting filler to methacrylic matrix to facilitate FDM 3D printing and explore the impact of (i) the nature of filler, (ii) compatibility with the gears of the 3D printer and iii) polymer: filler ratio on the 3D printing process. Amongst the investigated fillers in this work, directly compressible lactose, spray-dried lactose and microcrystalline cellulose showed a level of degradation at 135°C whilst talc and TCP allowed consistent flow of the filament and a successful 3D printing of the tablet. A specially developed universal filament based on pharmaceutically approved methacrylic polymer (Eudragit EPO) and thermally stable filler, TCP (tribasic calcium phosphate) was optimised. Four model drugs with different physicochemical properties were included into ready-to-use mechanically stable tablets with immediate release properties. Following the two thermal processes (hot melt extrusion (HME) and fused deposition modelling (FDM) 3D printing), drug contents were 94.22%, 88.53%, 96.51% and 93.04% for 5-ASA, captopril, theophylline and prednisolone respectively. XRPD indicated that a fraction of 5-ASA, theophylline and prednisolone remained crystalline whilst captopril was in amorphous form. By combining the advantages of thermally stable pharmaceutically approved polymers and fillers, this unique approach provides a low cost production method for on demand manufacturing of individualised dosage forms. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Metal artefact reduction for patients with metallic dental fillings in helical neck computed tomography: comparison of adaptive iterative dose reduction 3D (AIDR 3D), forward-projected model-based iterative reconstruction solution (FIRST) and AIDR 3D with single-energy metal artefact reduction (SEMAR).

    Science.gov (United States)

    Yasaka, Koichiro; Kamiya, Kouhei; Irie, Ryusuke; Maeda, Eriko; Sato, Jiro; Ohtomo, Kuni

    To compare the differences in metal artefact degree and the depiction of structures in helical neck CT, in patients with metallic dental fillings, among adaptive iterative dose reduction three dimensional (AIDR 3D), forward-projected model-based iterative reconstruction solution (FIRST) and AIDR 3D with single-energy metal artefact reduction (SEMAR-A). In this retrospective clinical study, 22 patients (males, 13; females, 9; mean age, 64.6 ± 12.6 years) with metallic dental fillings who underwent contrast-enhanced helical CT involving the oropharyngeal region were included. Neck axial images were reconstructed with AIDR 3D, FIRST and SEMAR-A. Metal artefact degree and depiction of structures (the apex and root of the tongue, parapharyngeal space, superior portion of the internal jugular chain and parotid gland) were evaluated on a four-point scale by two radiologists. Placing regions of interest, standard deviations of the oral cavity and nuchal muscle (at the slice where no metal exists) were measured and metal artefact indices were calculated (the square root of the difference of the squares of them). In SEMAR-A, metal artefact was significantly reduced and depictions of all structures were significantly improved compared with those in FIRST and AIDR 3D (p ≤ 0.001, sign test). Metal artefact index for the oral cavity in AIDR 3D/FIRST/SEMAR-A was 572.0/477.7/88.4, and significant differences were seen between each reconstruction algorithm (p metal artefact and better depiction of structures than AIDR 3D and FIRST.

  18. Adaptation of the three-dimensional wisdom scale (3D-WS) for the Korean cultural context.

    Science.gov (United States)

    Kim, Seungyoun; Knight, Bob G

    2014-10-23

    ABSTRACT Background: Previous research on wisdom has suggested that wisdom is comprised of cognitive, reflective, and affective components and has developed and validated wisdom measures based on samples from Western countries. To apply the measurement to Eastern cultures, the present study revised an existing wisdom scale, the three-dimensional wisdom scale (3D-WS, Ardelt, 2003) for the Korean cultural context. Methods: Participants included 189 Korean heritage adults (age range 19-96) living in Los Angeles. We added a culturally specific factor of wisdom to the 3D-WS: Modesty and Unobtrusiveness (Yang, 2001), which captures an Eastern aspect of wisdom. The structure and psychometrics of the scale were tested. By latent cluster analysis, we determined acculturation subgroups and examined group differences in the means of factors in the revised wisdom scale (3D-WS-K). Results: Three factors, Cognitive Flexibility, Viewpoint Relativism, and Empathic Modesty were found using confirmatory factor analysis. Respondents with high biculturalism were higher on Viewpoint Relativism and lower on Empathic Modesty. Conclusion: This study discovered that a revised wisdom scale had a distinct factor structure and item content in a Korean heritage sample. We also found acculturation influences on the meaning of wisdom.

  19. Optimized adaptation algorithm for HEVC/H.265 dynamic adaptive streaming over HTTP using variable segment duration

    Science.gov (United States)

    Irondi, Iheanyi; Wang, Qi; Grecos, Christos

    2016-04-01

    Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation algorithm is then proposed to accurately estimate the bandwidth and precisely determine the time to download the next

  20. Adaptive Image Restoration and Segmentation Method Using Different Neighborhood Sizes

    Directory of Open Access Journals (Sweden)

    Chengcheng Li

    2003-04-01

    Full Text Available The image restoration methods based on the Bayesian's framework and Markov random fields (MRF have been widely used in the image-processing field. The basic idea of all these methods is to use calculus of variation and mathematical statistics to average or estimate a pixel value by the values of its neighbors. After applying this averaging process to the whole image a number of times, the noisy pixels, which are abnormal values, are filtered out. Based on the Tea-trade model, which states that the closer the neighbor, more contribution it makes, almost all of these methods use only the nearest four neighbors for calculation. In our previous research [1, 2], we extended the research on CLRS (image restoration and segmentation by using competitive learning algorithm to enlarge the neighborhood size. The results showed that the longer neighborhood range could improve or worsen the restoration results. We also found that the autocorrelation coefficient was an important factor to determine the proper neighborhood size. We then further realized that the computational complexity increased dramatically along with the enlargement of the neighborhood size. This paper is to further the previous research and to discuss the tradeoff between the computational complexity and the restoration improvement by using longer neighborhood range. We used a couple of methods to construct the synthetic images with the exact correlation coefficients we want and to determine the corresponding neighborhood size. We constructed an image with a range of correlation coefficients by blending some synthetic images. Then an adaptive method to find the correlation coefficients of this image was constructed. We restored the image by applying different neighborhood CLRS algorithm to different parts of the image according to its correlation coefficient. Finally, we applied this adaptive method to some real-world images to get improved restoration results than by using single

  1. An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding

    OpenAIRE

    Wang, Fujun; Li, Junlan; Liu, Shiwei; Zhao, Xingyu; Zhang, Dawei; Tian, Yanling

    2014-01-01

    In order to improve the precision and efficiency of microelectronic bonding, this paper presents an improved adaptive genetic algorithm (IAGA) for the image segmentation and vision alignment of the solder joints in the microelectronic chips. The maximum between-cluster variance (OTSU) threshold segmentation method was adopted for the image segmentation of microchips, and the IAGA was introduced to the threshold segmentation considering the features of the images. The performance of the image ...

  2. 3D Image Modelling and Specific Treatments in Orthodontics Domain

    Directory of Open Access Journals (Sweden)

    Dionysis Goularas

    2007-01-01

    Full Text Available In this article, we present a 3D specific dental plaster treatment system for orthodontics. From computer tomography scanner images, we propose first a 3D image modelling and reconstruction method of the Mandible and Maxillary based on an adaptive triangulation allowing management of contours meant for the complex topologies. Secondly, we present two specific treatment methods directly achieved on obtained 3D model allowing the automatic correction for the setting in occlusion of the Mandible and the Maxillary, and the teeth segmentation allowing more specific dental examinations. Finally, these specific treatments are presented via a client/server application with the aim of allowing a telediagnosis and treatment.

  3. 3D-printed adaptive acoustic lens as a disruptive technology for transcranial ultrasound therapy using single-element transducers.

    Science.gov (United States)

    Maimbourg, Guillaume; Houdouin, Alexandre; Deffieux, Thomas; Tanter, Mickael; Aubry, Jean-François

    2018-01-16

    The development of multi-element arrays for better control of the shape of ultrasonic beams has opened the way for focusing through highly aberrating media, such as the human skull. As a result, the use of brain therapy with transcranial-focused ultrasound has rapidly grown. Although effective, such technology is expensive. We propose a disruptive, low-cost approach that consists of focusing a 1 MHz ultrasound beam through a human skull with a single-element transducer coupled with a tailored silicone acoustic lens cast in a 3D-printed mold and designed using computed tomography-based numerical acoustic simulation. We demonstrate on N  =  3 human skulls that adding lens-based aberration correction to a single-element transducer increases the deposited energy on the target 10 fold.

  4. 3D-printed adaptive acoustic lens as a disruptive technology for transcranial ultrasound therapy using single-element transducers

    Science.gov (United States)

    Maimbourg, Guillaume; Houdouin, Alexandre; Deffieux, Thomas; Tanter, Mickael; Aubry, Jean-François

    2018-01-01

    The development of multi-element arrays for better control of the shape of ultrasonic beams has opened the way for focusing through highly aberrating media, such as the human skull. As a result, the use of brain therapy with transcranial-focused ultrasound has rapidly grown. Although effective, such technology is expensive. We propose a disruptive, low-cost approach that consists of focusing a 1 MHz ultrasound beam through a human skull with a single-element transducer coupled with a tailored silicone acoustic lens cast in a 3D-printed mold and designed using computed tomography-based numerical acoustic simulation. We demonstrate on N  =  3 human skulls that adding lens-based aberration correction to a single-element transducer increases the deposited energy on the target 10 fold.

  5. 3D adaptive finite element method for a phase field model for the moving contact line problems

    KAUST Repository

    Shi, Yi

    2013-08-01

    In this paper, we propose an adaptive finite element method for simulating the moving contact line problems in three dimensions. The model that we used is the coupled Cahn-Hilliard Navier-Stokes equations with the generalized Navier boundary condition(GNBC) proposed in [18]. In our algorithm, to improve the efficiency of the simulation, we use the residual type adaptive finite element algorithm. It is well known that the phase variable decays much faster away from the interface than the velocity variables. There- fore we use an adaptive strategy that will take into account of such difference. Numerical experiments show that our algorithm is both efficient and reliable. © 2013 American Institute of Mathematical Sciences.

  6. Optimal Parameters of Adaptive Segmentation for Epileptic Graphoelements Recognition

    Directory of Open Access Journals (Sweden)

    D. Kala

    2017-04-01

    Full Text Available Manual review of EEG records, as it is per¬formed in common medical practice, is very time-consuming. There is an effort to make this analysis easier and faster for neurologists by using systems for automatic EEG graphoelements recognition. Such a system is composed of three steps: (1 segmentation, which is a subject of this article, (2 features extraction and (3 classification. Precision of classification, and thereby the whole recognition, is strongly affected by the quality of preceding segmentation procedure, which depends on the method of segmentation and its parameters. In this paper, Varri’s method for segmentation of real epileptic EEG signals is used. Effect of input parameters on segmentation outcome is discussed and parameters values are proposed to achieve optimal outcome suitable for the following classification and graphoelements recognition. Only the results of segmentation are presented in this paper.

  7. Pattern matching and adaptive image segmentation applied to plant reproduction by tissue culture

    Science.gov (United States)

    Vazquez Rueda, Martin G.; Hahn, Federico

    1999-03-01

    This paper shows the results obtained in a system vision applied to plant reproduction by tissue culture using adaptive image segmentation and pattern matching algorithms, this analysis improves the number of tissue obtained and minimize errors, the image features of tissue are considered join to statistical analysis to determine the best match and results. Tests make on potato plants are used to present comparative results with original images processed with adaptive segmentation algorithm and non adaptive algorithms and pattern matching.

  8. Cross-axis adaptation improves 3D vestibulo-ocular reflex alignment during chronic stimulation via a head-mounted multichannel vestibular prosthesis

    Science.gov (United States)

    Dai, Chenkai; Fridman, Gene Y.; Chiang, Bryce; Davidovics, Natan; Melvin, Thuy-Anh; Cullen, Kathleen E.; Della Santina, Charles C.

    2012-01-01

    By sensing three-dimensional (3D) head rotation and electrically stimulating the three ampullary branches of a vestibular nerve to encode head angular velocity, a multichannel vestibular prosthesis (MVP) can restore vestibular sensation to individuals disabled by loss of vestibular hair cell function. However, current spread to afferent fibers innervating non-targeted canals and otolith endorgans can distort the vestibular nerve activation pattern, causing misalignment between the perceived and actual axis of head rotation. We hypothesized that over time, central neural mechanisms can adapt to correct this misalignment. To test this, we rendered five chinchillas vestibular-deficient via bilateral gentamicin treatment and unilaterally implanted them with a head mounted MVP. Comparison of 3D angular vestibulo-ocular reflex (aVOR) responses during 2 Hz, 50°/s peak horizontal sinusoidal head rotations in darkness on the first, third and seventh days of continual MVP use revealed that eye responses about the intended axis remained stable (at about 70% of the normal gain) while misalignment improved significantly by the end of one week of prosthetic stimulation. A comparable time course of improvement was also observed for head rotations about the other two semicircular canal axes and at every stimulus frequency examined (0.2–5 Hz). In addition, the extent of disconjugacy between the two eyes progressively improved during the same time window. These results indicate that the central nervous system rapidly adapts to multichannel prosthetic vestibular stimulation to markedly improve 3D aVOR alignment within the first week after activation. Similar adaptive improvements are likely to occur in other species, including humans. PMID:21374081

  9. Goal-Oriented Self-Adaptive hp Finite Element Simulation of 3D DC Borehole Resistivity Simulations

    KAUST Repository

    Calo, Victor M.

    2011-05-14

    In this paper we present a goal-oriented self-adaptive hp Finite Element Method (hp-FEM) with shared data structures and a parallel multi-frontal direct solver. The algorithm automatically generates (without any user interaction) a sequence of meshes delivering exponential convergence of a prescribed quantity of interest with respect to the number of degrees of freedom. The sequence of meshes is generated from a given initial mesh, by performing h (breaking elements into smaller elements), p (adjusting polynomial orders of approximation) or hp (both) refinements on the finite elements. The new parallel implementation utilizes a computational mesh shared between multiple processors. All computational algorithms, including automatic hp goal-oriented adaptivity and the solver work fully in parallel. We describe the parallel self-adaptive hp-FEM algorithm with shared computational domain, as well as its efficiency measurements. We apply the methodology described to the three-dimensional simulation of the borehole resistivity measurement of direct current through casing in the presence of invasion.

  10. An $h$-Adaptive Operator Splitting Method for Two-Phase Flow in 3D Heterogeneous Porous Media

    KAUST Repository

    Chueh, Chih-Che

    2013-01-01

    The simulation of multiphase flow in porous media is a ubiquitous problem in a wide variety of fields, such as fuel cell modeling, oil reservoir simulation, magma dynamics, and tumor modeling. However, it is computationally expensive. This paper presents an interconnected set of algorithms which we show can accelerate computations by more than two orders of magnitude compared to traditional techniques, yet retains the high accuracy necessary for practical applications. Specifically, we base our approach on a new adaptive operator splitting technique driven by an a posteriori criterion to separate the flow from the transport equations, adaptive meshing to reduce the size of the discretized problem, efficient block preconditioned solver techniques for fast solution of the discrete equations, and a recently developed artificial diffusion strategy to stabilize the numerical solution of the transport equation. We demonstrate the accuracy and efficiency of our approach using numerical experiments in one, two, and three dimensions using a program that is made available as part of a large open source library. © 2013 Society for Industrial and Applied Mathematics.

  11. Improved respiratory efficiency of 3D late gadolinium enhancement imaging using the continuously adaptive windowing strategy (CLAWS).

    Science.gov (United States)

    Keegan, Jennifer; Jhooti, Permi; Babu-Narayan, Sonya V; Drivas, Peter; Ernst, Sabine; Firmin, David N

    2014-03-01

    Acquisition durations of navigator-gated high-resolution three-dimensional late gadolinium enhancement studies may typically be up to 10 min, depending on the respiratory efficiency and heart rate. Implementation of the continuously adaptive windowing strategy (CLAWS) could increase respiratory efficiency, but the resulting non-smooth k-space acquisition order during gadolinium wash-out could result in increased artifact. Navigator-gated three-dimensional late gadolinium enhancement acquisitions were performed in 18 patients using tracking end-expiratory accept/reject (EE-ARA) and CLAWS algorithms in random order. Retrospective analysis of the stored navigator data shows that CLAWS scan times are very close to (within 1%) or equal to the fastest achievable scan times while EE-ARA significantly extends the acquisition duration (P Image quality scores for CLAWS and EE-ARA acquisitions are not significantly different (4.1 ± 0.6 compared to 4.3 ± 0.6, P = ns). Numerical phantom simulations show that the non-uniform k-space ordering introduced by CLAWS results in slight, but not statistically significant, reductions in both blood signal-to-noise ratio (10%) and blood-myocardium contrast-to-noise ratio (12%). CLAWS results in markedly reduced acquisition durations compared to EE-ARA without significant detriment to the image quality. Copyright © 2013 Wiley Periodicals, Inc.

  12. Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

    Science.gov (United States)

    Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

    2017-10-01

    Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

  13. An automated image-based method of 3D subject-specific body segment parameter estimation for kinetic analyses of rapid movements.

    Science.gov (United States)

    Sheets, Alison L; Corazza, Stefano; Andriacchi, Thomas P

    2010-01-01

    Accurate subject-specific body segment parameters (BSPs) are necessary to perform kinetic analyses of human movements with large accelerations, or no external contact forces or moments. A new automated topographical image-based method of estimating segment mass, center of mass (CM) position, and moments of inertia is presented. Body geometry and volume were measured using a laser scanner, then an automated pose and shape registration algorithm segmented the scanned body surface, and identified joint center (JC) positions. Assuming the constant segment densities of Dempster, thigh and shank masses, CM locations, and moments of inertia were estimated for four male subjects with body mass indexes (BMIs) of 19.7-38.2. The subject-specific BSP were compared with those determined using Dempster and Clauser regression equations. The influence of BSP and BMI differences on knee and hip net forces and moments during a running swing phase were quantified for the subjects with the smallest and largest BMIs. Subject-specific BSP for 15 body segments were quickly calculated using the image-based method, and total subject masses were overestimated by 1.7-2.9%.When compared with the Dempster and Clauser methods, image-based and regression estimated thigh BSP varied more than the shank parameters. Thigh masses and hip JC to thigh CM distances were consistently larger, and each transverse moment of inertia was smaller using the image-based method. Because the shank had larger linear and angular accelerations than the thigh during the running swing phase, shank BSP differences had a larger effect on calculated intersegmental forces and moments at the knee joint than thigh BSP differences did at the hip. It was the net knee kinetic differences caused by the shank BSP differences that were the largest contributors to the hip variations. Finally, BSP differences produced larger kinetic differences for the subject with larger segment masses, suggesting that parameter accuracy is more

  14. Interior insulation—Characterisation of the historic, solid masonry building segment and analysis of the heat saving potential by 1d, 2d, and 3d simulation

    DEFF Research Database (Denmark)

    Odgaard, Tommy; Bjarløv, Søren Peter; Rode, Carsten

    2017-01-01

    When considering interior insulation of historic, multi-story buildings with solid masonry walls, it is important to focus on two important factors: How big is the building segment to which it can be applied, and what is the significance of how the multi-dimensional geometry of these façade walls...... is considered in the assessment of the heat saving potential. The findings show that a large proportion of Danish multi-storey dwellings with solid masonry walls, high energy consumption, and uniform characteristics were found to originate from the period 1851–1930. This segment accounts for 25% of all multi......-storey apartments in Denmark. It was investigated, which relative reduction of the average thermal transmittance could be obtained by interior insulation when simulated in different dimensions, degrees of insulation and thickness. The analysis showed that partial insulation of the spandrels below windows on the 2nd...

  15. Interactive constraints for 3D-simplex meshes

    Science.gov (United States)

    Boettger, Thomas; Kunert, Tobias; Meinzer, Hans-Peter; Wolf, Ivo

    2005-04-01

    Medical image segmentation is still a very time consuming task and therefore not often integrated into clinical routine. Various 3D segmentation approaches promise to facilitate the work. But they are rarely used in clinical setups due to complex intialization and parametrization of such models. Clinical users need interactive tools, intuitive and easy to handle. They do not want to play around with a set of parameters which will differ from dataset to dataset and often have a non-intuitive meaning. In this work new interactive constraints for deformable three-dimensional 2-simplex meshes are presented. The user can define attracting points in the original image data. These attractors are considered during model deformation and the new forces guarantee that the surface model will pass through these interactively set points. By using the constraints the model parameterization is simplified. Segmentation is started with a spherical surface model which is placed inside the structure of interest and then adapts to the boundaries. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process. The model deformation algorithm was implemented and integrated in ITK (Insight Segmentation and Registration Toolkit). The newly developed segmentation tool was tested on cardiac image data and MRI lung images, but is suitable for any kind of 3D and 3D+t medical image data. It has been shown that the model is less sensitive to preprocessing of the input data as well as model initialization.

  16. Texture segmentation using adaptive Gabor filters based on HVS

    Science.gov (United States)

    Bi, Sheng; Liang, Dequn

    2006-02-01

    A texture segmentation algorithm based on HVS (Human Visual System) is proposed in this paper. Psychophysical and Neurophysiological conclusions have supported the hypothesis that the processing of afferent pictorial information in the HVS (the visual cortex in particular) involves two stages: the preattentive stage, and the focused attention stage. To simulate the preattentive stage of HVS, ring and wedge filtering methods are used to segment coarsely and the texture number in the input image is gotten. As texture is the repeating patterns of local variations in image intensity, we can use a part of the texture as the whole region representation. The inscribed squares in the coarse regions are transformed respectively to frequency domain and each spectrum is analyzed in detail. New texture measurements based on the Fourier spectrums are given. Through analyzing the measurements of the texture, including repeatability directionality and regularity, we can extract the feature, and determine the parameters of the Gabor filter-bank. Then to simulate the focused attention stage of HVS, the determined Gabor filter-bank is used to filter the original input image to produce fine segmentation regions. This approach performs better in computational complexity and feature extraction than the fixed parameters and fixed stages Gabor filter-bank approaches.

  17. Emphysema quantification on low-dose CT using percentage of low-attenuation volume and size distribution of low-attenuation lung regions: Effects of adaptive iterative dose reduction using 3D processing

    Energy Technology Data Exchange (ETDEWEB)

    Nishio, Mizuho, E-mail: nmizuho@med.kobe-u.ac.jp [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Matsumoto, Sumiaki, E-mail: sumatsu@med.kobe-u.ac.jp [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Seki, Shinichiro, E-mail: sshin@med.kobe-u.ac.jp [Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Koyama, Hisanobu, E-mail: hkoyama@med.kobe-u.ac.jp [Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Ohno, Yoshiharu, E-mail: yosirad@kobe-u.ac.jp [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017 (Japan); Fujisawa, Yasuko, E-mail: yasuko1.fujisawa@toshiba.co.jp [Toshiba Medical Systems Corporation, 1385 Shimoishigami, Otawara, Tochigi 324-8550 (Japan); Sugihara, Naoki, E-mail: naoki.sugihara@toshiba.co.jp [Toshiba Medical Systems Corporation, 1385 Shimoishigami, Otawara, Tochigi 324-8550 (Japan); and others

    2014-12-15

    Highlights: • Emphysema quantification (LAV% and D) was affected by image noise on low-dose CT. • For LAV% and D, AIDR 3D improved agreement of quantification on low-dose CT. • AIDR 3D has the potential to quantify emphysema accurately on low-dose CT. - Abstract: Purpose: To evaluate the effects of adaptive iterative dose reduction using 3D processing (AIDR 3D) for quantification of two measures of emphysema: percentage of low-attenuation volume (LAV%) and size distribution of low-attenuation lung regions. Method and materials: : Fifty-two patients who underwent standard-dose (SDCT) and low-dose CT (LDCT) were included. SDCT without AIDR 3D, LDCT without AIDR 3D, and LDCT with AIDR 3D were used for emphysema quantification. First, LAV% was computed at 10 thresholds from −990 to −900 HU. Next, at the same thresholds, linear regression on a log–log plot was used to compute the power law exponent (D) for the cumulative frequency-size distribution of low-attenuation lung regions. Bland–Altman analysis was used to assess whether AIDR 3D improved agreement between LDCT and SDCT for emphysema quantification of LAV% and D. Results: The mean relative differences in LAV% between LDCT without AIDR 3D and SDCT were 3.73%–88.18% and between LDCT with AIDR 3D and SDCT were −6.61% to 0.406%. The mean relative differences in D between LDCT without AIDR 3D and SDCT were 8.22%–19.11% and between LDCT with AIDR 3D and SDCT were 1.82%–4.79%. AIDR 3D improved agreement between LDCT and SDCT at thresholds from −930 to −990 HU for LAV% and at all thresholds for D. Conclusion: AIDR 3D improved the consistency between LDCT and SDCT for emphysema quantification of LAV% and D.

  18. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

    Directory of Open Access Journals (Sweden)

    Ayman Habib

    2016-01-01

    Full Text Available 3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.

  19. Analysis, Adaptive Control and Synchronization of a Seven-Term Novel 3-D Chaotic System with Three Quadratic Nonlinearities and its Digital Implementation in LabVIEW

    Directory of Open Access Journals (Sweden)

    S. Vaidyanathan

    2014-10-01

    Full Text Available This research work proposes a seven-term novel 3-D chaotic system with three quadratic nonlinearities and analyses the fundamental properties of the system such as dissipativity, symmetry, equilibria, Lyapunov exponents and Kaplan-Yorke dimension. The phase portraits of the novel chaotic system simulated using MATLAB depict the strange chaotic attractor of the novel system. For the parameter values and initial conditions chosen in this work, the Lyapunov exponents of the novel chaotic system are obtained as �! = 2.71916, �! = 0 and �! = −13.72776. Also, the KaplanYorke dimension of the novel chaotic system is obtained as �!" = 2.19808. Next, an adaptive controller is designed to stabilize the novel chaotic system with unknown system parameters. Also, an adaptive controller is designed to achieve global chaos synchronization of two identical novel chaotic systems with unknown system parameters. Finally, an electronic circuit realization of the novel chaotic system is depicted using LabVIEW to confirm the feasibility of the theoretical chaotic model.

  20. Adaptive-optics SLO imaging combined with widefield OCT and SLO enables precise 3D localization of fluorescent cells in the mouse retina.

    Science.gov (United States)

    Zawadzki, Robert J; Zhang, Pengfei; Zam, Azhar; Miller, Eric B; Goswami, Mayank; Wang, Xinlei; Jonnal, Ravi S; Lee, Sang-Hyuck; Kim, Dae Yu; Flannery, John G; Werner, John S; Burns, Marie E; Pugh, Edward N

    2015-06-01

    Adaptive optics scanning laser ophthalmoscopy (AO-SLO) has recently been used to achieve exquisite subcellular resolution imaging of the mouse retina. Wavefront sensing-based AO typically restricts the field of view to a few degrees of visual angle. As a consequence the relationship between AO-SLO data and larger scale retinal structures and cellular patterns can be difficult to assess. The retinal vasculature affords a large-scale 3D map on which cells and structures can be located during in vivo imaging. Phase-variance OCT (pv-OCT) can efficiently image the vasculature with near-infrared light in a label-free manner, allowing 3D vascular reconstruction with high precision. We combined widefield pv-OCT and SLO imaging with AO-SLO reflection and fluorescence imaging to localize two types of fluorescent cells within the retinal layers: GFP-expressing microglia, the resident macrophages of the retina, and GFP-expressing cone photoreceptor cells. We describe in detail a reflective afocal AO-SLO retinal imaging system designed for high resolution retinal imaging in mice. The optical performance of this instrument is compared to other state-of-the-art AO-based mouse retinal imaging systems. The spatial and temporal resolution of the new AO instrumentation was characterized with angiography of retinal capillaries, including blood-flow velocity analysis. Depth-resolved AO-SLO fluorescent images of microglia and cone photoreceptors are visualized in parallel with 469 nm and 663 nm reflectance images of the microvasculature and other structures. Additional applications of the new instrumentation are discussed.

  1. Motion-capture-based walking simulation of digital human adapted to laser-scanned 3D as-is environments for accessibility evaluation

    Directory of Open Access Journals (Sweden)

    Tsubasa Maruyama

    2016-07-01

    Full Text Available Owing to our rapidly aging society, accessibility evaluation to enhance the ease and safety of access to indoor and outdoor environments for the elderly and disabled is increasing in importance. Accessibility must be assessed not only from the general standard aspect but also in terms of physical and cognitive friendliness for users of different ages, genders, and abilities. Meanwhile, human behavior simulation has been progressing in the areas of crowd behavior analysis and emergency evacuation planning. However, in human behavior simulation, environment models represent only “as-planned” situations. In addition, a pedestrian model cannot generate the detailed articulated movements of various people of different ages and genders in the simulation. Therefore, the final goal of this research was to develop a virtual accessibility evaluation by combining realistic human behavior simulation using a digital human model (DHM with “as-is” environment models. To achieve this goal, we developed an algorithm for generating human-like DHM walking motions, adapting its strides, turning angles, and footprints to laser-scanned 3D as-is environments including slopes and stairs. The DHM motion was generated based only on a motion-capture (MoCap data for flat walking. Our implementation constructed as-is 3D environment models from laser-scanned point clouds of real environments and enabled a DHM to walk autonomously in various environment models. The difference in joint angles between the DHM and MoCap data was evaluated. Demonstrations of our environment modeling and walking simulation in indoor and outdoor environments including corridors, slopes, and stairs are illustrated in this study.

  2. Retrospectively-gated CINE (23)Na imaging of the heart at 7.0 Tesla using density-adapted 3D projection reconstruction.

    Science.gov (United States)

    Resetar, Ana; Hoffmann, Stefan H; Graessl, Andreas; Winter, Lukas; Waiczies, Helmar; Ladd, Mark E; Niendorf, Thoralf; Nagel, Armin M

    2015-11-01

    Implementation, evaluation and application of a pulse sequence for retrospectively-gated sodium magnetic resonance imaging of the human heart. Measurements were conducted at a magnetic field strength of 7.0 Tesla. A 3D projection reconstruction technique using a standard (ST) and a golden angle (GA) acquisition scheme for short echo time (23)Na MR was applied. Data were acquired continuously without cardiac triggering using a free breathing regime. Arbitrary phases of the cardiac cycle were reconstructed using synchronization with a physiological trigger signal and different temporal resolutions. Phantom measurements and examinations of healthy subjects were performed to evaluate the performance of the ST and GA acquisition schemes. A signal-to-background ratio (SBR)--that compromises both the signal-to-noise ratio and artifacts--was calculated for benchmarking the GA and ST scheme. In phantom measurements, the measured SBR of the GA acquisition scheme was up to 88% higher versus ST. Undersampling artifacts were reduced in GA compared to the ST sampling scheme. Whole heart coverage sodium images could be reconstructed with a nominal spatial resolution of (6 mm)(3) and a temporal resolution of Δt=0.1 s for covering the entire cardiac cycle. Changes in overall heart volume and myocardial wall thickness throughout the cardiac cycle were clearly visible in the reconstructed images. For the in vivo data and the imaging protocol used, GA provided a mean SBR of 38.0±5.5 while ST provided a mean SBR of 37.2±2.2. Retrospectively-gated CINE (23)Na imaging of the heart at 7.0 T using density-adapted 3D projection reconstruction is feasible. The GA acquisition scheme is superior to the ST acquisition. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. 3D Animation Essentials

    CERN Document Server

    Beane, Andy

    2012-01-01

    The essential fundamentals of 3D animation for aspiring 3D artists 3D is everywhere--video games, movie and television special effects, mobile devices, etc. Many aspiring artists and animators have grown up with 3D and computers, and naturally gravitate to this field as their area of interest. Bringing a blend of studio and classroom experience to offer you thorough coverage of the 3D animation industry, this must-have book shows you what it takes to create compelling and realistic 3D imagery. Serves as the first step to understanding the language of 3D and computer graphics (CG)Covers 3D anim

  4. 3D video

    CERN Document Server

    Lucas, Laurent; Loscos, Céline

    2013-01-01

    While 3D vision has existed for many years, the use of 3D cameras and video-based modeling by the film industry has induced an explosion of interest for 3D acquisition technology, 3D content and 3D displays. As such, 3D video has become one of the new technology trends of this century.The chapters in this book cover a large spectrum of areas connected to 3D video, which are presented both theoretically and technologically, while taking into account both physiological and perceptual aspects. Stepping away from traditional 3D vision, the authors, all currently involved in these areas, provide th

  5. Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

    Energy Technology Data Exchange (ETDEWEB)

    Juneja, Prabhjot, E-mail: Prabhjot.Juneja@icr.ac.uk [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom); Harris, Emma J. [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom); Kirby, Anna M. [Department of Academic Radiotherapy, Royal Marsden National Health Service Foundation Trust, Sutton (United Kingdom); Evans, Philip M. [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom)

    2012-11-01

    Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significant differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue

  6. 3D printing of intracranial artery stenosis based on the source images of magnetic resonance angiograph.

    Science.gov (United States)

    Xu, Wei-Hai; Liu, Jia; Li, Ming-Li; Sun, Zhao-Yong; Chen, Jie; Wu, Jian-Huang

    2014-08-01

    Three dimensional (3D) printing techniques for brain diseases have not been widely studied. We attempted to 'print' the segments of intracranial arteries based on magnetic resonance imaging. Three dimensional magnetic resonance angiography (MRA) was performed on two patients with middle cerebral artery (MCA) stenosis. Using scale-adaptive vascular modeling, 3D vascular models were constructed from the MRA source images. The magnified (ten times) regions of interest (ROI) of the stenotic segments were selected and fabricated by a 3D printer with a resolution of 30 µm. A survey to 8 clinicians was performed to evaluate the accuracy of 3D printing results as compared with MRA findings (4 grades, grade 1: consistent with MRA and provide additional visual information; grade 2: consistent with MRA; grade 3: not consistent with MRA; grade 4: not consistent with MRA and provide probable misleading information). If a 3D printing vessel segment was ideally matched to the MRA findings (grade 2 or 1), a successful 3D printing was defined. Seven responders marked "grade 1" to 3D printing results, while one marked "grade 4". Therefore, 87.5% of the clinicians considered the 3D printing were successful. Our pilot study confirms the feasibility of using 3D printing technique in the research field of intracranial artery diseases. Further investigations are warranted to optimize this technique and translate it into clinical practice.

  7. Research on adaptive segmentation and activity classification method of filamentous fungi image in microbe fermentation

    Science.gov (United States)

    Cai, Xiaochun; Hu, Yihua; Wang, Peng; Sun, Dujuan; Hu, Guilan

    2009-10-01

    The paper presents an adaptive segmentation and activity classification method for filamentous fungi image. Firstly, an adaptive structuring element (SE) construction algorithm is proposed for image background suppression. Based on watershed transform method, the color labeled segmentation of fungi image is taken. Secondly, the fungi elements feature space is described and the feature set for fungi hyphae activity classification is extracted. The growth rate evaluation of fungi hyphae is achieved by using SVM classifier. Some experimental results demonstrate that the proposed method is effective for filamentous fungi image processing.

  8. Structure Segmentation and Transfer Faults in the Marcellus Shale, Clearfield County, Pennsylvania: Implications for Gas Recovery Efficiency and Risk Assessment Using 3D Seismic Attribute Analysis

    Science.gov (United States)

    Roberts, Emily D.

    The Marcellus Shale has become an important unconventional gas reservoir in the oil and gas industry. Fractures within this organic-rich black shale serve as an important component of porosity and permeability useful in enhancing production. Horizontal drilling is the primary approach for extracting hydrocarbons in the Marcellus Shale. Typically, wells are drilled perpendicular to natural fractures in an attempt to intersect fractures for effective hydraulic stimulation. If the fractures are contained within the shale, then hydraulic fracturing can enhance permeability by further breaking the already weakened rock. However, natural fractures can affect hydraulic stimulations by absorbing and/or redirecting the energy away from the wellbore, causing a decreased efficiency in gas recovery, as has been the case for the Clearfield County, Pennsylvania study area. Estimating appropriate distances away from faults and fractures, which may limit hydrocarbon recovery, is essential to reducing the risk of injection fluid migration along these faults. In an attempt to mitigate the negative influences of natural fractures on hydrocarbon extraction within the Marcellus Shale, fractures were analyzed through the aid of both traditional and advanced seismic attributes including variance, curvature, ant tracking, and waveform model regression. Through the integration of well log interpretations and seismic data, a detailed assessment of structural discontinuities that may decrease the recovery efficiency of hydrocarbons was conducted. High-quality 3D seismic data in Central Pennsylvania show regional folds and thrusts above the major detachment interval of the Salina Salt. In addition to the regional detachment folds and thrusts, cross-regional, northwest-trending lineaments were mapped. These lineaments may pose a threat to hydrocarbon productivity and recovery efficiency due to faults and fractures acting as paths of least resistance for induced hydraulic stimulation fluids

  9. EUROPEANA AND 3D

    OpenAIRE

    D. Pletinckx

    2012-01-01

    The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There ar...

  10. Electric field theory based approach to search-direction line definition in image segmentation: application to optimal femur-tibia cartilage segmentation in knee-joint 3-D MR

    Science.gov (United States)

    Yin, Y.; Sonka, M.

    2010-03-01

    A novel method is presented for definition of search lines in a variety of surface segmentation approaches. The method is inspired by properties of electric field direction lines and is applicable to general-purpose n-D shapebased image segmentation tasks. Its utility is demonstrated in graph construction and optimal segmentation of multiple mutually interacting objects. The properties of the electric field-based graph construction guarantee that inter-object graph connecting lines are non-intersecting and inherently covering the entire object-interaction space. When applied to inter-object cross-surface mapping, our approach generates one-to-one and all-to-all vertex correspondent pairs between the regions of mutual interaction. We demonstrate the benefits of the electric field approach in several examples ranging from relatively simple single-surface segmentation to complex multiobject multi-surface segmentation of femur-tibia cartilage. The performance of our approach is demonstrated in 60 MR images from the Osteoarthritis Initiative (OAI), in which our approach achieved a very good performance as judged by surface positioning errors (average of 0.29 and 0.59 mm for signed and unsigned cartilage positioning errors, respectively).

  11. The interaction of neutral evolutionary processes with climatically-driven adaptive changes in the 3D shape of the human os coxae.

    Science.gov (United States)

    Betti, Lia; von Cramon-Taubadel, Noreen; Manica, Andrea; Lycett, Stephen J

    2014-08-01

    Differences in the breadth of the pelvis among modern human populations and among extinct hominin species have often been interpreted in the light of thermoregulatory adaptation, whereby a larger pelvic girdle would help preserve body temperature in cold environments while a narrower pelvis would help dissipate heat in tropical climates. There is, however, a theoretical problem in interpreting a pattern of variation as evidence of selection without first accounting for the effects of neutral evolutionary processes (i.e., mutation, genetic drift and migration). Here, we analyse 3D configurations of 27 landmarks on the os coxae of 1494 modern human individuals representing 30 male and 23 female populations from five continents and a range of climatic conditions. We test for the effects of climate on the size and shape of the pelvic bone, while explicitly accounting for population history (i.e., geographically-mediated gene flow and genetic drift). We find that neutral processes account for a substantial proportion of shape variance in the human os coxae in both sexes. Beyond the neutral pattern due to population history, temperature is a significant predictor of shape and size variation in the os coxae, at least in males. The effect of climate on the shape of the pelvic bone, however, is comparatively limited, explaining only a small percentage of shape variation in males and females. In accordance with previous hypotheses, the size of the os coxae tends to increase with decreasing temperature, although the significance of the association is reduced when population history is taken into account. In conclusion, the shape and size of the human os coxae reflect both neutral evolutionary processes and climatically-driven adaptive changes. Neutral processes have a substantial effect on pelvic variation, suggesting such factors will need to be taken into account in future studies of human and fossil hominin coxal variation. Copyright © 2014 Elsevier Ltd. All rights

  12. Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

    Science.gov (United States)

    Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

    2015-03-01

    Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

  13. Infants adapt to speaking rate differences in word segmentation.

    Science.gov (United States)

    Wang, Yuanyuan; Llanos, Fernando; Seidl, Amanda

    2017-04-01

    Throughout their development, infants are exposed to varying speaking rates. Thus, it is important to determine whether they are able to adapt to speech at varying rates and recognize target words from continuous speech despite speaking rate differences. To address this question, a series of four experiments were conducted to test whether infants can recognize words in continuous speech when rate is variable. In addition, the underlying mechanisms that infants may use to cope with variations induced by different speaking rates were also examined. Specifically, using the Headturn Preference procedure [Jusczyk and Aslin (1995). Cognitive Psychol. 29, 1-23], infants were familiarized with normal-rate passages containing two trisyllabic target words (e.g., elephants and dinosaurs), and tested with familiar (elephants and dinosaurs) and unfamiliar (crocodiles and platypus) words embedded in normal-rate (experiment 1), fast-rate (experiments 2 and 3), or slow-rate passages (experiment 4). The results indicate that 14-month-olds, but not 11-month-olds, recognized target words in passages with a fast speaking rate. In addition, findings suggest that infants used context to normalize speech across different speaking rates.

  14. Europeana and 3D

    Science.gov (United States)

    Pletinckx, D.

    2011-09-01

    The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There are several aspects to this endeavour. First of all is the technical aspect of flawlessly delivering 3D content over all platforms and operating systems, without installing software. We have currently a working solution in PDF, but HTML5 will probably be the future. Secondly, there is still little knowledge on how to create 3D learning objects, 3D tourist information or 3D scholarly communication. We are still in a prototype phase when it comes to integrate 3D objects in physical or virtual museums. Nevertheless, Europeana has a tremendous potential as a multi-facetted virtual museum. Finally, 3D has a large potential to act as a hub of information, linking to related 2D imagery, texts, video, sound. We describe how to create such rich, explorable 3D objects that can be used intuitively by the generic Europeana user and what metadata is needed to support the semantic linking.

  15. EUROPEANA AND 3D

    Directory of Open Access Journals (Sweden)

    D. Pletinckx

    2012-09-01

    Full Text Available The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There are several aspects to this endeavour. First of all is the technical aspect of flawlessly delivering 3D content over all platforms and operating systems, without installing software. We have currently a working solution in PDF, but HTML5 will probably be the future. Secondly, there is still little knowledge on how to create 3D learning objects, 3D tourist information or 3D scholarly communication. We are still in a prototype phase when it comes to integrate 3D objects in physical or virtual museums. Nevertheless, Europeana has a tremendous potential as a multi-facetted virtual museum. Finally, 3D has a large potential to act as a hub of information, linking to related 2D imagery, texts, video, sound. We describe how to create such rich, explorable 3D objects that can be used intuitively by the generic Europeana user and what metadata is needed to support the semantic linking.

  16. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

    Directory of Open Access Journals (Sweden)

    Hui Liu

    2015-01-01

    Full Text Available The key problem of computer-aided diagnosis (CAD of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO pulmonary nodules than other typical algorithms.

  17. Perceptual adaptation to segmental and syllabic reductions in continuous spoken Dutch

    NARCIS (Netherlands)

    Poellmann, K.; Bosker, H.R.; McQueen, J.M.; Mitterer, H.A.

    2014-01-01

    This study investigates if and how listeners adapt to reductions in casual continuous speech. In a perceptual-learning variant of the visual-world paradigm, two groups of Dutch participants were exposed to either segmental (/b/ -> [upsilon] or syllabic (ver- -> [f:]) reductions in spoken Dutch

  18. An automatic segmentation method of a parameter-adaptive PCNN for medical images.

    Science.gov (United States)

    Lian, Jing; Shi, Bin; Li, Mingcong; Nan, Ziwei; Ma, Yide

    2017-09-01

    Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision. The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter [Formula: see text] for different kinds of images. Secondly, we acquire the parameter [Formula: see text] according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter V of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset [Formula: see text] to improve initial segmentation precision. Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726. The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.

  19. 3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥1  s) ground motions and sensitivity to kinematic rupture parameters

    Science.gov (United States)

    Moschetti, Morgan P.; Hartzell, Stephen; Ramirez-Guzman, Leonardo; Frankel, Arthur; Angster, Stephen J.; Stephenson, William J.

    2017-01-01

    We examine the variability of long‐period (T≥1  s) earthquake ground motions from 3D simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone, Utah, from a set of 96 rupture models with varying slip distributions, rupture speeds, slip velocities, and hypocenter locations. Earthquake ruptures were prescribed on a 3D fault representation that satisfies geologic constraints and maintained distinct strands for the Warm Springs and for the East Bench and Cottonwood faults. Response spectral accelerations (SA; 1.5–10 s; 5% damping) were measured, and average distance scaling was well fit by a simple functional form that depends on the near‐source intensity level SA0(T) and a corner distance Rc:SA(R,T)=SA0(T)(1+(R/Rc))−1. Period‐dependent hanging‐wall effects manifested and increased the ground motions by factors of about 2–3, though the effects appeared partially attributable to differences in shallow site response for sites on the hanging wall and footwall of the fault. Comparisons with modern ground‐motion prediction equations (GMPEs) found that the simulated ground motions were generally consistent, except within deep sedimentary basins, where simulated ground motions were greatly underpredicted. Ground‐motion variability exhibited strong lateral variations and, at some sites, exceeded the ground‐motion variability indicated by GMPEs. The effects on the ground motions of changing the values of the five kinematic rupture parameters can largely be explained by three predominant factors: distance to high‐slip subevents, dynamic stress drop, and changes in the contributions from directivity. These results emphasize the need for further characterization of the underlying distributions and covariances of the kinematic rupture parameters used in 3D ground‐motion simulations employed in probabilistic seismic‐hazard analyses.

  20. 3D image-based adapted high-dose-rate brachytherapy in cervical cancer with and without interstitial needles: measurement of applicator shift between imaging and dose delivery

    Directory of Open Access Journals (Sweden)

    Leif Karlsson

    2017-02-01

    Full Text Available Purpose: Using 3D image-guided adaptive brachytherapy for cervical cancer treatment, it often means that patients are transported and moved during the treatment procedure. The purpose of this study was to determine the intra-fractional longitudinal applicator shift in relation to the high risk clinical target volume (HR-CTV by comparing geometries at imaging and dose delivery for patients with and without needles. Material and methods : Measurements were performed in 33 patients (71 fractions, where 25 fractions were without and 46 were with interstitial needles. Gold markers were placed in the lower part of the cervix as a surrogate for HR-CTV, enabling distance measurements between HR-CTV and the ring applicator. Shifts of the applicator relative to the markers were determined using planning computed tomography (CT images used for planning, and the radiographs obtained at dose delivery. Differences in the physical D90 for HR-CTV due to applicator shifts were simulated individually in the treatment planning system to provide the relative dose variation. Results : The maximum distances of the applicator shifts, in relation to the markers, were 3.6 mm (caudal, and –2.5 mm (cranial. There was a significant displacement of –0.7 mm (SD = 0.9 mm without needles, while with needles there was no significant shift. The relative dose variation showed a significant increase in D90 HR-CTV of 1.6% (SD = 2.6% when not using needles, and no significant dose variation was found when using needles. Conclusions : The results from this study showed that there was a small longitudinal displacement of the ring applicator and a significant difference in displacement between using interstitial needles or not.

  1. PLOT3D/AMES, APOLLO UNIX VERSION USING GMR3D (WITH TURB3D)

    Science.gov (United States)

    Buning, P.

    1994-01-01

    PLOT3D is an interactive graphics program designed to help scientists visualize computational fluid dynamics (CFD) grids and solutions. Today, supercomputers and CFD algorithms can provide scientists with simulations of such highly complex phenomena that obtaining an understanding of the simulations has become a major problem. Tools which help the scientist visualize the simulations can be of tremendous aid. PLOT3D/AMES offers more functions and features, and has been adapted for more types of computers than any other CFD graphics program. Version 3.6b+ is supported for five computers and graphic libraries. Using PLOT3D, CFD physicists can view their computational models from any angle, observing the physics of problems and the quality of solutions. As an aid in designing aircraft, for example, PLOT3D's interactive computer graphics can show vortices, temperature, reverse flow, pressure, and dozens of other characteristics of air flow during flight. As critical areas become obvious, they can easily be studied more closely using a finer grid. PLOT3D is part of a computational fluid dynamics software cycle. First, a program such as 3DGRAPE (ARC-12620) helps the scientist generate computational grids to model an object and its surrounding space. Once the grids have been designed and parameters such as the angle of attack, Mach number, and Reynolds number have been specified, a "flow-solver" program such as INS3D (ARC-11794 or COS-10019) solves the system of equations governing fluid flow, usually on a supercomputer. Grids sometimes have as many as two million points, and the "flow-solver" produces a solution file which contains density, x- y- and z-momentum, and stagnation energy for each grid point. With such a solution file and a grid file containing up to 50 grids as input, PLOT3D can calculate and graphically display any one of 74 functions, including shock waves, surface pressure, velocity vectors, and particle traces. PLOT3D's 74 functions are organized into

  2. PLOT3D/AMES, APOLLO UNIX VERSION USING GMR3D (WITHOUT TURB3D)

    Science.gov (United States)

    Buning, P.

    1994-01-01

    PLOT3D is an interactive graphics program designed to help scientists visualize computational fluid dynamics (CFD) grids and solutions. Today, supercomputers and CFD algorithms can provide scientists with simulations of such highly complex phenomena that obtaining an understanding of the simulations has become a major problem. Tools which help the scientist visualize the simulations can be of tremendous aid. PLOT3D/AMES offers more functions and features, and has been adapted for more types of computers than any other CFD graphics program. Version 3.6b+ is supported for five computers and graphic libraries. Using PLOT3D, CFD physicists can view their computational models from any angle, observing the physics of problems and the quality of solutions. As an aid in designing aircraft, for example, PLOT3D's interactive computer graphics can show vortices, temperature, reverse flow, pressure, and dozens of other characteristics of air flow during flight. As critical areas become obvious, they can easily be studied more closely using a finer grid. PLOT3D is part of a computational fluid dynamics software cycle. First, a program such as 3DGRAPE (ARC-12620) helps the scientist generate computational grids to model an object and its surrounding space. Once the grids have been designed and parameters such as the angle of attack, Mach number, and Reynolds number have been specified, a "flow-solver" program such as INS3D (ARC-11794 or COS-10019) solves the system of equations governing fluid flow, usually on a supercomputer. Grids sometimes have as many as two million points, and the "flow-solver" produces a solution file which contains density, x- y- and z-momentum, and stagnation energy for each grid point. With such a solution file and a grid file containing up to 50 grids as input, PLOT3D can calculate and graphically display any one of 74 functions, including shock waves, surface pressure, velocity vectors, and particle traces. PLOT3D's 74 functions are organized into

  3. 3D Integration for Wireless Multimedia

    Science.gov (United States)

    Kimmich, Georg

    The convergence of mobile phone, internet, mapping, gaming and office automation tools with high quality video and still imaging capture capability is becoming a strong market trend for portable devices. High-density video encode and decode, 3D graphics for gaming, increased application-software complexity and ultra-high-bandwidth 4G modem technologies are driving the CPU performance and memory bandwidth requirements close to the PC segment. These portable multimedia devices are battery operated, which requires the deployment of new low-power-optimized silicon process technologies and ultra-low-power design techniques at system, architecture and device level. Mobile devices also need to comply with stringent silicon-area and package-volume constraints. As for all consumer devices, low production cost and fast time-to-volume production is key for success. This chapter shows how 3D architectures can bring a possible breakthrough to meet the conflicting power, performance and area constraints. Multiple 3D die-stacking partitioning strategies are described and analyzed on their potential to improve the overall system power, performance and cost for specific application scenarios. Requirements and maturity of the basic process-technology bricks including through-silicon via (TSV) and die-to-die attachment techniques are reviewed. Finally, we highlight new challenges which will arise with 3D stacking and an outlook on how they may be addressed: Higher power density will require thermal design considerations, new EDA tools will need to be developed to cope with the integration of heterogeneous technologies and to guarantee signal and power integrity across the die stack. The silicon/wafer test strategies have to be adapted to handle high-density IO arrays, ultra-thin wafers and provide built-in self-test of attached memories. New standards and business models have to be developed to allow cost-efficient assembly and testing of devices from different silicon and technology

  4. 3d-3d correspondence revisited

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Hee-Joong [California Institute of Technology,Pasadena, CA 91125 (United States); Dimofte, Tudor [Institute for Advanced Study,Einstein Dr., Princeton, NJ 08540 (United States); Gukov, Sergei [California Institute of Technology,Pasadena, CA 91125 (United States); Max-Planck-Institut für Mathematik,Vivatsgasse 7, D-53111 Bonn (Germany); Sułkowski, Piotr [California Institute of Technology,Pasadena, CA 91125 (United States); Faculty of Physics, University of Warsaw,ul. Pasteura 5, 02-093 Warsaw (Poland)

    2016-04-21

    In fivebrane compactifications on 3-manifolds, we point out the importance of all flat connections in the proper definition of the effective 3d N=2 theory. The Lagrangians of some theories with the desired properties can be constructed with the help of homological knot invariants that categorify colored Jones polynomials. Higgsing the full 3d theories constructed this way recovers theories found previously by Dimofte-Gaiotto-Gukov. We also consider the cutting and gluing of 3-manifolds along smooth boundaries and the role played by all flat connections in this operation.

  5. Utility of real-time prospective motion correction (PROMO) for segmentation of cerebral cortex on 3D T1-weighted imaging: Voxel-based morphometry analysis for uncooperative patients

    Energy Technology Data Exchange (ETDEWEB)

    Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Narimatsu, Hidekuni; Ide, Satoru; Korogi, Yukunori [University of Occupational and Environmental Health School of Medicine, Department of Radiology, Kitakyushu (Japan); Nozaki, Atsushi [MR Applications and Workflow Asia Pacific GE Healthcare Japan, Tokyo (Japan); Rettmann, Dan [MR Applications and Workflow GE Healthcare, Rochester, MN (United States); Abe, Osamu [University of Tokyo, Department of Radiology, Graduate School of Medicine, Tokyo (Japan)

    2017-08-15

    To assess the utility of the motion correction method with prospective motion correction (PROMO) in a voxel-based morphometry (VBM) analysis for 'uncooperative' patient populations. High-resolution 3D T1-weighted imaging both with and without PROMO were performed in 33 uncooperative patients with Parkinson's disease (n = 11) or dementia (n = 22). We compared the grey matter (GM) volumes and cortical thickness between the scans with and without PROMO. For the mean total GM volume with the VBM analysis, the scan without PROMO showed a significantly smaller volume than that with PROMO (p < 0.05), which was caused by segmentation problems due to motion during acquisition. The whole-brain VBM analysis showed significant GM volume reductions in some regions in the scans without PROMO (familywise error corrected p < 0.05). In the cortical thickness analysis, the scans without PROMO also showed decreased cortical thickness compared to the scan with PROMO (p < 0.05). Our results with the uncooperative patients indicate that the use of PROMO can reduce misclassification during segmentation of the VBM analyses, although it may not prevent GM volume reduction. (orig.)

  6. A novel white blood cells segmentation algorithm based on adaptive neutrosophic similarity score.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2018-12-01

    White blood cells (WBCs) play a crucial role in the diagnosis of many diseases according to their numbers or morphology. The recent digital pathology equipments investigate and analyze the blood smear images automatically. The previous automated segmentation algorithms worked on healthy and non-healthy WBCs separately. Also, such algorithms had employed certain color components which leak adaptively with different datasets. In this paper, a novel segmentation algorithm for WBCs in the blood smear images is proposed using multi-scale similarity measure based on the neutrosophic domain. We employ neutrosophic similarity score to measure the similarity between different color components of the blood smear image. Since we utilize different color components from different color spaces, we modify the neutrosphic score algorithm to be adaptive. Two different segmentation frameworks are proposed: one for the segmentation of nucleus, and the other for the cytoplasm of WBCs. Moreover, our proposed algorithm is applied to both healthy and non-healthy WBCs. in some cases, the single blood smear image gather between healthy and non-healthy WBCs which is considered in our proposed algorithm. Also, our segmentation algorithm is performed without any external morphological binary enhancement methods which may effect on the original shape of the WBC. Different public datasets with different resolutions were used in our experiments. We evaluate the system performance based on both qualitative and quantitative measurements. The quantitative results indicates high precision rates of the segmentation performance measurement A1 = 96.5% and A2 = 97.2% of the proposed method. The average segmentation performance results for different WBCs types reach to 97.6%. In this paper, a method based on adaptive neutrosphic sets similarity score is proposed in order to detect WBCs from a blood smear microscopic image and segment its components (nucleus and the cytoplasm). The proposed

  7. Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions.

    Science.gov (United States)

    Galimzianova, Alfiia; Lesjak, Žiga; Rubin, Daniel L; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

    2018-01-01

    Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.

  8. Integrating an adaptive region-based appearance model with a landmark-free statistical shape model: application to prostate MRI segmentation

    Science.gov (United States)

    Toth, Robert; Bulman, Julie; Patel, Amish D.; Bloch, B. Nicholas; Genega, Elizabeth M.; Rofsky, Neil M.; Lenkinski, Robert E.; Madabhushi, Anant

    2011-03-01

    In this paper we present a system for segmenting medical images using statistical shape models (SSM's) which is landmark free, fully 3D, and accurate. To overcome the limitations associated with previous 3D landmark-based SSM's, our system creates a levelset-based SSM which uses the minimum distance from each voxel in the image to the object's surface to define a shape. Subsequently, an advanced statistical appearance model (SAM) is generated to model the object of interest. This SAM is based on a series of statistical texture features calculated from each image, modeled by a Gaussian Mixture Model. In order to segment the object of interest on a new image, a Bayesian classifier is first employed to pre-classify the image voxels as belonging to the foreground object of interest or the background. The result of the Bayesian classifier is then employed for optimally fitting the SSM so there is maximum agreement between the SAM and the SSM. The SAM is then able to adaptively learn the statistics of the textures of the foreground and background voxels on the new image. The fitting of the SSM, and the adaptive updating of the SAM is repeated until convergence. We have tested our system on 36 T2-w, 3.0 Tesla, in vivo, endorectal prostate images. The results showed that our system achieves a Dice similarity coefficient of .84+/-.04, with a median Dice value of .86, which is comparable (and in most cases superior) to other state of the art prostate segmentation systems. Further, unlike most other state of the art prostate segmentation schemes, our scheme is fully automated requiring no user intervention.

  9. Using the Technology Acceptance Model to explore community dwelling older adults' perceptions of a 3D interior design application to facilitate pre-discharge home adaptations.

    Science.gov (United States)

    Money, Arthur G; Atwal, Anita; Young, Katherine L; Day, Yasmin; Wilson, Lesley; Money, Kevin G

    2015-08-26

    In the UK occupational therapy pre-discharge home visits are routinely carried out as a means of facilitating safe transfer from the hospital to home. Whilst they are an integral part of practice, there is little evidence to demonstrate they have a positive outcome on the discharge process. Current issues for patients are around the speed of home visits and the lack of shared decision making in the process, resulting in less than 50 % of the specialist equipment installed actually being used by patients on follow-up. To improve practice there is an urgent need to examine other ways of conducting home visits to facilitate safe discharge. We believe that Computerised 3D Interior Design Applications (CIDAs) could be a means to support more efficient, effective and collaborative practice. A previous study explored practitioners perceptions of using CIDAs; however it is important to ascertain older adult's views about the usability of technology and to compare findings. This study explores the perceptions of community dwelling older adults with regards to adopting and using CIDAs as an assistive tool for the home adaptations process. Ten community dwelling older adults participated in individual interactive task-focused usability sessions with a customised CIDA, utilising the think-aloud protocol and individual semi-structured interviews. Template analysis was used to carry out both deductive and inductive analysis of the think-aloud and interview data. Initially, a deductive stance was adopted, using the three pre-determined high-level themes of the technology acceptance model (TAM): Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Actual Use (AU). Inductive template analysis was then carried out on the data within these themes, from which a number of sub-thmes emerged. Regarding PU, participants believed CIDAs served as a useful visual tool and saw clear potential to facilitate shared understanding and partnership in care delivery. For PEOU, participants were

  10. 3D and Education

    Science.gov (United States)

    Meulien Ohlmann, Odile

    2013-02-01

    Today the industry offers a chain of 3D products. Learning to "read" and to "create in 3D" becomes an issue of education of primary importance. 25 years professional experience in France, the United States and Germany, Odile Meulien set up a personal method of initiation to 3D creation that entails the spatial/temporal experience of the holographic visual. She will present some different tools and techniques used for this learning, their advantages and disadvantages, programs and issues of educational policies, constraints and expectations related to the development of new techniques for 3D imaging. Although the creation of display holograms is very much reduced compared to the creation of the 90ies, the holographic concept is spreading in all scientific, social, and artistic activities of our present time. She will also raise many questions: What means 3D? Is it communication? Is it perception? How the seeing and none seeing is interferes? What else has to be taken in consideration to communicate in 3D? How to handle the non visible relations of moving objects with subjects? Does this transform our model of exchange with others? What kind of interaction this has with our everyday life? Then come more practical questions: How to learn creating 3D visualization, to learn 3D grammar, 3D language, 3D thinking? What for? At what level? In which matter? for whom?

  11. Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm.

    Science.gov (United States)

    Jackman, Patrick; Sun, Da-Wen; Allen, Paul

    2009-10-01

    An algorithm for automatic segmentation of beef longissimus dorsi (LD) muscle and marbling has been developed. The algorithm used simple thresholding to remove the background and then used clustering and thresholding with contrast enhancement via a customised greyscale to remove marbling. It was possible to attain lean muscle free of obvious marbling or background pixels where specular reflection could be effectively mitigated. Features of the automatically derived LD muscle and marbling images were compared to corresponding features of LD muscle and marbling images derived with a segmentation method requiring manual completion. Very strong correlations (up to r=1) were found between the colour features of both sets of LD muscle images. Strong correlations (up to r=0.96) were found between the features of both sets of marbling images. The automatic segmentation method has shown its good ability to approximate colour and marbling features. The algorithm has adaptable parameters and can be retailored to suit different image acquisition environments.

  12. YouDash3D: exploring stereoscopic 3D gaming for 3D movie theaters

    Science.gov (United States)

    Schild, Jonas; Seele, Sven; Masuch, Maic

    2012-03-01

    Along with the success of the digitally revived stereoscopic cinema, events beyond 3D movies become attractive for movie theater operators, i.e. interactive 3D games. In this paper, we present a case that explores possible challenges and solutions for interactive 3D games to be played by a movie theater audience. We analyze the setting and showcase current issues related to lighting and interaction. Our second focus is to provide gameplay mechanics that make special use of stereoscopy, especially depth-based game design. Based on these results, we present YouDash3D, a game prototype that explores public stereoscopic gameplay in a reduced kiosk setup. It features live 3D HD video stream of a professional stereo camera rig rendered in a real-time game scene. We use the effect to place the stereoscopic effigies of players into the digital game. The game showcases how stereoscopic vision can provide for a novel depth-based game mechanic. Projected trigger zones and distributed clusters of the audience video allow for easy adaptation to larger audiences and 3D movie theater gaming.

  13. Refined 3d-3d correspondence

    Science.gov (United States)

    Alday, Luis F.; Genolini, Pietro Benetti; Bullimore, Mathew; van Loon, Mark

    2017-04-01

    We explore aspects of the correspondence between Seifert 3-manifolds and 3d N = 2 supersymmetric theories with a distinguished abelian flavour symmetry. We give a prescription for computing the squashed three-sphere partition functions of such 3d N = 2 theories constructed from boundary conditions and interfaces in a 4d N = 2∗ theory, mirroring the construction of Seifert manifold invariants via Dehn surgery. This is extended to include links in the Seifert manifold by the insertion of supersymmetric Wilson-'t Hooft loops in the 4d N = 2∗ theory. In the presence of a mass parameter cfor the distinguished flavour symmetry, we recover aspects of refined Chern-Simons theory with complex gauge group, and in particular construct an analytic continuation of the S-matrix of refined Chern-Simons theory.

  14. Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters

    Directory of Open Access Journals (Sweden)

    Xia Zheng

    2015-01-01

    Full Text Available Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.

  15. Automatic white matter lesion segmentation using an adaptive outlier detection method.

    Science.gov (United States)

    Ong, Kok Haur; Ramachandram, Dhanesh; Mandava, Rajeswari; Shuaib, Ibrahim Lutfi

    2012-07-01

    White matter (WM) lesions are diffuse WM abnormalities that appear as hyperintense (bright) regions in cranial magnetic resonance imaging (MRI). WM lesions are often observed in older populations and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. In this paper, a new automated method for WM lesions segmentation is presented. In the proposed method, the presence of WM lesions is detected as outliers in the intensity distribution of the fluid-attenuated inversion recovery (FLAIR) MR images using an adaptive outlier detection approach. Outliers are detected using a novel adaptive trimmed mean algorithm and box-whisker plot. In addition, pre- and postprocessing steps are implemented to reduce false positives attributed to MRI artifacts commonly observed in FLAIR sequences. The approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation (R=0.9641, P value=3.12×10(-3)) is observed between the automated approach and manual segmentation by radiologist. The accuracy of the proposed approach was further validated by comparing the lesion volumes computed using the automated approach and lesions manually segmented by an expert radiologist. Finally, the proposed approach is compared against leading lesion segmentation algorithms using a benchmark dataset. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.

  16. A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

    Science.gov (United States)

    Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

    2017-02-01

    Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

  17. TEHNOLOGIJE 3D TISKALNIKOV

    OpenAIRE

    Kolar, Nataša

    2016-01-01

    Diplomsko delo predstavi razvoj tiskanja skozi čas. Podrobneje so opisani 3D tiskalniki, ki uporabljajo različne tehnologije 3D tiskanja. Predstavljene so različne tehnologije 3D tiskanja, njihova uporaba in narejeni prototipi oz. končni izdelki. Diplomsko delo opiše celoten postopek, od zamisli, priprave podatkov in tiskalnika do izdelave prototipa oz. končnega izdelka.

  18. Unsupervised image segmentation based on the multi-resolution integration of adaptive local texture descriptions

    OpenAIRE

    Ilea, Dana E.; Whelan, Paul F.; Ghita, Ovidiu

    2010-01-01

    The major aim of this paper consists of a comprehensive quantitative evaluation of adaptive texture descriptors when integrated into an unsupervised image segmentation framework. The techniques involved in this evaluation are: the standard and rotation invariant Local Binary Pattern (LBP) operators, multichannel texture decomposition based on Gabor filters and a recently proposed technique that analyses the distribution of dominant image orientations at both micro and macro levels. These sele...

  19. Nanoworld in 3-D

    OpenAIRE

    Torrisi, Antonio

    2011-01-01

    The 3-D version of James Cameron’s last movie, “Avatar”, has been considered a breakthrough in the cinematographic world and I, personally, still remember the strong impact of the experience of watching this film at the IMAX-3D cinema. The 3-D movies must all be grateful to the advent of stereoscopic photography, which dates back to 1838, when Sir Charles Wheatstone invented the first 3-D stereoscope. Stereoscopy creates an illusion of depth by using eyeglasses to combine two perspectives (2-...

  20. Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

    Science.gov (United States)

    Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Lo, Po Kim; Yap, Vooi Voon

    2015-11-01

    Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region- and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics.

  1. Interpolation of 3D slice volume data for 3D printing

    Science.gov (United States)

    Littley, Samuel; Voiculescu, Irina

    2017-03-01

    Medical imaging from CT and MRI scans has become essential to clinicians for diagnosis, treatment planning and even prevention of a wide array of conditions. The presentation of image data volumes as 2D slice series provides some challenges with visualising internal structures. 3D reconstructions of organs and other tissue samples from data with low scan resolution leads to a `stepped' appearance. This paper demonstrates how to improve 3D visualisation of features and automated preparation for 3D printing from such low resolution data, using novel techniques for morphing from one slice to the next. The boundary of the starting contour is grown until it matches the boundary of the ending contour by adapting a variant of the Fast Marching Method (FMM). Our spoke based approach generates scalar speed field for FMM by estimating distances to boundaries with line segments connecting the two boundaries. These can be regularly spaced radial spokes or spokes at radial extrema. We introduce clamped FMM by running the algorithm outwards from the smaller boundary and inwards from the larger boundary and combining the two runs to achieve FMM growth stability near the two region boundaries. Our method inserts a series of uniformly distributed intermediate contours between each pair of consecutive slices from the scan volume thus creating smoother feature boundaries. Whilst hard to quantify, our overall results give clinicians an evidently improved tangible and tactile representation of the tissues, that they can examine more easily and even handle.

  2. 3D future internet media

    CERN Document Server

    Dagiuklas, Tasos

    2014-01-01

    This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The main contributions are based on the results of the FP7 European Projects ROMEO, which focus on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the Future Internet (www.ict-romeo.eu). The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of constant video quality to both fixed and mobile users. ROMEO will design and develop hybrid-networking solutions that co...

  3. Novel 3D media technologies

    CERN Document Server

    Dagiuklas, Tasos

    2015-01-01

    This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The contributions are based on the results of the FP7 European Project ROMEO, which focuses on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the future Internet. The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of consistent video quality to fixed and mobile users. ROMEO will present hybrid networking solutions that combine the DVB-T2 and DVB-NGH broadcas...

  4. Dimensional accuracy of 3D printed vertebra

    Science.gov (United States)

    Ogden, Kent; Ordway, Nathaniel; Diallo, Dalanda; Tillapaugh-Fay, Gwen; Aslan, Can

    2014-03-01

    3D printer applications in the biomedical sciences and medical imaging are expanding and will have an increasing impact on the practice of medicine. Orthopedic and reconstructive surgery has been an obvious area for development of 3D printer applications as the segmentation of bony anatomy to generate printable models is relatively straightforward. There are important issues that should be addressed when using 3D printed models for applications that may affect patient care; in particular the dimensional accuracy of the printed parts needs to be high to avoid poor decisions being made prior to surgery or therapeutic procedures. In this work, the dimensional accuracy of 3D printed vertebral bodies derived from CT data for a cadaver spine is compared with direct measurements on the ex-vivo vertebra and with measurements made on the 3D rendered vertebra using commercial 3D image processing software. The vertebra was printed on a consumer grade 3D printer using an additive print process using PLA (polylactic acid) filament. Measurements were made for 15 different anatomic features of the vertebral body, including vertebral body height, endplate width and depth, pedicle height and width, and spinal canal width and depth, among others. It is shown that for the segmentation and printing process used, the results of measurements made on the 3D printed vertebral body are substantially the same as those produced by direct measurement on the vertebra and measurements made on the 3D rendered vertebra.

  5. 3D virtuel udstilling

    DEFF Research Database (Denmark)

    Tournay, Bruno; Rüdiger, Bjarne

    2006-01-01

    3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s.......3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s....

  6. Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method.

    Science.gov (United States)

    Guo, Xiaoxi; Huang, Shaohui; Fu, Xiaozhu; Wang, Boliang; Huang, Xiaoyang

    2015-06-19

    Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshold value for final segmentation. In this paper, an accelerated strategy based on a lookup table was presented first which can reduce the connectivity scene calculation time and achieve a speed-up factor of above 2. When the computing of the fuzzy connectedness relations is finished, a threshold is needed to generate the final result. Currently the threshold is preset by users. Since different thresholds may produce different outcomes, how to determine a proper threshold is crucial. According to our analysis of the hepatic vessel structure, a watershed-like method was used to find the optimal threshold. Meanwhile, by using Ostu algorithm to calculate the parameters for affinity relations and assigning the seed with the mean value, it is able to reduce the influence on the segmentation result caused by the location of the seed and enhance the robustness of fuzzy connectedness method. Experiments based on four different datasets demonstrate the efficiency of the lookup table strategy. These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels. Comparing to a refined region-growing algorithm that has been widely used for hepatic vessel segmentation, fuzzy connectedness method has advantages in detecting vascular edge and generating more than one vessel system through the weak connectivity of the vessel ends. An improved algorithm based on fuzzy connectedness method is proposed. This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation.

  7. Blender 3D cookbook

    CERN Document Server

    Valenza, Enrico

    2015-01-01

    This book is aimed at the professionals that already have good 3D CGI experience with commercial packages and have now decided to try the open source Blender and want to experiment with something more complex than the average tutorials on the web. However, it's also aimed at the intermediate Blender users who simply want to go some steps further.It's taken for granted that you already know how to move inside the Blender interface, that you already have 3D modeling knowledge, and also that of basic 3D modeling and rendering concepts, for example, edge-loops, n-gons, or samples. In any case, it'

  8. Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2017-07-01

    The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system. The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset. The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069. The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. In situ repair of bone and cartilage defects using 3D scanning and 3D printing

    OpenAIRE

    Li, Lan; Yu, Fei; Shi, Jianping; Shen, Sheng; Teng, Huajian; Yang, Jiquan; Wang, Xingsong; Jiang, Qing

    2017-01-01

    Three-dimensional (3D) printing is a rapidly emerging technology that promises to transform tissue engineering into a commercially successful biomedical industry. However, the use of robotic bioprinters alone is not sufficient for disease treatment. This study aimed to report the combined application of 3D scanning and 3D printing for treating bone and cartilage defects. Three different kinds of defect models were created to mimic three orthopedic diseases: large segmental defects of long bon...

  10. DELTA 3D PRINTER

    Directory of Open Access Journals (Sweden)

    ȘOVĂILĂ Florin

    2016-07-01

    Full Text Available 3D printing is a very used process in industry, the generic name being “rapid prototyping”. The essential advantage of a 3D printer is that it allows the designers to produce a prototype in a very short time, which is tested and quickly remodeled, considerably reducing the required time to get from the prototype phase to the final product. At the same time, through this technique we can achieve components with very precise forms, complex pieces that, through classical methods, could have been accomplished only in a large amount of time. In this paper, there are presented the stages of a 3D model execution, also the physical achievement after of a Delta 3D printer after the model.

  11. Unsupervised mitochondria segmentation using recursive spectral clustering and adaptive similarity models.

    Science.gov (United States)

    Dietlmeier, Julia; Ghita, Ovidiu; Duessmann, Heiko; Prehn, Jochen H M; Whelan, Paul F

    2013-12-01

    The unsupervised segmentation method proposed in the current study follows the evolutional ability of human vision to extrapolate significant structures in an image. In this work we adopt the perceptual grouping strategy by selecting the spectral clustering framework, which is known to capture perceptual organization features, as well as by developing similarity models according to Gestaltic laws of visual segregation. Our proposed framework applies but is not limited to the detection of cells and organelles in microscopic images and attempts to provide an effective alternative to presently dominating manual segmentation and tissue classification practice. The main theoretical contribution of our work resides in the formulation of robust similarity models which automatically adapt to the statistical structure of the biological domain and return optimal performance in pixel classification tasks under the wide variety of distributional assumptions. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. Adaptive segmentation and mask-specific Sobolev inpainting of specular highlights for endoscopic images.

    Science.gov (United States)

    Alsaleh, Samar M; Aviles, Angelica I; Sobrevilla, Pilar; Casals, Alicia; Hahn, James K

    2016-08-01

    Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance. In this paper, we propose a segmentation method of the specular regions based on an automatic color-adaptive threshold and a gradient-based edge detector. The segmented regions are then recovered using a robust mask-specific Sobolev inpainting approach. Experimental results demonstrate the precision and efficiency of the proposed method. In contrast to the existing approaches, the proposed solution does not require manual threshold selection or complex computations to achieve accurate results. Moreover, our method has a real-time performance and can be generalized to various applications.

  13. AE3D

    Energy Technology Data Exchange (ETDEWEB)

    2016-06-20

    AE3D solves for the shear Alfven eigenmodes and eigenfrequencies in a torodal magnetic fusion confinement device. The configuration can be either 2D (e.g. tokamak, reversed field pinch) or 3D (e.g. stellarator, helical reversed field pinch, tokamak with ripple). The equations solved are based on a reduced MHD model and sound wave coupling effects are not currently included.

  14. Adaptive iterative dose reduction (AIDR) 3D in low dose CT abdomen-pelvis: Effects on image quality and radiation exposure

    Science.gov (United States)

    Ang, W. C.; Hashim, S.; Karim, M. K. A.; Bahruddin, N. A.; Salehhon, N.; Musa, Y.

    2017-05-01

    The widespread use of computed tomography (CT) has increased the medical radiation exposure and cancer risk. We aimed to evaluate the impact of AIDR 3D in CT abdomen-pelvic examinations based on image quality and radiation dose in low dose (LD) setting compared to standard dose (STD) with filtered back projection (FBP) reconstruction. We retrospectively reviewed the images of 40 patients who underwent CT abdomen-pelvic using a 80 slice CT scanner. Group 1 patients (n=20, mean age 41 ± 17 years) were performed at LD with AIDR 3D reconstruction and Group 2 patients (n=20, mean age 52 ± 21 years) were scanned with STD using FBP reconstruction. Objective image noise was assessed by region of interest (ROI) measurements in the liver and aorta as standard deviation (SD) of the attenuation value (Hounsfield Unit, HU) while subjective image quality was evaluated by two radiologists. Statistical analysis was used to compare the scan length, CT dose index volume (CTDIvol) and image quality of both patient groups. Although both groups have similar mean scan length, the CTDIvol significantly decreased by 38% in LD CT compared to STD CT (psuperior image quality in LD CT abdomen-pelvis.

  15. Adaptation Measurement of CAD/CAM Dental Crowns with X-Ray Micro-CT: Metrological Chain Standardization and 3D Gap Size Distribution

    Directory of Open Access Journals (Sweden)

    L. Tapie

    2016-01-01

    Full Text Available Computer-Aided Design and Manufacturing systems are increasingly used to produce dental prostheses, but the parts produced suffer from a lack of evaluation, especially concerning the internal gap of the final assembly, that is, the space between the prepared tooth and the prosthesis. X-ray micro-Computed Tomography (micro-CT is a noninvasive imaging technique enabling the internal inspection of the assembly. It has proved to be an efficient tool for measuring the gap. In this study, a critical review of the protocols using micro-CT to quantify the gap is proposed as an introduction to a new protocol aimed at minimizing errors and enabling comparison between CAD/CAM systems. To compare different systems, a standardized protocol is proposed including two reference geometries. Micro-CT is used to acquire the reference geometries. A new 3D method is then proposed and a new indicator is defined (Gap Size Distribution (GSD. In addition, the usual 2D measurements are described and discussed. The 3D gap measurement method proposed can be used in clinical case geometries and has the considerable advantage of minimizing the data processing steps before performing the measurements.

  16. 3D Digital Modelling

    DEFF Research Database (Denmark)

    Hundebøl, Jesper

    wave of new building information modelling tools demands further investigation, not least because of industry representatives' somewhat coarse parlance: Now the word is spreading -3D digital modelling is nothing less than a revolution, a shift of paradigm, a new alphabet... Research qeustions. Based...... on empirical probes (interviews, observations, written inscriptions) within the Danish construction industry this paper explores the organizational and managerial dynamics of 3D Digital Modelling. The paper intends to - Illustrate how the network of (non-)human actors engaged in the promotion (and arrest) of 3...... important to appreciate the analysis. Before turning to the presentation of preliminary findings and a discussion of 3D digital modelling, it begins, however, with an outline of industry specific ICT strategic issues. Paper type. Multi-site field study...

  17. 3D Projection Installations

    DEFF Research Database (Denmark)

    Halskov, Kim; Johansen, Stine Liv; Bach Mikkelsen, Michelle

    2014-01-01

    Three-dimensional projection installations are particular kinds of augmented spaces in which a digital 3-D model is projected onto a physical three-dimensional object, thereby fusing the digital content and the physical object. Based on interaction design research and media studies, this article...... contributes to the understanding of the distinctive characteristics of such a new medium, and identifies three strategies for designing 3-D projection installations: establishing space; interplay between the digital and the physical; and transformation of materiality. The principal empirical case, From...... Fingerplan to Loop City, is a 3-D projection installation presenting the history and future of city planning for the Copenhagen area in Denmark. The installation was presented as part of the 12th Architecture Biennale in Venice in 2010....

  18. 3-D Banegenerator

    OpenAIRE

    Majidian, Peshko

    2014-01-01

    Denne oppgaven har gått ut på å lage en banegenerator som simulerer banen til et fartøy i 3-D. Den spesifikke kraften og vinkelhastigheten har blitt regnet ut for å simulere akselerometer og gyroskop og ved å implementere et treghetsnavigasjonssystem (TNS), har det vært mulig å beregne banen til fartøyet ved å bruke sensordataene i navigasjonslikningene. En sirkelbane ble brukt til å verifisere TNS systemet før 3-D banen ble testet uten støy. Deretter skulle det legges til hvit og farget støy...

  19. 3D ARCHITECTURAL VIDEOMAPPING

    Directory of Open Access Journals (Sweden)

    R. Catanese

    2013-07-01

    Full Text Available 3D architectural mapping is a video projection technique that can be done with a survey of a chosen building in order to realize a perfect correspondence between its shapes and the images in projection. As a performative kind of audiovisual artifact, the real event of the 3D mapping is a combination of a registered video animation file with a real architecture. This new kind of visual art is becoming very popular and its big audience success testifies new expressive chances in the field of urban design. My case study has been experienced in Pisa for the Luminara feast in 2012.

  20. Herramientas SIG 3D

    Directory of Open Access Journals (Sweden)

    Francisco R. Feito Higueruela

    2010-04-01

    Full Text Available Applications of Geographical Information Systems on several Archeology fields have been increasing during the last years. Recent avances in these technologies make possible to work with more realistic 3D models. In this paper we introduce a new paradigm for this system, the GIS Thetrahedron, in which we define the fundamental elements of GIS, in order to provide a better understanding of their capabilities. At the same time the basic 3D characteristics of some comercial and open source software are described, as well as the application to some samples on archeological researchs

  1. Adaptations in rod outer segment disc membranes in response to environmental lighting conditions.

    Science.gov (United States)

    Rakshit, Tatini; Senapati, Subhadip; Parmar, Vipul M; Sahu, Bhubanananda; Maeda, Akiko; Park, Paul S-H

    2017-10-01

    The light-sensing rod photoreceptor cell exhibits several adaptations in response to the lighting environment. While adaptations to short-term changes in lighting conditions have been examined in depth, adaptations to long-term changes in lighting conditions are less understood. Atomic force microscopy was used to characterize the structure of rod outer segment disc membranes, the site of photon absorption by the pigment rhodopsin, to better understand how photoreceptor cells respond to long-term lighting changes. Structural properties of the disc membrane changed in response to housing mice in constant dark or light conditions and these adaptive changes required output from the phototransduction cascade initiated by rhodopsin. Among these were changes in the packing density of rhodopsin in the membrane, which was independent of rhodopsin synthesis and specifically affected scotopic visual function as assessed by electroretinography. Studies here support the concept of photostasis, which maintains optimal photoreceptor cell function with implications in retinal degenerations. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations.

    Science.gov (United States)

    Nanayakkara, Nuwan D; Samarabandu, Jagath; Fenster, Aaron

    2006-04-07

    Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.

  3. Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

    Directory of Open Access Journals (Sweden)

    Luis Daniel Lledó

    2015-03-01

    Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

  4. Adaptation de maillage non structuré 3D pour les problèmes instationnaires.Application à la mécanique des fluides

    OpenAIRE

    Alauzet, Frédéric; Frey, Pascal; George, Paul-Louis; Mohammadi, Bijan

    2003-01-01

    Ce rapport aborde le thème de l'adaptation de maillage non structuré en trois dimensions pour les problèmes instationnaires et son application à la mécanique des fluides. On y montre en particulier que l'algorithme classique d'adaptation est inapproprié pour traiter ce type de problèmes. Par conséquent, on propose ici une autre approche basée sur un nouvel algorithme d'adaptation et sur une procédure d'intersection de métriques en temps, permettant de capturer de tels phénomènes. Plus précisé...

  5. Tangible 3D Modelling

    DEFF Research Database (Denmark)

    Hejlesen, Aske K.; Ovesen, Nis

    2012-01-01

    This paper presents an experimental approach to teaching 3D modelling techniques in an Industrial Design programme. The approach includes the use of tangible free form models as tools for improving the overall learning. The paper is based on lecturer and student experiences obtained through facil...

  6. 3D Wire 2015

    DEFF Research Database (Denmark)

    Jordi, Moréton; F, Escribano; J. L., Farias

    This document is a general report on the implementation of gamification in 3D Wire 2015 event. As the second gamification experience in this event, we have delved deeply in the previous objectives (attracting public areas less frequented exhibition in previous years and enhance networking) and have...

  7. 2D to 3D conversion implemented in different hardware

    Science.gov (United States)

    Ramos-Diaz, Eduardo; Gonzalez-Huitron, Victor; Ponomaryov, Volodymyr I.; Hernandez-Fragoso, Araceli

    2015-02-01

    Conversion of available 2D data for release in 3D content is a hot topic for providers and for success of the 3D applications, in general. It naturally completely relies on virtual view synthesis of a second view given by original 2D video. Disparity map (DM) estimation is a central task in 3D generation but still follows a very difficult problem for rendering novel images precisely. There exist different approaches in DM reconstruction, among them manually and semiautomatic methods that can produce high quality DMs but they demonstrate hard time consuming and are computationally expensive. In this paper, several hardware implementations of designed frameworks for an automatic 3D color video generation based on 2D real video sequence are proposed. The novel framework includes simultaneous processing of stereo pairs using the following blocks: CIE L*a*b* color space conversions, stereo matching via pyramidal scheme, color segmentation by k-means on an a*b* color plane, and adaptive post-filtering, DM estimation using stereo matching between left and right images (or neighboring frames in a video), adaptive post-filtering, and finally, the anaglyph 3D scene generation. Novel technique has been implemented on DSP TMS320DM648, Matlab's Simulink module over a PC with Windows 7, and using graphic card (NVIDIA Quadro K2000) demonstrating that the proposed approach can be applied in real-time processing mode. The time values needed, mean Similarity Structural Index Measure (SSIM) and Bad Matching Pixels (B) values for different hardware implementations (GPU, Single CPU, and DSP) are exposed in this paper.

  8. 3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells

    OpenAIRE

    Tong Luo; Huan Chen; Kassab, Ghassan S.

    2016-01-01

    Aims The 3D geometry of individual vascular smooth muscle cells (VSMCs), which are essential for understanding the mechanical function of blood vessels, are currently not available. This paper introduces a new 3D segmentation algorithm to determine VSMC morphology and orientation. Methods and Results A total of 112 VSMCs from six porcine coronary arteries were used in the analysis. A 3D semi-automatic segmentation method was developed to reconstruct individual VSMCs from cell clumps as well a...

  9. 3-D Cataract Imaging System

    Science.gov (United States)

    Otten, L. J.; Soliz, P.; McMakin, I.; Greenaway, A.; Blanchard, P.; Ogawa, G.

    This paper describes a new adaptive optics instrument and associated diagnostic system for volumetric, in vivo imaging of the human lens and visual acuity characterization. The system is designed to allow one to capture simultaneous, in-focus images of the human lens at multiple "image planes." Based on the adaptation of a deformable grating originally developed for atmospheric turbulence measurements, the instrument will demonstrate an improvement over current techniques for imaging cortical, nuclear and posterior subcapsular cataracts. The system will characterize the human lens optically and will automatically produce an estimate of visual function as affected by the measured abnormalities in the lens. The process that Kestrel and DERA Malvern will use to demonstrate the key techniques for simultaneously acquiring in vivo lens imagery at multiple focus planes employs a surrogate lens. Eventually the camera could be considered as a replacement for most standard slit lamp instruments allowing them to be converted into a 3-D imaging system.

  10. 3D Microchannel Co-Culture: Method and Biological Validation

    OpenAIRE

    Bauer, Maret; Su, Gui; Beebe, David J; Friedl, Andreas

    2010-01-01

    Conventional 3D culture is typically performed in multi-well plates (e.g. 12 wells). The volumes and dimensions necessitate relatively large numbers of cells and fluid exchange steps are not easily automated limiting throughput. 3D microchannel culture can overcome these challenges simplifying 3D culture processes. However, the adaptation of immunocytochemical endpoint measurements and the validation of microchannel 3D culture with conventional 3D culture are needed before widespread adoption...

  11. Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming.

    Science.gov (United States)

    Pitiot, Alain; Toga, Arthur W; Thompson, Paul M

    2002-08-01

    This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequent quantitative analysis. We propose a new hybrid strategy that combines a general elastic template matching approach and an evolutionary heuristic. The evolutionary algorithm uses prior statistical information about the shape of the target structure to control the behavior of a number of deformable templates. Each template, modeled in the form of a B-spline, is warped in a potential field which is itself dynamically adapted. Such a hybrid scheme proves to be promising: by maintaining a population of templates, we cover a large domain of the solution space under the global guidance of the evolutionary heuristic, and thoroughly explore interesting areas. We address key issues of automated image segmentation systems. The potential fields are initially designed based on the spatial features of the edges in the input image, and are subjected to spatially adaptive diffusion to guarantee the deformation of the template. This also improves its global consistency and convergence speed. The deformation algorithm can modify the internal structure of the templates to allow a better match. We investigate in detail the preprocessing phase that the images undergo before they can be used more effectively in the iterative elastic matching procedure: a texture classifier, trained via linear discriminant analysis of a learning set, is used to enhance the contrast of the target structure with respect to surrounding tissues. We show how these techniques interact within a statistically driven evolutionary scheme to achieve a better tradeoff between template flexibility and sensitivity to noise and outliers. We focus on understanding the features of template matching that are most beneficial in terms of the achieved match. Examples from simulated and real image data are discussed, with considerations of

  12. In situ repair of bone and cartilage defects using 3D scanning and 3D printing.

    Science.gov (United States)

    Li, Lan; Yu, Fei; Shi, Jianping; Shen, Sheng; Teng, Huajian; Yang, Jiquan; Wang, Xingsong; Jiang, Qing

    2017-08-25

    Three-dimensional (3D) printing is a rapidly emerging technology that promises to transform tissue engineering into a commercially successful biomedical industry. However, the use of robotic bioprinters alone is not sufficient for disease treatment. This study aimed to report the combined application of 3D scanning and 3D printing for treating bone and cartilage defects. Three different kinds of defect models were created to mimic three orthopedic diseases: large segmental defects of long bones, free-form fracture of femoral condyle, and International Cartilage Repair Society grade IV chondral lesion. Feasibility of in situ 3D bioprinting for these diseases was explored. The 3D digital models of samples with defects and corresponding healthy parts were obtained using high-resolution 3D scanning. The Boolean operation was used to achieve the shape of the defects, and then the target geometries were imported in a 3D bioprinter. Two kinds of photopolymerized hydrogels were synthesized as bioinks. Finally, the defects of bone and cartilage were restored perfectly in situ using 3D bioprinting. The results of this study suggested that 3D scanning and 3D bioprinting could provide another strategy for tissue engineering and regenerative medicine.

  13. Adaptive output-based command shaping for sway control of a 3D overhead crane with payload hoisting and wind disturbance

    Science.gov (United States)

    Abdullahi, Auwalu M.; Mohamed, Z.; Selamat, H.; Pota, Hemanshu R.; Zainal Abidin, M. S.; Ismail, F. S.; Haruna, A.

    2018-01-01

    Payload hoisting and wind disturbance during crane operations are among the challenging factors that affect a payload sway and thus, affect the crane's performance. This paper proposes a new online adaptive output-based command shaping (AOCS) technique for an effective payload sway reduction of an overhead crane under the influence of those effects. This technique enhances the previously developed output-based command shaping (OCS) which was effective only for a fixed system and without external disturbances. Unlike the conventional input shaping design technique which requires the system's natural frequency and damping ratio, the proposed technique is designed by using the output signal and thus, an online adaptive algorithm can be formulated. To test the effectiveness of the AOCS, experiments are carried out using a laboratory overhead crane with a payload hoisting in the presence of wind, and with different payloads. The superiority of the method is confirmed by 82% and 29% reductions in the overall sway and the maximum transient sway respectively, when compared to the OCS, and two robust input shapers namely Zero Vibration Derivative-Derivative and Extra-Insensitive shapers. Furthermore, the method demonstrates a uniform crane's performance under all conditions. It is envisaged that the proposed method can be very useful in designing an effective controller for a crane system with an unknown payload and under the influence of external disturbances.

  14. From medical imaging data to 3D printed anatomical models.

    Science.gov (United States)

    Bücking, Thore M; Hill, Emma R; Robertson, James L; Maneas, Efthymios; Plumb, Andrew A; Nikitichev, Daniil I

    2017-01-01

    Anatomical models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D) printers have made it possible to create cost-efficient patient-specific models without expert knowledge. We introduce a general workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT)) to 3D printed physical models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an anatomical model from medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.

  15. Spatial Evidential Clustering With Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images.

    Science.gov (United States)

    Lian, Chunfeng; Ruan, Su; Denoux, Thierry; Li, Hua; Vera, Pierre

    2018-01-01

    While the accurate delineation of tumor volumes in FDG-positron emission tomography (PET) is a vital task for diverse objectives in clinical oncology, noise and blur due to the imaging system make it a challenging work. In this paper, we propose to address the imprecision and noise inherent in PET using Dempster-Shafer theory, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. Based on Dempster-Shafer theory, a novel evidential clustering algorithm is proposed and tailored for the tumor segmentation task in three-dimensional. For accurate clustering of PET voxels, each voxel is described not only by the single intensity value but also complementarily by textural features extracted from a patch surrounding the voxel. Considering that there are a large amount of textures without consensus regarding the most informative ones, and some of the extracted features are even unreliable due to the low-quality PET images, a specific procedure is included in the proposed clustering algorithm to adapt distance metric for properly representing the clustering distortions and the similarities between neighboring voxels. This integrated metric adaptation procedure will realize a low-dimensional transformation from the original space, and will limit the influence of unreliable inputs via feature selection. A Dempster-Shafer-theory-based spatial regularization is also proposed and included in the clustering algorithm, so as to effectively quantify the local homogeneity. The proposed method has been compared with other methods on the real-patient FDG-PET images, showing good performance.

  16. An aerial 3D printing test mission

    Science.gov (United States)

    Hirsch, Michael; McGuire, Thomas; Parsons, Michael; Leake, Skye; Straub, Jeremy

    2016-05-01

    This paper provides an overview of an aerial 3D printing technology, its development and its testing. This technology is potentially useful in its own right. In addition, this work advances the development of a related in-space 3D printing technology. A series of aerial 3D printing test missions, used to test the aerial printing technology, are discussed. Through completing these test missions, the design for an in-space 3D printer may be advanced. The current design for the in-space 3D printer involves focusing thermal energy to heat an extrusion head and allow for the extrusion of molten print material. Plastics can be used as well as composites including metal, allowing for the extrusion of conductive material. A variety of experiments will be used to test this initial 3D printer design. High altitude balloons will be used to test the effects of microgravity on 3D printing, as well as parabolic flight tests. Zero pressure balloons can be used to test the effect of long 3D printing missions subjected to low temperatures. Vacuum chambers will be used to test 3D printing in a vacuum environment. The results will be used to adapt a current prototype of an in-space 3D printer. Then, a small scale prototype can be sent into low-Earth orbit as a 3-U cube satellite. With the ability to 3D print in space demonstrated, future missions can launch production hardware through which the sustainability and durability of structures in space will be greatly improved.

  17. Viability of 3 D Woven Carbon Cloth and Advanced Carbon-Carbon Ribs for Adaptive Deployable Entry Placement Technology (ADEPT) for Future NASA Missions

    Science.gov (United States)

    Venkatapathy, Ethiraj; Arnold, James O.; Peterson, K. H.; Blosser, M. L.

    2013-01-01

    This paper describes aerothermodynamic and thermal structural testing that demonstrate the viability of three dimensional woven carbon cloth and advanced carbon-carbon (ACC) ribs for use in the Adaptive Deployable Entry Placement Technology (ADEPT). ADEPT is an umbrella-like entry system that is folded for stowage in the launch vehicle's shroud and deployed prior to reaching the atmeopheric interface. A key feature of the ADEPT concept is a lower ballistic coefficient for delivery of a given payload than seen with conventional, rigid body entry systems. The benefits that accrue from the lower ballistic coefficient incllude factor-of-ten reductions of deceleration forces and entry heating. The former enables consideration of new classes of scientific instruments for solar system exploration while the latter enables the design of a more efficient thermal protection system. The carbon cloth base lined for ADEPT has a dual use in that it serves as the thermal protection system and as the "skin" that transfers aerdynamic deceleration loads to its umbrella-like substructure. Arcjet testing described in this paper was conducted for some of the higher heating conditions for a future Venus mission using the ADEPT concept, thereby showing that the carbon cloth can perform in a relevant entry environment. Recently completed the thermal structural testing of the cloth attached to a representative ACC rib design is also described. Finally, this paper describes a preliminary engineering level code, based on the arcjet data, that can be used to estimate cloth thickness for future ADEPT missions and to predict carbon cloth performance in future arcjet tests.

  18. Shaping 3-D boxes

    DEFF Research Database (Denmark)

    Stenholt, Rasmus; Madsen, Claus B.

    2011-01-01

    Enabling users to shape 3-D boxes in immersive virtual environments is a non-trivial problem. In this paper, a new family of techniques for creating rectangular boxes of arbitrary position, orientation, and size is presented and evaluated. These new techniques are based solely on position data......F) docking experiment against an existing technique, which requires the user to perform the rotation and scaling of the box explicitly. The precision of the users' box construction is evaluated by a novel error metric measuring the difference between two boxes. The results of the experiment strongly indicate...... that for precision docking of 9 DoF boxes, some of the proposed techniques are significantly better than ones with explicit rotation and scaling. Another interesting result is that the number of DoF simultaneously controlled by the user significantly influences the precision of the docking....

  19. Image segmentation for uranium isotopic analysis by SIMS: Combined adaptive thresholding and marker controlled watershed approach

    Energy Technology Data Exchange (ETDEWEB)

    Willingham, David G.; Naes, Benjamin E.; Heasler, Patrick G.; Zimmer, Mindy M.; Barrett, Christopher A.; Addleman, Raymond S.

    2016-05-31

    A novel approach to particle identification and particle isotope ratio determination has been developed for nuclear safeguard applications. This particle search approach combines an adaptive thresholding algorithm and marker-controlled watershed segmentation (MCWS) transform, which improves the secondary ion mass spectrometry (SIMS) isotopic analysis of uranium containing particle populations for nuclear safeguards applications. The Niblack assisted MCWS approach (a.k.a. SEEKER) developed for this work has improved the identification of isotopically unique uranium particles under conditions that have historically presented significant challenges for SIMS image data processing techniques. Particles obtained from five NIST uranium certified reference materials (CRM U129A, U015, U150, U500 and U850) were successfully identified in regions of SIMS image data 1) where a high variability in image intensity existed, 2) where particles were touching or were in close proximity to one another and/or 3) where the magnitude of ion signal for a given region was count limited. Analysis of the isotopic distributions of uranium containing particles identified by SEEKER showed four distinct, accurately identified 235U enrichment distributions, corresponding to the NIST certified 235U/238U isotope ratios for CRM U129A/U015 (not statistically differentiated), U150, U500 and U850. Additionally, comparison of the minor uranium isotope (234U, 235U and 236U) atom percent values verified that, even in the absence of high precision isotope ratio measurements, SEEKER could be used to segment isotopically unique uranium particles from SIMS image data. Although demonstrated specifically for SIMS analysis of uranium containing particles for nuclear safeguards, SEEKER has application in addressing a broad set of image processing challenges.

  20. Adaptive stress response in segmental progeria resembles long-lived dwarfism and calorie restriction in mice.

    Directory of Open Access Journals (Sweden)

    Marieke van de Ven

    2006-12-01

    Full Text Available How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a likely contributing mechanism. Contrary to expectations, neither accelerated senescence nor acute oxidative stress hypersensitivity was detected in primary fibroblast or erythroblast cultures from multiple progeroid mouse models for defects in the nucleotide excision DNA repair pathway, which share premature aging features including postnatal growth retardation, cerebellar ataxia, and death before weaning. Instead, we report a prominent phenotypic overlap with long-lived dwarfism and calorie restriction during postnatal development (2 wk of age, including reduced size, reduced body temperature, hypoglycemia, and perturbation of the growth hormone/insulin-like growth factor 1 neuroendocrine axis. These symptoms were also present at 2 wk of age in a novel progeroid nucleotide excision repair-deficient mouse model (XPD(G602D/R722W/XPA(-/- that survived weaning with high penetrance. However, despite persistent cachectic dwarfism, blood glucose and serum insulin-like growth factor 1 levels returned to normal by 10 wk, with hypoglycemia reappearing near premature death at 5 mo of age. These data strongly suggest changes in energy metabolism as part of an adaptive response during the stressful period of postnatal growth. Interestingly, a similar perturbation of the postnatal growth axis was not detected in another progeroid mouse model, the double-strand DNA break repair deficient Ku80(-/- mouse. Specific (but not all types of genome instability may thus engage a conserved response to stress that evolved to cope with environmental pressures such as food shortage.

  1. Adaptive stress response in segmental progeria resembles long-lived dwarfism and calorie restriction in mice

    NARCIS (Netherlands)

    van de Ven, Marieke; Andressoo, Jaan-Olle; Holcomb, Valerie B.; von Lindern, Marieke; Jong, Willeke M. C.; de Zeeuw, Chris I.; Suh, Yousin; Hasty, Paul; Hoeijmakers, Jan H. J.; van der Horst, Gijsbertus T. J.; Mitchell, James R.

    2006-01-01

    How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a

  2. Ultralow-dose CT of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction and model-based iterative reconstruction: 2D and 3D image quality.

    Science.gov (United States)

    Widmann, Gerlig; Schullian, Peter; Gassner, Eva-Maria; Hoermann, Romed; Bale, Reto; Puelacher, Wolfgang

    2015-03-01

    OBJECTIVE. The purpose of this article is to evaluate 2D and 3D image quality of high-resolution ultralow-dose CT images of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in comparison with standard filtered backprojection (FBP). MATERIALS AND METHODS. A formalin-fixed human cadaver head was scanned using a clinical reference protocol at a CT dose index volume of 30.48 mGy and a series of five ultralow-dose protocols at 3.48, 2.19, 0.82, 0.44, and 0.22 mGy using FBP and ASIR at 50% (ASIR-50), ASIR at 100% (ASIR-100), and MBIR. Blinded 2D axial and 3D volume-rendered images were compared with each other by three readers using top-down scoring. Scores were analyzed per protocol or dose and reconstruction. All images were compared with the FBP reference at 30.48 mGy. A nonparametric Mann-Whitney U test was used. Statistical significance was set at p reconstructions at 3.48 mGy; FBP and ASIR-100 at 2.19 mGy; FBP, ASIR-100, and MBIR at 0.82 mGy; MBIR at 0.44 mGy; and MBIR at 0.22 mGy. CONCLUSION. MBIR (2D and 3D) and ASIR-100 (2D) may significantly improve subjective image quality of ultralow-dose images and may allow more than 90% dose reductions.

  3. Intraoral 3D scanner

    Science.gov (United States)

    Kühmstedt, Peter; Bräuer-Burchardt, Christian; Munkelt, Christoph; Heinze, Matthias; Palme, Martin; Schmidt, Ingo; Hintersehr, Josef; Notni, Gunther

    2007-09-01

    Here a new set-up of a 3D-scanning system for CAD/CAM in dental industry is proposed. The system is designed for direct scanning of the dental preparations within the mouth. The measuring process is based on phase correlation technique in combination with fast fringe projection in a stereo arrangement. The novelty in the approach is characterized by the following features: A phase correlation between the phase values of the images of two cameras is used for the co-ordinate calculation. This works contrary to the usage of only phase values (phasogrammetry) or classical triangulation (phase values and camera image co-ordinate values) for the determination of the co-ordinates. The main advantage of the method is that the absolute value of the phase at each point does not directly determine the coordinate. Thus errors in the determination of the co-ordinates are prevented. Furthermore, using the epipolar geometry of the stereo-like arrangement the phase unwrapping problem of fringe analysis can be solved. The endoscope like measurement system contains one projection and two camera channels for illumination and observation of the object, respectively. The new system has a measurement field of nearly 25mm × 15mm. The user can measure two or three teeth at one time. So the system can by used for scanning of single tooth up to bridges preparations. In the paper the first realization of the intraoral scanner is described.

  4. 3D Printing and 3D Bioprinting in Pediatrics.

    Science.gov (United States)

    Vijayavenkataraman, Sanjairaj; Fuh, Jerry Y H; Lu, Wen Feng

    2017-07-13

    Additive manufacturing, commonly referred to as 3D printing, is a technology that builds three-dimensional structures and components layer by layer. Bioprinting is the use of 3D printing technology to fabricate tissue constructs for regenerative medicine from cell-laden bio-inks. 3D printing and bioprinting have huge potential in revolutionizing the field of tissue engineering and regenerative medicine. This paper reviews the application of 3D printing and bioprinting in the field of pediatrics.

  5. 3D Printing and 3D Bioprinting in Pediatrics

    OpenAIRE

    Vijayavenkataraman, Sanjairaj; Fuh, Jerry Y H; Lu, Wen Feng

    2017-01-01

    Additive manufacturing, commonly referred to as 3D printing, is a technology that builds three-dimensional structures and components layer by layer. Bioprinting is the use of 3D printing technology to fabricate tissue constructs for regenerative medicine from cell-laden bio-inks. 3D printing and bioprinting have huge potential in revolutionizing the field of tissue engineering and regenerative medicine. This paper reviews the application of 3D printing and bioprinting in the field of pediatrics.

  6. Compact 3D camera

    Science.gov (United States)

    Bothe, Thorsten; Osten, Wolfgang; Gesierich, Achim; Jueptner, Werner P. O.

    2002-06-01

    A new, miniaturized fringe projection system is presented which has a size and handling that approximates to common 2D cameras. The system is based on the fringe projection technique. A miniaturized fringe projector and camera are assembled into a housing of 21x20x11 cm size with a triangulation basis of 10 cm. The advantage of the small triangulation basis is the possibility to measure difficult objects with high gradients. Normally a small basis has the disadvantage of reduced sensitivity. We investigated in methods to compensate the reduced sensitivity via setup and enhanced evaluation methods. Special hardware issues are a high quality, bright light source (and components to handle the high luminous flux) as well as adapted optics to gain a large aperture angle and a focus scan unit to increase the usable measurement volume. Adaptable synthetic wavelengths and integration times were used to increase the measurement quality and allow robust measurements that are adaptable to the desired speed and accuracy. Algorithms were developed to generate automatic focus positions to completely cover extended measurement volumes. Principles, setup, measurement examples and applications are shown.

  7. 3D printing for dummies

    CERN Document Server

    Hausman, Kalani Kirk

    2014-01-01

    Get started printing out 3D objects quickly and inexpensively! 3D printing is no longer just a figment of your imagination. This remarkable technology is coming to the masses with the growing availability of 3D printers. 3D printers create 3-dimensional layered models and they allow users to create prototypes that use multiple materials and colors.  This friendly-but-straightforward guide examines each type of 3D printing technology available today and gives artists, entrepreneurs, engineers, and hobbyists insight into the amazing things 3D printing has to offer. You'll discover methods for

  8. Using Ignorance in 3D Scene Understanding

    OpenAIRE

    Bogdan Harasymowicz-Boggio; Barbara Siemiątkowska

    2014-01-01

    Awareness of its own limitations is a fundamental feature of the human sight, which has been almost completely omitted in computer vision systems. In this paper we present a method of explicitly using information about perceptual limitations of a 3D vision system, such as occluded areas, limited field of view, loss of precision along with distance increase, and imperfect segmentation for a better understanding of the observed scene. The proposed mechanism integrates metric and semantic infere...

  9. 3D visualisation of underground pipelines : Best strategy for 3D scene creation

    NARCIS (Netherlands)

    Guerrero, J.M.; Zlatanova, S.; Meijers, B.M.

    2013-01-01

    Underground pipelines pose numerous challenges to 3D visualization. Pipes and cables are conceptually simple and narrow objects with clearly defined shapes, spanned over large geographical areas and made of multiple segments. Pipes are usually maintained as linear objects in the databases. However,

  10. Lunaserv Global Explorer, 3D

    Science.gov (United States)

    Miconi, C. E.; Estes, N. M.; Bowman-Cisneros, E.; Robinson, M. S.

    2015-06-01

    Lunaserv Global Explorer 3D is a platform independent, planetary data visualization application, which serves high resolution base-map imagery and terrain from web map service data sources, and displays it on a 3D spinning-globe interface.

  11. The Future Is 3D

    Science.gov (United States)

    Carter, Luke

    2015-01-01

    3D printers are a way of producing a 3D model of an item from a digital file. The model builds up in successive layers of material placed by the printer controlled by the information in the computer file. In this article the author argues that 3D printers are one of the greatest technological advances of recent times. He discusses practical uses…

  12. The 3D additivist cookbook

    NARCIS (Netherlands)

    Allahyari, Morehshin; Rourke, Daniel; Rasch, Miriam

    The 3D Additivist Cookbook, devised and edited by Morehshin Allahyari & Daniel Rourke, is a free compendium of imaginative, provocative works from over 100 world-leading artists, activists and theorists. The 3D Additivist Cookbook contains .obj and .stl files for the 3D printer, as well as critical

  13. The Future Is 3D

    Science.gov (United States)

    Carter, Luke

    3D printers are a way of producing a 3D model of an item from a digital file. The model builds up in successive layers of material placed by the printer controlled by the information in the computer file. In this article the author argues that 3D printers are one of the greatest technological advances of recent times. He discusses practical uses…

  14. Proposal of custom made wrist orthoses based on 3D modelling and 3D printing.

    Science.gov (United States)

    Abreu de Souza, Mauren; Schmitz, Cristiane; Marega Pinhel, Marcelo; Palma Setti, Joao A; Nohama, Percy

    2017-07-01

    Accessibility to three-dimensional (3D) technologies, such as 3D scanning systems and additive manufacturing (like 3D printers), allows a variety of 3D applications. For medical applications in particular, these modalities are gaining a lot of attention enabling several opportunities for healthcare applications. The literature brings several cases applying both technologies, but none of them focus on the spreading of how this technology could benefit the health segment. This paper proposes a new methodology, which employs both 3D modelling and 3D printing for building orthoses, which could better fit the demands of different patients. Additionally, there is an opportunity for sharing expertise, as it represents a trendy in terms of the maker-movement. Therefore, as a result of the proposed approach, we present a case study based on a volunteer who needs an immobilization orthosis, which was built for exemplification of the whole process. This proposal also employs freely available 3D models and software, having a strong social impact. As a result, it enables the implementation and effective usability for a variety of built to fit solutions, hitching useful and smarter technologies for the healthcare sector.

  15. Adaptive local multi-atlas segmentation: application to the heart and the caudate nucleus.

    NARCIS (Netherlands)

    Rikxoort, E.M. van; Isgum, I.; Arzhaeva, Y.; Staring, M.; Klein, S.; Viergever, M.A.; Pluim, J.P.; Ginneken, B. van

    2010-01-01

    Atlas-based segmentation is a powerful generic technique for automatic delineation of structures in volumetric images. Several studies have shown that multi-atlas segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on volumetric data is

  16. Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

    DEFF Research Database (Denmark)

    Puonti, Oula; Van Leemput, Koen

    2016-01-01

    In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional...... in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations....... restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset...

  17. 3D Structure of Tillage Soils

    Science.gov (United States)

    González-Torre, Iván; Losada, Juan Carlos; Falconer, Ruth; Hapca, Simona; Tarquis, Ana M.

    2015-04-01

    application of multifractal analysis methods in images for the study of soil structure. Master thesis, UPM, 2014. Houston, A.N.; S. Schmidt, A.M. Tarquis, W. Otten, P.C. Baveye, S.M. Hapca. Effect of scanning and image reconstruction settings in X-ray computed tomography on soil image quality and segmentation performance. Geoderma, 207-208, 154-165, 2013a. Houston, A, Otten, W., Baveye, Ph., Hapca, S. Adaptive-Window Indicator Kriging: A Thresholding Method for Computed Tomography, Computers & Geosciences, 54, 239-248, 2013b. Tarquis, A.M., R.J. Heck, D. Andina, A. Alvarez and J.M. Antón. Multifractal analysis and thresholding of 3D soil images. Ecological Complexity, 6, 230-239, 2009. Tarquis, A.M.; D. Giménez, A. Saa, M.C. Díaz. and J.M. Gascó. Scaling and Multiscaling of Soil Pore Systems Determined by Image Analysis. Scaling Methods in Soil Systems. Pachepsky, Radcliffe and Selim Eds., 19-33, 2003. CRC Press, Boca Ratón, Florida. Acknowledgements First author acknowledges the financial support obtained from Soil Imaging Laboratory (University of Gueplh, Canada) in 2014.

  18. 3D IBFV : Hardware-Accelerated 3D Flow Visualization

    NARCIS (Netherlands)

    Telea, Alexandru; Wijk, Jarke J. van

    2003-01-01

    We present a hardware-accelerated method for visualizing 3D flow fields. The method is based on insertion, advection, and decay of dye. To this aim, we extend the texture-based IBFV technique for 2D flow visualization in two main directions. First, we decompose the 3D flow visualization problem in a

  19. Using 3D in Visualization

    DEFF Research Database (Denmark)

    Wood, Jo; Kirschenbauer, Sabine; Döllner, Jürgen

    2005-01-01

    to display 3D imagery. The extra cartographic degree of freedom offered by using 3D is explored and offered as a motivation for employing 3D in visualization. The use of VR and the construction of virtual environments exploit navigational and behavioral realism, but become most usefil when combined...... with abstracted representations embedded in a 3D space. The interactions between development of geovisualization, the technology used to implement it and the theory surrounding cartographic representation are explored. The dominance of computing technologies, driven particularly by the gaming industry...

  20. 3D for Graphic Designers

    CERN Document Server

    Connell, Ellery

    2011-01-01

    Helping graphic designers expand their 2D skills into the 3D space The trend in graphic design is towards 3D, with the demand for motion graphics, animation, photorealism, and interactivity rapidly increasing. And with the meteoric rise of iPads, smartphones, and other interactive devices, the design landscape is changing faster than ever.2D digital artists who need a quick and efficient way to join this brave new world will want 3D for Graphic Designers. Readers get hands-on basic training in working in the 3D space, including product design, industrial design and visualization, modeling, ani

  1. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  2. 3-D printers for libraries

    CERN Document Server

    Griffey, Jason

    2014-01-01

    As the maker movement continues to grow and 3-D printers become more affordable, an expanding group of hobbyists is keen to explore this new technology. In the time-honored tradition of introducing new technologies, many libraries are considering purchasing a 3-D printer. Jason Griffey, an early enthusiast of 3-D printing, has researched the marketplace and seen several systems first hand at the Consumer Electronics Show. In this report he introduces readers to the 3-D printing marketplace, covering such topics asHow fused deposition modeling (FDM) printing workBasic terminology such as build

  3. 3D puzzle reconstruction for archeological fragments

    Science.gov (United States)

    Jampy, F.; Hostein, A.; Fauvet, E.; Laligant, O.; Truchetet, F.

    2015-03-01

    The reconstruction of broken artifacts is a common task in archeology domain; it can be supported now by 3D data acquisition device and computer processing. Many works have been dedicated in the past to reconstructing 2D puzzles but very few propose a true 3D approach. We present here a complete solution including a dedicated transportable 3D acquisition set-up and a virtual tool with a graphic interface allowing the archeologists to manipulate the fragments and to, interactively, reconstruct the puzzle. The whole lateral part is acquired by rotating the fragment around an axis chosen within a light sheet thanks to a step-motor synchronized with the camera frame clock. Another camera provides a top view of the fragment under scanning. A scanning accuracy of 100μm is attained. The iterative automatic processing algorithm is based on segmentation into facets of the lateral part of the fragments followed by a 3D matching providing the user with a ranked short list of possible assemblies. The device has been applied to the reconstruction of a set of 1200 fragments from broken tablets supporting a Latin inscription dating from the first century AD.

  4. 3-D imaging of the CNS.

    Science.gov (United States)

    Runge, V M; Gelblum, D Y; Wood, M L

    1990-01-01

    3-D gradient echo techniques, and in particular FLASH, represent a significant advance in MR imaging strategy allowing thin section, high resolution imaging through a large region of interest. Anatomical areas of application include the brain, spine, and extremities, although the majority of work to date has been performed in the brain. Superior T1 contrast and thus sensitivity to the presence of GdDTPA is achieved with 3-D FLASH when compared to 2-D spin echo technique. There is marked arterial and venous enhancement following Gd DTPA administration on 3-D FLASH, a less common finding with 2-D spin echo. Enhancement of the falx and tentorium is also more prominent. From a single data acquisition, requiring less than 11 min of scan time, high resolution reformatted sagittal, coronal, and axial images can obtained in addition to sections in any arbitrary plane. Tissue segmentation techniques can be applied and lesions displayed in three dimensions. These results may lead to the replacement of 2-D spin echo with 3-D FLASH for high resolution T1-weighted MR imaging of the CNS, particularly in the study of mass lesions and structural anomalies. The application of similar T2-weighted gradient echo techniques may follow, however the signal-to-noise ratio which can be achieved remains a potential limitation.

  5. Assessing hippocampal development and language in early childhood: Evidence from a new application of the Automatic Segmentation Adapter Tool.

    Science.gov (United States)

    Lee, Joshua K; Nordahl, Christine W; Amaral, David G; Lee, Aaron; Solomon, Marjorie; Ghetti, Simona

    2015-11-01

    Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically