Automatic analysis of digitized TV-images by a computer-driven optical microscope

International Nuclear Information System (INIS)

Rosa, G.; Di Bartolomeo, A.; Grella, G.; Romano, G.

1997-01-01

New methods of image analysis and three-dimensional pattern recognition were developed in order to perform the automatic scan of nuclear emulsion pellicles. An optical microscope, with a motorized stage, was equipped with a CCD camera and an image digitizer, and interfaced to a personal computer. Selected software routines inspired the design of a dedicated hardware processor. Fast operation, high efficiency and accuracy were achieved. First applications to high-energy physics experiments are reported. Further improvements are in progress, based on a high-resolution fast CCD camera and on programmable digital signal processors. Applications to other research fields are envisaged. (orig.)

SU-C-201-04: Quantification of Perfusion Heterogeneity Based On Texture Analysis for Fully Automatic Detection of Ischemic Deficits From Myocardial Perfusion Imaging

International Nuclear Information System (INIS)

Fang, Y; Huang, H; Su, T

2015-01-01

Purpose: Texture-based quantification of image heterogeneity has been a popular topic for imaging studies in recent years. As previous studies mainly focus on oncological applications, we report our recent efforts of applying such techniques on cardiac perfusion imaging. A fully automated procedure has been developed to perform texture analysis for measuring the image heterogeneity. Clinical data were used to evaluate the preliminary performance of such methods. Methods: Myocardial perfusion images of Thallium-201 scans were collected from 293 patients with suspected coronary artery disease. Each subject underwent a Tl-201 scan and a percutaneous coronary intervention (PCI) within three months. The PCI Result was used as the gold standard of coronary ischemia of more than 70% stenosis. Each Tl-201 scan was spatially normalized to an image template for fully automatic segmentation of the LV. The segmented voxel intensities were then carried into the texture analysis with our open-source software Chang Gung Image Texture Analysis toolbox (CGITA). To evaluate the clinical performance of the image heterogeneity for detecting the coronary stenosis, receiver operating characteristic (ROC) analysis was used to compute the overall accuracy, sensitivity and specificity as well as the area under curve (AUC). Those indices were compared to those obtained from the commercially available semi-automatic software QPS. Results: With the fully automatic procedure to quantify heterogeneity from Tl-201 scans, we were able to achieve a good discrimination with good accuracy (74%), sensitivity (73%), specificity (77%) and AUC of 0.82. Such performance is similar to those obtained from the semi-automatic QPS software that gives a sensitivity of 71% and specificity of 77%. Conclusion: Based on fully automatic procedures of data processing, our preliminary data indicate that the image heterogeneity of myocardial perfusion imaging can provide useful information for automatic determination

Semi-Automatic Removal of Foreground Stars from Images of Galaxies

Science.gov (United States)

Frei, Zsolt

1996-07-01

A new procedure, designed to remove foreground stars from galaxy proviles is presented here. Although several programs exist for stellar and faint object photometry, none of them treat star removal from the images very carefully. I present my attempt to develop such a system, and briefly compare the performance of my software to one of the well-known stellar photometry packages, DAOPhot (Stetson 1987). Major steps in my procedure are: (1) automatic construction of an empirical 2D point spread function from well separated stars that are situated off the galaxy; (2) automatic identification of those peaks that are likely to be foreground stars, scaling the PSF and removing these stars, and patching residuals (in the automatically determined smallest possible area where residuals are truly significant); and (3) cosmetic fix of remaining degradations in the image. The algorithm and software presented here is significantly better for automatic removal of foreground stars from images of galaxies than DAOPhot or similar packages, since: (a) the most suitable stars are selected automatically from the image for the PSF fit; (b) after star-removal an intelligent and automatic procedure removes any possible residuals; (c) unlimited number of images can be cleaned in one run without any user interaction whatsoever. (SECTION: Computing and Data Analysis)

Automatic Segmentation of Dermoscopic Images by Iterative Classification

Directory of Open Access Journals (Sweden)

Maciel Zortea

2011-01-01

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

An analysis of line-drawings based upon automatically inferred grammar and its application to chest x-ray images

International Nuclear Information System (INIS)

Nakayama, Akira; Yoshida, Yuuji; Fukumura, Teruo

1984-01-01

There is a technique using inferring grammer as image- structure analyzing technique. This technique involves a few problems if it is applied to naturally obtained images, as the practical grammatical technique for two-dimensional image is not established. The authors developed a technique which solved the above problems for the main purpose of the automated structure analysis of naturally obtained image. The first half of this paper describes on the automatic inference of line drawing generation grammar and the line drawing analysis based on that automatic inference. The second half of the paper reports on the actual analysis. The proposed technique is that to extract object line drawings out of the line drawings containing noise. The technique was evaluated for its effectiveness with an example of extracting rib center lines out of thin line chest X-ray images having practical scale and complexity. In this example, the total number of characteristic points (ends, branch points and intersections) composing line drawings per one image was 377, and the total number of line segments composing line drawings was 566 on average per sheet. The extraction ratio was 86.6 % which seemed to be proper when the complexity of input line drawings was considered. Further, the result was compared with the identified rib center lines with the automatic screening system AISCR-V3 for comparison with the conventional processing technique, and it was satisfactory when the versatility of this method was considered. (Wakatsuki, Y.)

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

Science.gov (United States)

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

2011-01-01

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

Automatic extraction of left ventricle in SPECT myocardial perfusion imaging

International Nuclear Information System (INIS)

Liu Li; Zhao Shujun; Yao Zhiming; Wang Daoyu

1999-01-01

An automatic method of extracting left ventricle from SPECT myocardial perfusion data was introduced. This method was based on the least square analysis of the positions of all short-axis slices pixels from the half sphere-cylinder myocardial model, and used a iterative reconstruction technique to automatically cut off the non-left ventricular tissue from the perfusion images. Thereby, this technique provided the bases for further quantitative analysis

Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences.

Science.gov (United States)

Gupta, Vikas; Hendriks, Emile A; Milles, Julien; van der Geest, Rob J; Jerosch-Herold, Michael; Reiber, Johan H C; Lelieveldt, Boudewijn P F

2010-11-01

Derivation of diagnostically relevant parameters from first-pass myocardial perfusion magnetic resonance images involves the tedious and time-consuming manual segmentation of the myocardium in a large number of images. To reduce the manual interaction and expedite the perfusion analysis, we propose an automatic registration and segmentation method for the derivation of perfusion linked parameters. A complete automation was accomplished by first registering misaligned images using a method based on independent component analysis, and then using the registered data to automatically segment the myocardium with active appearance models. We used 18 perfusion studies (100 images per study) for validation in which the automatically obtained (AO) contours were compared with expert drawn contours on the basis of point-to-curve error, Dice index, and relative perfusion upslope in the myocardium. Visual inspection revealed successful segmentation in 15 out of 18 studies. Comparison of the AO contours with expert drawn contours yielded 2.23 ± 0.53 mm and 0.91 ± 0.02 as point-to-curve error and Dice index, respectively. The average difference between manually and automatically obtained relative upslope parameters was found to be statistically insignificant (P = .37). Moreover, the analysis time per slice was reduced from 20 minutes (manual) to 1.5 minutes (automatic). We proposed an automatic method that significantly reduced the time required for analysis of first-pass cardiac magnetic resonance perfusion images. The robustness and accuracy of the proposed method were demonstrated by the high spatial correspondence and statistically insignificant difference in perfusion parameters, when AO contours were compared with expert drawn contours. Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.

Automatic cloud coverage assessment of Formosat-2 image

Science.gov (United States)

Hsu, Kuo-Hsien

2011-11-01

Formosat-2 satellite equips with the high-spatial-resolution (2m ground sampling distance) remote sensing instrument. It has been being operated on the daily-revisiting mission orbit by National Space organization (NSPO) of Taiwan since May 21 2004. NSPO has also serving as one of the ground receiving stations for daily processing the received Formosat- 2 images. The current cloud coverage assessment of Formosat-2 image for NSPO Image Processing System generally consists of two major steps. Firstly, an un-supervised K-means method is used for automatically estimating the cloud statistic of Formosat-2 image. Secondly, manual estimation of cloud coverage from Formosat-2 image is processed by manual examination. Apparently, a more accurate Automatic Cloud Coverage Assessment (ACCA) method certainly increases the efficiency of processing step 2 with a good prediction of cloud statistic. In this paper, mainly based on the research results from Chang et al, Irish, and Gotoh, we propose a modified Formosat-2 ACCA method which considered pre-processing and post-processing analysis. For pre-processing analysis, cloud statistic is determined by using un-supervised K-means classification, Sobel's method, Otsu's method, non-cloudy pixels reexamination, and cross-band filter method. Box-Counting fractal method is considered as a post-processing tool to double check the results of pre-processing analysis for increasing the efficiency of manual examination.

Semi-automatic system for UV images analysis of historical musical instruments

Science.gov (United States)

Dondi, Piercarlo; Invernizzi, Claudia; Licchelli, Maurizio; Lombardi, Luca; Malagodi, Marco; Rovetta, Tommaso

2015-06-01

The selection of representative areas to be analyzed is a common problem in the study of Cultural Heritage items. UV fluorescence photography is an extensively used technique to highlight specific surface features which cannot be observed in visible light (e.g. restored parts or treated with different materials), and it proves to be very effective in the study of historical musical instruments. In this work we propose a new semi-automatic solution for selecting areas with the same perceived color (a simple clue of similar materials) on UV photos, using a specifically designed interactive tool. The proposed method works in two steps: (i) users select a small rectangular area of the image; (ii) program automatically highlights all the areas that have the same color of the selected input. The identification is made by the analysis of the image in HSV color model, the most similar to the human perception. The achievable result is more accurate than a manual selection, because it can detect also points that users do not recognize as similar due to perception illusion. The application has been developed following the rules of usability, and Human Computer Interface has been improved after a series of tests performed by expert and non-expert users. All the experiments were performed on UV imagery of the Stradivari violins collection stored by "Museo del Violino" in Cremona.

Automatic Cobb Angle Determination From Radiographic Images

NARCIS (Netherlands)

Sardjono, Tri Arief; Wilkinson, Michael H. F.; Veldhuizen, Albert G.; van Ooijen, Peter M. A.; Purnama, Ketut E.; Verkerke, Gijsbertus J.

2013-01-01

Study Design. Automatic measurement of Cobb angle in patients with scoliosis. Objective. To test the accuracy of an automatic Cobb angle determination method from frontal radiographical images. Summary of Background Data. Thirty-six frontal radiographical images of patients with scoliosis. Methods.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

Directory of Open Access Journals (Sweden)

Kemal Akyol

2016-01-01

Full Text Available With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

Automatic system for quantification and visualization of lung aeration on chest computed tomography images: the Lung Image System Analysis - LISA

International Nuclear Information System (INIS)

Felix, John Hebert da Silva; Cortez, Paulo Cesar; Holanda, Marcelo Alcantara

2010-01-01

High Resolution Computed Tomography (HRCT) is the exam of choice for the diagnostic evaluation of lung parenchyma diseases. There is an increasing interest for computational systems able to automatically analyze the radiological densities of the lungs in CT images. The main objective of this study is to present a system for the automatic quantification and visualization of the lung aeration in HRCT images of different degrees of aeration, called Lung Image System Analysis (LISA). The secondary objective is to compare LISA to the Osiris system and also to specific algorithm lung segmentation (ALS), on the accuracy of the lungs segmentation. The LISA system automatically extracts the following image attributes: lungs perimeter, cross sectional area, volume, the radiological densities histograms, the mean lung density (MLD) in Hounsfield units (HU), the relative area of the lungs with voxels with density values lower than -950 HU (RA950) and the 15th percentile of the least density voxels (PERC15). Furthermore, LISA has a colored mask algorithm that applies pseudo-colors to the lung parenchyma according to the pre-defined radiological density chosen by the system user. The lungs segmentations of 102 images of 8 healthy volunteers and 141 images of 11 patients with Chronic Obstructive Pulmonary Disease (COPD) were compared on the accuracy and concordance among the three methods. The LISA was more effective on lungs segmentation than the other two methods. LISA's color mask tool improves the spatial visualization of the degrees of lung aeration and the various attributes of the image that can be extracted may help physicians and researchers to better assess lung aeration both quantitatively and qualitatively. LISA may have important clinical and research applications on the assessment of global and regional lung aeration and therefore deserves further developments and validation studies. (author)

Automatic system for quantification and visualization of lung aeration on chest computed tomography images: the Lung Image System Analysis - LISA

Energy Technology Data Exchange (ETDEWEB)

Felix, John Hebert da Silva; Cortez, Paulo Cesar, E-mail: jhsfelix@gmail.co [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil). Dept. de Engenharia de Teleinformatica; Holanda, Marcelo Alcantara [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil). Hospital Universitario Walter Cantidio. Dept. de Medicina Clinica

2010-12-15

High Resolution Computed Tomography (HRCT) is the exam of choice for the diagnostic evaluation of lung parenchyma diseases. There is an increasing interest for computational systems able to automatically analyze the radiological densities of the lungs in CT images. The main objective of this study is to present a system for the automatic quantification and visualization of the lung aeration in HRCT images of different degrees of aeration, called Lung Image System Analysis (LISA). The secondary objective is to compare LISA to the Osiris system and also to specific algorithm lung segmentation (ALS), on the accuracy of the lungs segmentation. The LISA system automatically extracts the following image attributes: lungs perimeter, cross sectional area, volume, the radiological densities histograms, the mean lung density (MLD) in Hounsfield units (HU), the relative area of the lungs with voxels with density values lower than -950 HU (RA950) and the 15th percentile of the least density voxels (PERC15). Furthermore, LISA has a colored mask algorithm that applies pseudo-colors to the lung parenchyma according to the pre-defined radiological density chosen by the system user. The lungs segmentations of 102 images of 8 healthy volunteers and 141 images of 11 patients with Chronic Obstructive Pulmonary Disease (COPD) were compared on the accuracy and concordance among the three methods. The LISA was more effective on lungs segmentation than the other two methods. LISA's color mask tool improves the spatial visualization of the degrees of lung aeration and the various attributes of the image that can be extracted may help physicians and researchers to better assess lung aeration both quantitatively and qualitatively. LISA may have important clinical and research applications on the assessment of global and regional lung aeration and therefore deserves further developments and validation studies. (author)

Automatic Contour Extraction from 2D Image

Directory of Open Access Journals (Sweden)

Panagiotis GIOANNIS

2011-03-01

Full Text Available Aim: To develop a method for automatic contour extraction from a 2D image. Material and Method: The method is divided in two basic parts where the user initially chooses the starting point and the threshold. Finally the method is applied to computed tomography of bone images. Results: An interesting method is developed which can lead to a successful boundary extraction of 2D images. Specifically data extracted from a computed tomography images can be used for 2D bone reconstruction. Conclusions: We believe that such an algorithm or part of it can be applied on several other applications for shape feature extraction in medical image analysis and generally at computer graphics.

Fully automatic algorithm for the analysis of vessels in the angiographic image of the eye fundus

Directory of Open Access Journals (Sweden)

Koprowski Robert

2012-06-01

Full Text Available Abstract Background The available scientific literature contains descriptions of manual, semi-automated and automated methods for analysing angiographic images. The presented algorithms segment vessels calculating their tortuosity or number in a given area. We describe a statistical analysis of the inclination of the vessels in the fundus as related to their distance from the center of the optic disc. Methods The paper presents an automated method for analysing vessels which are found in angiographic images of the eye using a Matlab implemented algorithm. It performs filtration and convolution operations with suggested masks. The result is an image containing information on the location of vessels and their inclination angle in relation to the center of the optic disc. This is a new approach to the analysis of vessels whose usefulness has been confirmed in the diagnosis of hypertension. Results The proposed algorithm analyzed and processed the images of the eye fundus using a classifier in the form of decision trees. It enabled the proper classification of healthy patients and those with hypertension. The result is a very good separation of healthy subjects from the hypertensive ones: sensitivity - 83%, specificity - 100%, accuracy - 96%. This confirms a practical usefulness of the proposed method. Conclusions This paper presents an algorithm for the automatic analysis of morphological parameters of the fundus vessels. Such an analysis is performed during fluorescein angiography of the eye. The presented algorithm automatically calculates the global statistical features connected with both tortuosity of vessels and their total area or their number.

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

OpenAIRE

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

2013-01-01

With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. T...

TU-F-17A-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - An Automatic Toolkit for Efficient and Robust Analysis of 4D Respiratory Motion

International Nuclear Information System (INIS)

Wei, J; Yuan, A; Li, G

2014-01-01

Purpose: To provide an automatic image analysis toolkit to process thoracic 4-dimensional computed tomography (4DCT) and extract patient-specific motion information to facilitate investigational or clinical use of 4DCT. Methods: We developed an automatic toolkit in MATLAB to overcome the extra workload from the time dimension in 4DCT. This toolkit employs image/signal processing, computer vision, and machine learning methods to visualize, segment, register, and characterize lung 4DCT automatically or interactively. A fully-automated 3D lung segmentation algorithm was designed and 4D lung segmentation was achieved in batch mode. Voxel counting was used to calculate volume variations of the torso, lung and its air component, and local volume changes at the diaphragm and chest wall to characterize breathing pattern. Segmented lung volumes in 12 patients are compared with those from a treatment planning system (TPS). Voxel conversion was introduced from CT# to other physical parameters, such as gravity-induced pressure, to create a secondary 4D image. A demon algorithm was applied in deformable image registration and motion trajectories were extracted automatically. Calculated motion parameters were plotted with various templates. Machine learning algorithms, such as Naive Bayes and random forests, were implemented to study respiratory motion. This toolkit is complementary to and will be integrated with the Computational Environment for Radiotherapy Research (CERR). Results: The automatic 4D image/data processing toolkit provides a platform for analysis of 4D images and datasets. It processes 4D data automatically in batch mode and provides interactive visual verification for manual adjustments. The discrepancy in lung volume calculation between this and the TPS is <±2% and the time saving is by 1–2 orders of magnitude. Conclusion: A framework of 4D toolkit has been developed to analyze thoracic 4DCT automatically or interactively, facilitating both investigational

TU-F-17A-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - An Automatic Toolkit for Efficient and Robust Analysis of 4D Respiratory Motion

Energy Technology Data Exchange (ETDEWEB)

Wei, J [City College of New York, New York, NY (United States); Yuan, A; Li, G [Memorial Sloan Kettering Cancer Center, New York, NY (United States)

2014-06-15

Purpose: To provide an automatic image analysis toolkit to process thoracic 4-dimensional computed tomography (4DCT) and extract patient-specific motion information to facilitate investigational or clinical use of 4DCT. Methods: We developed an automatic toolkit in MATLAB to overcome the extra workload from the time dimension in 4DCT. This toolkit employs image/signal processing, computer vision, and machine learning methods to visualize, segment, register, and characterize lung 4DCT automatically or interactively. A fully-automated 3D lung segmentation algorithm was designed and 4D lung segmentation was achieved in batch mode. Voxel counting was used to calculate volume variations of the torso, lung and its air component, and local volume changes at the diaphragm and chest wall to characterize breathing pattern. Segmented lung volumes in 12 patients are compared with those from a treatment planning system (TPS). Voxel conversion was introduced from CT# to other physical parameters, such as gravity-induced pressure, to create a secondary 4D image. A demon algorithm was applied in deformable image registration and motion trajectories were extracted automatically. Calculated motion parameters were plotted with various templates. Machine learning algorithms, such as Naive Bayes and random forests, were implemented to study respiratory motion. This toolkit is complementary to and will be integrated with the Computational Environment for Radiotherapy Research (CERR). Results: The automatic 4D image/data processing toolkit provides a platform for analysis of 4D images and datasets. It processes 4D data automatically in batch mode and provides interactive visual verification for manual adjustments. The discrepancy in lung volume calculation between this and the TPS is <±2% and the time saving is by 1–2 orders of magnitude. Conclusion: A framework of 4D toolkit has been developed to analyze thoracic 4DCT automatically or interactively, facilitating both investigational

Automatic telangiectasia analysis in dermoscopy images using adaptive critic design.

Science.gov (United States)

Cheng, B; Stanley, R J; Stoecker, W V; Hinton, K

2012-11-01

Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs. A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply action-dependent heuristic dynamic programming (ADHDP) for discrimination based on computed features using different skin lesion contrast variations to promote the discrimination process. Lesion discrimination results for ADHDP are compared with multilayer perception backpropagation artificial neural networks. This study uses a data set of 498 dermoscopy skin lesion images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the ADHDP approach, providing an 8.03% improvement over a standard multilayer perception method. We have chosen BCC detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, BCC detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research. © 2011 John Wiley & Sons A/S.

Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections

International Nuclear Information System (INIS)

Lippolis, Giuseppe; Edsjö, Anders; Helczynski, Leszek; Bjartell, Anders; Overgaard, Niels Chr

2013-01-01

Prostate cancer is one of the leading causes of cancer related deaths. For diagnosis, predicting the outcome of the disease, and for assessing potential new biomarkers, pathologists and researchers routinely analyze histological samples. Morphological and molecular information may be integrated by aligning microscopic histological images in a multiplex fashion. This process is usually time-consuming and results in intra- and inter-user variability. The aim of this study is to investigate the feasibility of using modern image analysis methods for automated alignment of microscopic images from differently stained adjacent paraffin sections from prostatic tissue specimens. Tissue samples, obtained from biopsy or radical prostatectomy, were sectioned and stained with either hematoxylin & eosin (H&E), immunohistochemistry for p63 and AMACR or Time Resolved Fluorescence (TRF) for androgen receptor (AR). Image pairs were aligned allowing for translation, rotation and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale invariant image transform (SIFT), followed by the well-known RANSAC protocol for finding point correspondences and finally aligned by Procrustes fit. The Registration results were evaluated using both visual and quantitative criteria as defined in the text. Three experiments were carried out. First, images of consecutive tissue sections stained with H&E and p63/AMACR were successfully aligned in 85 of 88 cases (96.6%). The failures occurred in 3 out of 13 cores with highly aggressive cancer (Gleason score ≥ 8). Second, TRF and H&E image pairs were aligned correctly in 103 out of 106 cases (97%). The third experiment considered the alignment of image pairs with the same staining (H&E) coming from a stack of 4 sections. The success rate for alignment dropped from 93.8% in adjacent sections to 22% for sections furthest away. The proposed method is both reliable and fast and therefore well suited

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels.

Science.gov (United States)

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Automatic tissue image segmentation based on image processing and deep learning

Science.gov (United States)

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

2018-02-01

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

Automatic Generation of Algorithms for the Statistical Analysis of Planetary Nebulae Images

Science.gov (United States)

Fischer, Bernd

2004-01-01

which use numerical approximations even in cases where closed-form solutions exist. AutoBayes is implemented in Prolog and comprises approximately 75.000 lines of code. In this paper, we take one typical scientific data analysis problem-analyzing planetary nebulae images taken by the Hubble Space Telescope-and show how AutoBayes can be used to automate the implementation of the necessary anal- ysis programs. We initially follow the analysis described by Knuth and Hajian [KHO2] and use AutoBayes to derive code for the published models. We show the details of the code derivation process, including the symbolic computations and automatic integration of library procedures, and compare the results of the automatically generated and manually implemented code. We then go beyond the original analysis and use AutoBayes to derive code for a simple image segmentation procedure based on a mixture model which can be used to automate a manual preproceesing step. Finally, we combine the original approach with the simple segmentation which yields a more detailed analysis. This also demonstrates that AutoBayes makes it easy to combine different aspects of data analysis.

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA. Part 1: Introduction

Directory of Open Access Journals (Sweden)

Andrea Baraldi

2012-09-01

Full Text Available According to existing literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA systems and three-stage iterative geographic object-oriented image analysis (GEOOIA systems, where GEOOIA/GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the degree of automation, accuracy, efficiency, robustness, scalability and timeliness of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO guidelines, this methodological work is split into two parts. The present first paper provides a multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT analysis of the GEOBIA/GEOOIA approaches that augments similar analyses proposed in recent years. In line with constraints stemming from human vision, this SWOT analysis promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS image understanding system (RS-IUS, from sub-symbolic statistical model-based (inductive image segmentation to symbolic physical model-based (deductive image preliminary classification. Hence, a symbolic deductive pre-attentive vision first stage accomplishes image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the second part of this work a novel hybrid (combined deductive and inductive RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a computational theory (system design; (b information/knowledge representation; (c algorithm design; and (d implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time Satellite Image Automatic Mapper™ (SIAM™ is selected from existing literature. To the best of these authors’ knowledge, this is the first time a

Fractal Analysis of Elastographic Images for Automatic Detection of Diffuse Diseases of Salivary Glands: Preliminary Results

Directory of Open Access Journals (Sweden)

Alexandru Florin Badea

2013-01-01

Full Text Available The geometry of some medical images of tissues, obtained by elastography and ultrasonography, is characterized in terms of complexity parameters such as the fractal dimension (FD. It is well known that in any image there are very subtle details that are not easily detectable by the human eye. However, in many cases like medical imaging diagnosis, these details are very important since they might contain some hidden information about the possible existence of certain pathological lesions like tissue degeneration, inflammation, or tumors. Therefore, an automatic method of analysis could be an expedient tool for physicians to give a faultless diagnosis. The fractal analysis is of great importance in relation to a quantitative evaluation of “real-time” elastography, a procedure considered to be operator dependent in the current clinical practice. Mathematical analysis reveals significant discrepancies among normal and pathological image patterns. The main objective of our work is to demonstrate the clinical utility of this procedure on an ultrasound image corresponding to a submandibular diffuse pathology.

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

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

2013-03-13

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

Coherence measures in automatic time-migration velocity analysis

International Nuclear Information System (INIS)

Maciel, Jonathas S; Costa, Jessé C; Schleicher, Jörg

2012-01-01

Time-migration velocity analysis can be carried out automatically by evaluating the coherence of migrated seismic events in common-image gathers (CIGs). The performance of gradient methods for automatic time-migration velocity analysis depends on the coherence measures used as the objective function. We compare the results of four different coherence measures, being conventional semblance, differential semblance, an extended differential semblance using differences of more distant image traces and the product of the latter with conventional semblance. In our numerical experiments, the objective functions based on conventional semblance and on the product of conventional semblance with extended differential semblance provided the best velocity models, as evaluated by the flatness of the resulting CIGs. The method can be easily extended to anisotropic media. (paper)

Image-guided automatic triggering of a fractional CO2 laser in aesthetic procedures.

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Wilczyński, Sławomir; Koprowski, Robert; Wiernek, Barbara K; Błońska-Fajfrowska, Barbara

2016-09-01

Laser procedures in dermatology and aesthetic medicine are associated with the need for manual laser triggering. This leads to pulse overlapping and side effects. Automatic laser triggering based on image analysis can provide a secure fit to each successive doses of radiation. A fractional CO2 laser was used in the study. 500 images of the human skin of healthy subjects were acquired. Automatic triggering was initiated by an application together with a camera which tracks and analyses the skin in visible light. The tracking algorithm uses the methods of image analysis to overlap images. After locating the characteristic points in analysed adjacent areas, the correspondence of graphs is found. The point coordinates derived from the images are the vertices of graphs with respect to which isomorphism is sought. When the correspondence of graphs is found, it is possible to overlap the neighbouring parts of the image. The proposed method of laser triggering owing to the automatic image fitting method allows for 100% repeatability. To meet this requirement, there must be at least 13 graph vertices obtained from the image. For this number of vertices, the time of analysis of a single image is less than 0.5s. The proposed method, applied in practice, may help reduce the number of side effects during dermatological laser procedures resulting from laser pulse overlapping. In addition, it reduces treatment time and enables to propose new techniques of treatment through controlled, precise laser pulse overlapping. Copyright © 2016 Elsevier Ltd. All rights reserved.

ACIR: automatic cochlea image registration

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Al-Dhamari, Ibraheem; Bauer, Sabine; Paulus, Dietrich; Lissek, Friedrich; Jacob, Roland

2017-02-01

Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea's size and its characteristics. This information helps to select suitable implants for different patients. To get these measurements, a segmentation method of cochlea medical images is needed. An important pre-processing step for good cochlea segmentation involves efficient image registration. The cochlea's small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. In this paper, an Automatic Cochlea Image Registration (ACIR) method for multi- modal human cochlea images is proposed. This method is based on using small areas that have clear structures from both input images instead of registering the complete image. It uses the Adaptive Stochastic Gradient Descent Optimizer (ASGD) and Mattes's Mutual Information metric (MMI) to estimate 3D rigid transform parameters. The use of state of the art medical image registration optimizers published over the last two years are studied and compared quantitatively using the standard Dice Similarity Coefficient (DSC). ACIR requires only 4.86 seconds on average to align cochlea images automatically and to put all the modalities in the same spatial locations without human interference. The source code is based on the tool elastix and is provided for free as a 3D Slicer plugin. Another contribution of this work is a proposed public cochlea standard dataset which can be downloaded for free from a public XNAT server.

Image processing applied to automatic detection of defects during ultrasonic examination

International Nuclear Information System (INIS)

Moysan, J.

1992-10-01

This work is a study about image processing applied to ultrasonic BSCAN images which are obtained in the field of non destructive testing of weld. The goal is to define what image processing techniques can bring to ameliorate the exploitation of the data collected and, more precisely, what image processing can do to extract the meaningful echoes which enable to characterize and to size the defects. The report presents non destructive testing by ultrasounds in the nuclear field and it indicates specificities of the propagation of ultrasonic waves in austenitic weld. It gives a state of the art of the data processing applied to ultrasonic images in nondestructive evaluation. A new image analysis is then developed. It is based on a powerful tool, the co-occurrence matrix. This matrix enables to represent, in a whole representation, relations between amplitudes of couples of pixels. From the matrix analysis, a new complete and automatic method has been set down in order to define a threshold which separates echoes from noise. An automatic interpretation of the ultrasonic echoes is then possible. Complete validation has been done with standard pieces

Automatic alignment of radionuclide images

International Nuclear Information System (INIS)

Barber, D.C.

1982-01-01

The variability of the position, dimensions and orientation of a radionuclide image within the field of view of a gamma camera hampers attempts to analyse the image numerically. This paper describes a method of using a set of training images of a particular type, in this case right lateral brain images, to define the likely variations in the position, dimensions and orientation for that type of image and to provide alignment data for a program that automatically aligns new images of the specified type to a standard position, size and orientation. Examples are given of the use of this method on three types of radionuclide image. (author)

Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

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Lu, Kongkuo; Hall, Christopher S.

2014-03-01

Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

Automatic selection of resting-state networks with functional magnetic resonance imaging

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Silvia Francesca eStorti

2013-05-01

Full Text Available Functional magnetic resonance imaging (fMRI during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set. Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93% and precision (90%, 90%, 84%. The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥ 92% and the occipital network, which includes the medial visual cortical areas (≥ 94%, and consistent for the attention network (≥ 80%, the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥ 80%. The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA

Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

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Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L

2010-07-01

The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used

Automatic emotional expression analysis from eye area

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Akkoç, Betül; Arslan, Ahmet

2015-02-01

Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.

Some results of automatic processing of images

International Nuclear Information System (INIS)

Golenishchev, I.A.; Gracheva, T.N.; Khardikov, S.V.

1975-01-01

The problems of automatic deciphering of the radiographic picture the purpose of which is making a conclusion concerning the quality of the inspected product on the basis of the product defect images in the picture are considered. The methods of defect image recognition are listed, and the algorithms and the class features of defects are described. The results of deciphering of a small radiographic picture by means of the ''Minsk-22'' computer are presented. It is established that the sensitivity of the method of the automatic deciphering is close to that obtained for visual deciphering

Automatic detection of blurred images in UAV image sets

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Sieberth, Till; Wackrow, Rene; Chandler, Jim H.

2016-12-01

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. Images with known blur are processed digitally to determine a quantifiable measure of image blur. The algorithm is required to process UAV images fast and reliably to relieve the operator from detecting blurred images manually. The newly developed method makes it possible to detect blur caused by linear camera displacement and is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of

Automatic slice identification in 3D medical images with a ConvNet regressor

NARCIS (Netherlands)

de Vos, Bob D.; Viergever, Max A.; de Jong, Pim A.; Išgum, Ivana

2016-01-01

Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were

Markov random field based automatic image alignment for electron tomography.

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

2008-03-01

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

Automatic multiresolution age-related macular degeneration detection from fundus images

Science.gov (United States)

Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

2014-03-01

Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

MatchGUI: A Graphical MATLAB-Based Tool for Automatic Image Co-Registration

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Ansar, Adnan I.

2011-01-01

MatchGUI software, based on MATLAB, automatically matches two images and displays the match result by superimposing one image on the other. A slider bar allows focus to shift between the two images. There are tools for zoom, auto-crop to overlap region, and basic image markup. Given a pair of ortho-rectified images (focused primarily on Mars orbital imagery for now), this software automatically co-registers the imagery so that corresponding image pixels are aligned. MatchGUI requires minimal user input, and performs a registration over scale and inplane rotation fully automatically

SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

International Nuclear Information System (INIS)

Qiu, J; Li, H. Harlod; Zhang, T; Yang, D; Ma, F

2015-01-01

Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools

SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

Energy Technology Data Exchange (ETDEWEB)

Qiu, J [Taishan Medical University, Taian, Shandong (China); Washington University in St Louis, St Louis, MO (United States); Li, H. Harlod; Zhang, T; Yang, D [Washington University in St Louis, St Louis, MO (United States); Ma, F [Taishan Medical University, Taian, Shandong (China)

2015-06-15

Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools.

Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement

Directory of Open Access Journals (Sweden)

P. Jagatheeswari

2014-01-01

Full Text Available The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.

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

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

2017-11-01

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

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

Science.gov (United States)

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

2018-04-01

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

ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying.

Science.gov (United States)

Nguyen, Hoai Nam; Paveau, Vincent; Cauchois, Cyril; Kervrann, Charles

2018-04-19

Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. We

Research of x-ray automatic image mosaic method

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Liu, Bin; Chen, Shunan; Guo, Lianpeng; Xu, Wanpeng

2013-10-01

Image mosaic has widely applications value in the fields of medical image analysis, and it is a technology that carries on the spatial matching to a series of image which are overlapped with each other, and finally builds a seamless and high quality image which has high resolution and big eyeshot. In this paper, the method of grayscale cutting pseudo-color enhancement was firstly used to complete the mapping transformation from gray to the pseudo-color, and to extract SIFT features from the images. And then by making use of a similar measure of NCC (normalized cross correlation - Normalized cross-correlation), the method of RANSAC (Random Sample Consensus) was used to exclude the pseudofeature points right in order to complete the exact match of feature points. Finally, seamless mosaic and color fusion were completed by using wavelet multi-decomposition. The experiment shows that the method we used can effectively improve the precision and automation of the medical image mosaic, and provide an effective technical approach for automatic medical image mosaic.

Automatic discrimination of fine roots in minirhizotron images.

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Zeng, Guang; Birchfield, Stanley T; Wells, Christina E

2008-01-01

Minirhizotrons provide detailed information on the production, life history and mortality of fine roots. However, manual processing of minirhizotron images is time-consuming, limiting the number and size of experiments that can reasonably be analysed. Previously, an algorithm was developed to automatically detect and measure individual roots in minirhizotron images. Here, species-specific root classifiers were developed to discriminate detected roots from bright background artifacts. Classifiers were developed from training images of peach (Prunus persica), freeman maple (Acer x freemanii) and sweetbay magnolia (Magnolia virginiana) using the Adaboost algorithm. True- and false-positive rates for classifiers were estimated using receiver operating characteristic curves. Classifiers gave true positive rates of 89-94% and false positive rates of 3-7% when applied to nontraining images of the species for which they were developed. The application of a classifier trained on one species to images from another species resulted in little or no reduction in accuracy. These results suggest that a single root classifier can be used to distinguish roots from background objects across multiple minirhizotron experiments. By incorporating root detection and discrimination algorithms into an open-source minirhizotron image analysis application, many analysis tasks that are currently performed by hand can be automated.

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

Science.gov (United States)

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

2018-03-01

The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal

Bio-EdIP: An automatic approach for in vitro cell confluence images quantification.

Science.gov (United States)

Cardona, Andrés; Ariza-Jiménez, Leandro; Uribe, Diego; Arroyave, Johanna C; Galeano, July; Cortés-Mancera, Fabian M

2017-07-01

Cell imaging is a widely-employed technique to analyze multiple biological processes. Therefore, simple, accurate and quantitative tools are needed to understand cellular events. For this purpose, Bio-EdIP was developed as a user-friendly tool to quantify confluence levels using cell culture images. The proposed algorithm combines a pre-processing step with subsequent stages that involve local processing techniques and a morphological reconstruction-based segmentation algorithm. Segmentation performance was assessed in three constructed image sets, comparing F-measure scores and AUC values (ROC analysis) for Bio-EdIP, its previous version and TScratch. Furthermore, segmentation results were compared with published algorithms using eight public benchmarks. Bio-EdIP automatically segmented cell-free regions from images of in vitro cell culture. Based on mean F-measure scores and ROC analysis, Bio-EdIP conserved a high performance regardless of image characteristics of the constructed dataset, when compared with its previous version and TScratch. Although acquisition quality of the public dataset affected Bio-EdIP segmentation, performance was better in two out of eight public sets. Bio-EdIP is a user-friendly interface, which is useful for the automatic analysis of confluence levels and cell growth processes using in vitro cell culture images. Here, we also presented new manually annotated data for algorithms evaluation. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

Science.gov (United States)

Freitas, Nuno R.; Vieira, Pedro M.; Lima, Estevão; Lima, Carlos S.

2018-02-01

Correct classification of cystoscopy images depends on the interpreter’s experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

Directory of Open Access Journals (Sweden)

Yehu Shen

2014-01-01

Full Text Available Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.

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

Science.gov (United States)

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

2013-03-01

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

Automatic extraction of soft tissues from 3D MRI head images using model driven analysis

International Nuclear Information System (INIS)

Jiang, Hao; Yamamoto, Shinji; Imao, Masanao.

1995-01-01

This paper presents an automatic extraction system (called TOPS-3D : Top Down Parallel Pattern Recognition System for 3D Images) of soft tissues from 3D MRI head images by using model driven analysis algorithm. As the construction of system TOPS we developed, two concepts have been considered in the design of system TOPS-3D. One is the system having a hierarchical structure of reasoning using model information in higher level, and the other is a parallel image processing structure used to extract plural candidate regions for a destination entity. The new points of system TOPS-3D are as follows. (1) The TOPS-3D is a three-dimensional image analysis system including 3D model construction and 3D image processing techniques. (2) A technique is proposed to increase connectivity between knowledge processing in higher level and image processing in lower level. The technique is realized by applying opening operation of mathematical morphology, in which a structural model function defined in higher level by knowledge representation is immediately used to the filter function of opening operation as image processing in lower level. The system TOPS-3D applied to 3D MRI head images consists of three levels. First and second levels are reasoning part, and third level is image processing part. In experiments, we applied 5 samples of 3D MRI head images with size 128 x 128 x 128 pixels to the system TOPS-3D to extract the regions of soft tissues such as cerebrum, cerebellum and brain stem. From the experimental results, the system is robust for variation of input data by using model information, and the position and shape of soft tissues are extracted corresponding to anatomical structure. (author)

Automatic DNA Diagnosis for 1D Gel Electrophoresis Images using Bio-image Processing Technique.

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Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Shaw, Philip J; Ukosakit, Kittipat; Tragoonrung, Somvong; Tongsima, Sissades

2015-01-01

DNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained. We developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists. This work presents an automated genotyping tool from DNA

Automatic DNA Diagnosis for 1D Gel Electrophoresis Images using Bio-image Processing Technique

Science.gov (United States)

2015-01-01

Background DNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained. Results We developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists. Conclusions This work presents an

Automatic Detection of Vehicles Using Intensity Laser and Anaglyph Image

Directory of Open Access Journals (Sweden)

Hideo Araki

2006-12-01

Full Text Available In this work is presented a methodology to automatic car detection motion presents in digital aerial image on urban area using intensity, anaglyph and subtracting images. The anaglyph image is used to identify the motion cars on the expose take, because the cars provide red color due the not homology between objects. An implicit model was developed to provide a digital pixel value that has the specific propriety presented early, using the ratio between the RGB color of car object in the anaglyph image. The intensity image is used to decrease the false positive and to do the processing to work into roads and streets. The subtracting image is applied to decrease the false positives obtained due the markings road. The goal of this paper is automatically detect motion cars presents in digital aerial image in urban areas. The algorithm implemented applies normalization on the left and right images and later form the anaglyph with using the translation. The results show the applicability of proposed method and it potentiality on the automatic car detection and presented the performance of proposed methodology.

Automatic Image Alignment and Stitching of Medical Images with Seam Blending

OpenAIRE

Abhinav Kumar; Raja Sekhar Bandaru; B Madhusudan Rao; Saket Kulkarni; Nilesh Ghatpande

2010-01-01

This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together usin...

AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

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Liang Tang

2011-05-01

Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

Automatic measurement of cusps in 2.5D dental images

Science.gov (United States)

Wolf, Mattias; Paulus, Dietrich W.; Niemann, Heinrich

1996-01-01

Automatic reconstruction of occlusal surfaces of teeth is an application which might become more and more urgent due to the toxicity of amalgam. Modern dental chairside equipment is currently restricted to the production of inlays. The automatic reconstruction of the occlusal surface is presently not possible. For manufacturing an occlusal surface it is required to extract features from which it is possible to reconstruct destroyed teeth. In this paper, we demonstrate how intact upper molars can be automatically extracted in dental range and intensity images. After normalization of the 3D location, the sizes of the cusps are detected and the distances between them are calculated. In the presented approach, the detection of the upper molar is based on a knowledge-based segmentation which includes anatomic knowledge. After the segmentation of the interesting tooth the central fossa is calculated. The normalization of the spatial location is archieved by aligning the detected fossa with a reference axis. After searching the cusp tips in the range image the image is resized. The methods have been successfully tested on 60 images. The results have been compared with the results of a dentist's evaluation on a sample of 20 images. The results will be further used for automatic production of tooth inlays.

Evaluation of automatic image quality assessment in chest CT - A human cadaver study.

Science.gov (United States)

Franck, Caro; De Crop, An; De Roo, Bieke; Smeets, Peter; Vergauwen, Merel; Dewaele, Tom; Van Borsel, Mathias; Achten, Eric; Van Hoof, Tom; Bacher, Klaus

2017-04-01

The evaluation of clinical image quality (IQ) is important to optimize CT protocols and to keep patient doses as low as reasonably achievable. Considering the significant amount of effort needed for human observer studies, automatic IQ tools are a promising alternative. The purpose of this study was to evaluate automatic IQ assessment in chest CT using Thiel embalmed cadavers. Chest CT's of Thiel embalmed cadavers were acquired at different exposures. Clinical IQ was determined by performing a visual grading analysis. Physical-technical IQ (noise, contrast-to-noise and contrast-detail) was assessed in a Catphan phantom. Soft and sharp reconstructions were made with filtered back projection and two strengths of iterative reconstruction. In addition to the classical IQ metrics, an automatic algorithm was used to calculate image quality scores (IQs). To be able to compare datasets reconstructed with different kernels, the IQs values were normalized. Good correlations were found between IQs and the measured physical-technical image quality: noise (ρ=-1.00), contrast-to-noise (ρ=1.00) and contrast-detail (ρ=0.96). The correlation coefficients between IQs and the observed clinical image quality of soft and sharp reconstructions were 0.88 and 0.93, respectively. The automatic scoring algorithm is a promising tool for the evaluation of thoracic CT scans in daily clinical practice. It allows monitoring of the image quality of a chest protocol over time, without human intervention. Different reconstruction kernels can be compared after normalization of the IQs. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Automatic Blastomere Recognition from a Single Embryo Image

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Yun Tian

2014-01-01

Full Text Available The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.

Occupancy Analysis of Sports Arenas Using Thermal Imaging

DEFF Research Database (Denmark)

Gade, Rikke; Jørgensen, Anders; Moeslund, Thomas B.

2012-01-01

This paper presents a system for automatic analysis of the occupancy of sports arenas. By using a thermal camera for image capturing the number of persons and their location on the court are found without violating any privacy issues. The images are binarised with an automatic threshold method...

Development of automatic extraction method of left ventricular contours on long axis view MR cine images

International Nuclear Information System (INIS)

Utsunomiya, Shinichi; Iijima, Naoto; Yamasaki, Kazunari; Fujita, Akinori

1995-01-01

In the MRI cardiac function analysis, left ventricular volume curves and diagnosis parameters are obtained by extracting the left ventricular cavities as regions of interest (ROI) from long axis view MR cine images. The ROI extractions had to be done by manual operations, because automatization of the extraction is difficult. A long axis view left ventricular contour consists of a cardiac wall part and an aortic valve part. The above mentioned difficulty is due to the decline of contrast on the cardiac wall part, and the disappearance of edge on the aortic valve part. In this paper, we report a new automatic extraction method for long axis view MR cine images, which needs only 3 manually indicated points on the 1st image to extract all the contours from the total sequence of images. At first, candidate points of a contour are detected by edge detection. Then, selecting the best matched combination of candidate points by Dynamic Programming, the cardiac wall part is automatically extracted. The aortic valve part is manually extracted for the 1st image by indicating both the end points, and is automatically extracted for the rest of the images, by utilizing the aortic valve motion characteristics throughout a cardiac cycle. (author)

Automatic Methods in Image Processing and Their Relevance to Map-Making.

Science.gov (United States)

1981-02-11

folding fre- quency = .5) and s is the "shaoing fac- tor" which controls the spatial frequency content of the signal; the signal band- width increases...ARIZONA UNIV TUCSON DIGITAL IAgE ANALYSIS LAB Iris 8/ 2AUTOMATIC METHOOS IN IMAGE PROCESSING AND THEIR RELEVANCE TO MA-.ETC~tl;FEB 1 S R HUNT DAA629

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

Science.gov (United States)

Perez-Sanz, Fernando; Navarro, Pedro J; Egea-Cortines, Marcos

2017-11-01

The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion. © The Author 2017. Published by Oxford University Press.

Automatic 2D segmentation of airways in thorax computed tomography images

International Nuclear Information System (INIS)

Cavalcante, Tarique da Silveira; Cortez, Paulo Cesar; Almeida, Thomaz Maia de; Felix, John Hebert da Silva; Holanda, Marcelo Alcantara

2013-01-01

Introduction: much of the world population is affected by pulmonary diseases, such as the bronchial asthma, bronchitis and bronchiectasis. The bronchial diagnosis is based on the airways state. In this sense, the automatic segmentation of the airways in Computed Tomography (CT) scans is a critical step in the aid to diagnosis of these diseases. Methods: this paper evaluates algorithms for airway automatic segmentation, using Neural Network Multilayer Perceptron (MLP) and Lung Densities Analysis (LDA) for detecting airways, along with Region Growing (RG), Active Contour Method (ACM) Balloon and Topology Adaptive to segment them. Results: we obtained results in three stages: comparative analysis of the detection algorithms MLP and LDA, with a gold standard acquired by three physicians with expertise in CT imaging of the chest; comparative analysis of segmentation algorithms ACM Balloon, ACM Topology Adaptive, MLP and RG; and evaluation of possible combinations between segmentation and detection algorithms, resulting in the complete method for automatic segmentation of the airways in 2D. Conclusion: the low incidence of false negative and the significant reduction of false positive, results in similarity coefficient and sensitivity exceeding 91% and 87% respectively, for a combination of algorithms with satisfactory segmentation quality. (author)

Towards an automatic tool for resolution evaluation of mammographic images

Energy Technology Data Exchange (ETDEWEB)

De Oliveira, J. E. E. [FUMEC, Av. Alfonso Pena 3880, CEP 30130-009 Belo Horizonte - MG (Brazil); Nogueira, M. S., E-mail: juliae@fumec.br [Centro de Desenvolvimento da Tecnologia Nuclear / CNEN, Pte. Antonio Carlos 6627, 31270-901, Belo Horizonte - MG (Brazil)

2014-08-15

Medical images are important for diagnosis purposes as they are related to patients medical history and pathology. Breast cancer represents a leading cause of death among women worldwide, and its early detection is the most effective method of reducing mortality. In a way to identify small structures with low density differences, a high image quality is required with the use of low doses of radiation. The analysis of the quality of the obtained image from a mammogram is performed from an image of a simulated breast and this is a fundamental key point for a program of quality control of mammography equipment s. In a control program of mammographic equipment s, besides the analysis of the quality of mammographic images, each element of the chain which composes the formation of the image is also analyzed: X-rays equipment s, radiographic films, and operating conditions. This control allows that an effective and efficient exam can be provided to the population and is within the standards of quality required for the early detection of breast cancer. However, according to the State Program of Quality Control in Mammography of Minas Gerais, Brazil, only 40% of the mammographies have provided a simulated image with a minimum level of quality, thus reinforcing the need for monitoring the images. The reduction of the morbidity and mortality indexes, with optimization and assurance of access to diagnosis and breast cancer treatment in the state of Minas Gerais, Brazil, may be the result of a mammographic exam which has a final image with good quality and which automatic evaluation is not subjective. The reason is that one has to consider the hypothesis that humans are subjective when performing the image analysis and that the evaluation of the image can be executed by a computer with objectivity. In 2007, in order to maintain the standard quality needed to mammography, the State Health Secretariat of Minas Gerais, Brazil, established a Program of Monthly Monitoring the

Towards an automatic tool for resolution evaluation of mammographic images

International Nuclear Information System (INIS)

De Oliveira, J. E. E.; Nogueira, M. S.

2014-08-01

Medical images are important for diagnosis purposes as they are related to patients medical history and pathology. Breast cancer represents a leading cause of death among women worldwide, and its early detection is the most effective method of reducing mortality. In a way to identify small structures with low density differences, a high image quality is required with the use of low doses of radiation. The analysis of the quality of the obtained image from a mammogram is performed from an image of a simulated breast and this is a fundamental key point for a program of quality control of mammography equipment s. In a control program of mammographic equipment s, besides the analysis of the quality of mammographic images, each element of the chain which composes the formation of the image is also analyzed: X-rays equipment s, radiographic films, and operating conditions. This control allows that an effective and efficient exam can be provided to the population and is within the standards of quality required for the early detection of breast cancer. However, according to the State Program of Quality Control in Mammography of Minas Gerais, Brazil, only 40% of the mammographies have provided a simulated image with a minimum level of quality, thus reinforcing the need for monitoring the images. The reduction of the morbidity and mortality indexes, with optimization and assurance of access to diagnosis and breast cancer treatment in the state of Minas Gerais, Brazil, may be the result of a mammographic exam which has a final image with good quality and which automatic evaluation is not subjective. The reason is that one has to consider the hypothesis that humans are subjective when performing the image analysis and that the evaluation of the image can be executed by a computer with objectivity. In 2007, in order to maintain the standard quality needed to mammography, the State Health Secretariat of Minas Gerais, Brazil, established a Program of Monthly Monitoring the

Automatic segmentation of liver structure in CT images

International Nuclear Information System (INIS)

Bae, K.T.; Giger, M.L.; Chen, C.; Kahn, C.E. Jr.

1993-01-01

The segmentation and three-dimensional representation of the liver from a computed tomography (CT) scan is an important step in many medical applications, such as in the surgical planning for a living-donor liver transplant and in the automatic detection and documentation of pathological states. A method is being developed to automatically extract liver structure from abdominal CT scans using a priori information about liver morphology and digital image-processing techniques. Segmentation is performed sequentially image-by-image (slice-by-slice), starting with a reference image in which the liver occupies almost the entire right half of the abdomen cross section. Image processing techniques include gray-level thresholding, Gaussian smoothing, and eight-point connectivity tracking. For each case, the shape, size, and pixel density distribution of the liver are recorded for each CT image and used in the processing of other CT images. Extracted boundaries of the liver are smoothed using mathematical morphology techniques and B-splines. Computer-determined boundaries were compared with those drawn by a radiologist. The boundary descriptions from the two methods were in agreement, and the calculated areas were within 10%

Automatic terrain modeling using transfinite element analysis

KAUST Repository

Collier, Nathan; Calo, Victor M.

2010-01-01

An automatic procedure for modeling terrain is developed based on L2 projection-based interpolation of discrete terrain data onto transfinite function spaces. The function space is refined automatically by the use of image processing techniques

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA. Part 2: Novel system Architecture, Information/Knowledge Representation, Algorithm Design and Implementation

Directory of Open Access Journals (Sweden)

Luigi Boschetti

2012-09-01

Full Text Available According to literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA systems and three-stage iterative geographic object-oriented image analysis (GEOOIA systems, where GEOOIA/GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the Quality Indexes of Operativeness (OQIs of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO guidelines, this methodological work is split into two parts. Based on an original multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT analysis of the GEOBIA/GEOOIA approaches, the first part of this work promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS image understanding system (RS-IUS, from sub-symbolic statistical model-based (inductive image segmentation to symbolic physical model-based (deductive image preliminary classification capable of accomplishing image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the present second part of this work, a novel hybrid (combined deductive and inductive RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a computational theory (system design, (b information/knowledge representation, (c algorithm design and (d implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time, multi-sensor, multi-resolution, application-independent Satellite Image Automatic Mapper™ (SIAM™ is selected from existing literature. To the best of these authors’ knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage

Automatic macroscopic characterization of diesel sprays by means of a new image processing algorithm

Science.gov (United States)

Rubio-Gómez, Guillermo; Martínez-Martínez, S.; Rua-Mojica, Luis F.; Gómez-Gordo, Pablo; de la Garza, Oscar A.

2018-05-01

A novel algorithm is proposed for the automatic segmentation of diesel spray images and the calculation of their macroscopic parameters. The algorithm automatically detects each spray present in an image, and therefore it is able to work with diesel injectors with a different number of nozzle holes without any modification. The main characteristic of the algorithm is that it splits each spray into three different regions and then segments each one with an individually calculated binarization threshold. Each threshold level is calculated from the analysis of a representative luminosity profile of each region. This approach makes it robust to irregular light distribution along a single spray and between different sprays of an image. Once the sprays are segmented, the macroscopic parameters of each one are calculated. The algorithm is tested with two sets of diesel spray images taken under normal and irregular illumination setups.

Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

International Nuclear Information System (INIS)

Zhang Tiezhi; Chi Yuwei; Meldolesi, Elisa; Yan Di

2007-01-01

Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy. Methods and Materials: On-line adaptive radiotherapy requires a robust and automatic image segmentation method to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentation method to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32 daily computed tomography images from 7 head-and-neck patients were delineated using this automatic image segmentation method. Manually drawn contours on the daily images were used as references in the evaluation of automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similarity coefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs; and (2) the distance transformation, which yields the distances between the manually and automatically delineated ROI surfaces. Results: Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similarity coefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed that the distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. The distances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs. Conclusion: With atlas-based image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments

Automatic measurement of images on astrometric plates

Science.gov (United States)

Ortiz Gil, A.; Lopez Garcia, A.; Martinez Gonzalez, J. M.; Yershov, V.

1994-04-01

We present some results on the process of automatic detection and measurement of objects in overlapped fields of astrometric plates. The main steps of our algorithm are the following: determination of the Scale and Tilt between charge coupled devices (CCD) and microscope coordinate systems and estimation of signal-to-noise ratio in each field;--image identification and improvement of its position and size;--image final centering;--image selection and storage. Several parameters allow the use of variable criteria for image identification, characterization and selection. Problems related with faint images and crowded fields will be approached by special techniques (morphological filters, histogram properties and fitting models).

Automatic multimodal real-time tracking for image plane alignment in interventional Magnetic Resonance Imaging

International Nuclear Information System (INIS)

Neumann, Markus

2014-01-01

Interventional magnetic resonance imaging (MRI) aims at performing minimally invasive percutaneous interventions, such as tumor ablations and biopsies, under MRI guidance. During such interventions, the acquired MR image planes are typically aligned to the surgical instrument (needle) axis and to surrounding anatomical structures of interest in order to efficiently monitor the advancement in real-time of the instrument inside the patient's body. Object tracking inside the MRI is expected to facilitate and accelerate MR-guided interventions by allowing to automatically align the image planes to the surgical instrument. In this PhD thesis, an image-based work-flow is proposed and refined for automatic image plane alignment. An automatic tracking work-flow was developed, performing detection and tracking of a passive marker directly in clinical real-time images. This tracking work-flow is designed for fully automated image plane alignment, with minimization of tracking-dedicated time. Its main drawback is its inherent dependence on the slow clinical MRI update rate. First, the addition of motion estimation and prediction with a Kalman filter was investigated and improved the work-flow tracking performance. Second, a complementary optical sensor was used for multi-sensor tracking in order to decouple the tracking update rate from the MR image acquisition rate. Performance of the work-flow was evaluated with both computer simulations and experiments using an MR compatible test bed. Results show a high robustness of the multi-sensor tracking approach for dynamic image plane alignment, due to the combination of the individual strengths of each sensor. (author)

Reliable clarity automatic-evaluation method for optical remote sensing images

Science.gov (United States)

Qin, Bangyong; Shang, Ren; Li, Shengyang; Hei, Baoqin; Liu, Zhiwen

2015-10-01

Image clarity, which reflects the sharpness degree at the edge of objects in images, is an important quality evaluate index for optical remote sensing images. Scholars at home and abroad have done a lot of work on estimation of image clarity. At present, common clarity-estimation methods for digital images mainly include frequency-domain function methods, statistical parametric methods, gradient function methods and edge acutance methods. Frequency-domain function method is an accurate clarity-measure approach. However, its calculation process is complicate and cannot be carried out automatically. Statistical parametric methods and gradient function methods are both sensitive to clarity of images, while their results are easy to be affected by the complex degree of images. Edge acutance method is an effective approach for clarity estimate, while it needs picking out the edges manually. Due to the limits in accuracy, consistent or automation, these existing methods are not applicable to quality evaluation of optical remote sensing images. In this article, a new clarity-evaluation method, which is based on the principle of edge acutance algorithm, is proposed. In the new method, edge detection algorithm and gradient search algorithm are adopted to automatically search the object edges in images. Moreover, The calculation algorithm for edge sharpness has been improved. The new method has been tested with several groups of optical remote sensing images. Compared with the existing automatic evaluation methods, the new method perform better both in accuracy and consistency. Thus, the new method is an effective clarity evaluation method for optical remote sensing images.

AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

P. Kupidura

2016-06-01

Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

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

International Nuclear Information System (INIS)

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

1999-01-01

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

A fast and automatic mosaic method for high-resolution satellite images

Science.gov (United States)

Chen, Hongshun; He, Hui; Xiao, Hongyu; Huang, Jing

2015-12-01

We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.

Automatic coronary calcium scoring using noncontrast and contrast CT images

Energy Technology Data Exchange (ETDEWEB)

Yang, Guanyu, E-mail: yang.list@seu.edu.cn; Chen, Yang; Shu, Huazhong [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, No. 2, Si Pai Lou, Nanjing 210096 (China); Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096 (China); Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096 (China); Ning, Xiufang; Sun, Qiaoyu [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, No. 2, Si Pai Lou, Nanjing 210096 (China); Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096 (China); Coatrieux, Jean-Louis [INSERM-U1099, Rennes F-35000 (France); Labotatoire Traitement du Signal et de l’Image (LTSI), Université de Rennes 1, Campus de Beaulieu, Bat. 22, Rennes 35042 Cedex (France); Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096 (China)

2016-05-15

Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CT image is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries is difficult to be observed in the noncontrast CT images, the contrast CT image of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CT image according to the segmentation results in the contrast CT image. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible image noise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1–100, 101–300, >300) by comparing with the ground

Image-based automatic recognition of larvae

Science.gov (United States)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

Interconnecting smartphone, image analysis server, and case report forms in clinical trials for automatic skin lesion tracking in clinical trials

Science.gov (United States)

Haak, Daniel; Doma, Aliaa; Gombert, Alexander; Deserno, Thomas M.

2016-03-01

Today, subject's medical data in controlled clinical trials is captured digitally in electronic case report forms (eCRFs). However, eCRFs only insufficiently support integration of subject's image data, although medical imaging is looming large in studies today. For bed-side image integration, we present a mobile application (App) that utilizes the smartphone-integrated camera. To ensure high image quality with this inexpensive consumer hardware, color reference cards are placed in the camera's field of view next to the lesion. The cards are used for automatic calibration of geometry, color, and contrast. In addition, a personalized code is read from the cards that allows subject identification. For data integration, the App is connected to an communication and image analysis server that also holds the code-study-subject relation. In a second system interconnection, web services are used to connect the smartphone with OpenClinica, an open-source, Food and Drug Administration (FDA)-approved electronic data capture (EDC) system in clinical trials. Once the photographs have been securely stored on the server, they are released automatically from the mobile device. The workflow of the system is demonstrated by an ongoing clinical trial, in which photographic documentation is frequently performed to measure the effect of wound incision management systems. All 205 images, which have been collected in the study so far, have been correctly identified and successfully integrated into the corresponding subject's eCRF. Using this system, manual steps for the study personnel are reduced, and, therefore, errors, latency and costs decreased. Our approach also increases data security and privacy.

Real-time automatic fiducial marker tracking in low contrast cine-MV images

International Nuclear Information System (INIS)

Lin, Wei-Yang; Lin, Shu-Fang; Yang, Sheng-Chang; Liou, Shu-Cheng; Nath, Ravinder; Liu Wu

2013-01-01

Purpose: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Methods: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. Results: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The

Real-time automatic fiducial marker tracking in low contrast cine-MV images

Energy Technology Data Exchange (ETDEWEB)

Lin, Wei-Yang; Lin, Shu-Fang; Yang, Sheng-Chang; Liou, Shu-Cheng; Nath, Ravinder; Liu Wu [Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan, 62102 (China); Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut 06510-3220 (United States)

2013-01-15

Purpose: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Methods: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. Results: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The

Planning applications in image analysis

Science.gov (United States)

Boddy, Mark; White, Jim; Goldman, Robert; Short, Nick, Jr.

1994-01-01

We describe two interim results from an ongoing effort to automate the acquisition, analysis, archiving, and distribution of satellite earth science data. Both results are applications of Artificial Intelligence planning research to the automatic generation of processing steps for image analysis tasks. First, we have constructed a linear conditional planner (CPed), used to generate conditional processing plans. Second, we have extended an existing hierarchical planning system to make use of durations, resources, and deadlines, thus supporting the automatic generation of processing steps in time and resource-constrained environments.

Automatic Georeferencing of Aerial Images by Means of Topographic Database Information

DEFF Research Database (Denmark)

Høhle, Joachim

The book includes a preface and four articles which deal with the automatic georeferencing of aerial images. The articles are the written contribution of an seminar, held at Aalborg University in October 2002. The georeferencing or orientation of aerial images is the first step in mapping tasks l...... like generation of orthoimages, updating of topographic map data bases and generation of digial terrain models.......The book includes a preface and four articles which deal with the automatic georeferencing of aerial images. The articles are the written contribution of an seminar, held at Aalborg University in October 2002. The georeferencing or orientation of aerial images is the first step in mapping tasks...

AUTOMATIC AND GENERIC MOSAICING OF MULTISENSOR IMAGES: AN APPLICATION TO PLEIADES HR

Directory of Open Access Journals (Sweden)

F. Bignalet-Cazalet

2012-07-01

Full Text Available In the early phase of the Pleiades program, the CNES (the French Space Agency specified and developed a fully automatic mosaicing processing unit, in order to generate satellite image mosaics under operational conditions. This tool can automatically put each input image in a common geometry, homogenize the radiometry, and generate orthomosaics using stitching lines. As the image quality commissioning phase of Pleiades1A is on-going, this mosaicing process is being tested for the first time under operational conditions. The French newly launched high resolution satellite can acquire adjacent images for French Civil and Defense User Ground Segments. This paper presents the very firsts results of mosaicing Pleiades1A images. Beyond Pleiades’ use, our mosaicing tool can process a significant variety of images, including other satellites and airborne acquisitions, using automatically-taken or external ground control points, offering time-based image superposition, and more. This paper also presents the design of the mosaicing tool and describes the processing workflow and the additional capabilities and applications.

Quantitative assessment of intermetallic phase precipitation in a super duplex stainless steel weld metal using automatic image analysis

Energy Technology Data Exchange (ETDEWEB)

Gregori, A. [AB Sandvik Steel, Sandviken (Sweden). R and D Centre; Nilsson, J.-O. [AB Sandvik Steel, R and D Centre, Sandviken (Sweden); Bonollo, F. [Univ. di Padova, DTGSI, Vicenza (Italy)

1999-07-01

The microstructure of weld metal of the type 25%Cr-10%Ni-4%Mo-0.28%N in both as-welded and isothermally heat treated (temperature range: 700-1050 C: time range: 10s-72h) conditions has been investigated. Multipass welding was performed in Ar+2%N{sub 2} atmosphere using GTAW. By means of the electron diffraction technique. {sigma}-phase and {chi}-phase were detected and investigated. {chi}-phase precipitated more readily than {sigma}-phase and was found to be a precursor to {sigma}-phase by providing suitable nucleation sites. Quantitative image analysis of ferrite and intermetallic phases was performed as well as manual point counting (ISO 9042). Automatic image analysis was found to be more accurate. The results were used to assess the TTT-diagram with respect to intermetallic phase formation. On the basis of these results a CCT-diagram was computed, considering the intermetallic phase formation described by an Avrami type equation and adopting the additivity rule. (orig.)

Automatic analysis of macerals and reflectance; Analisis Automatico de Macerales y Reflectancia

Energy Technology Data Exchange (ETDEWEB)

Catalina, J.C.; Alarcon, D.; Gonzalez Prado, J.

1998-12-01

A new system has been developed to perform automatically macerals and reflectance analysis of single-seam bituminous coals, improving the interlaboratory accuracy of these types of analyses. The system follows the same steps as the manual method, requiring a human operator for preparation of coal samples and system startup; then, sample scanning, microscope focusing and field centre analysis are fully automatic. The main and most innovative idea of this approach is to coordinate an expert system with an image processing system, using both reflectance and morphological information. In this way, the system tries to reproduce the analysis procedure followed by a human expert in petrography. (Author)

Automatic Road Pavement Assessment with Image Processing: Review and Comparison

Directory of Open Access Journals (Sweden)

Sylvie Chambon

2011-01-01

Full Text Available In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

CLG for Automatic Image Segmentation

OpenAIRE

Christo Ananth; S.Santhana Priya; S.Manisha; T.Ezhil Jothi; M.S.Ramasubhaeswari

2017-01-01

This paper proposes an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. Active Contour Model provides a statistical model of object shape and appearance to a new image which are built during a training phase. In the graph cut technique, each pixel is represented as a node and the distance between those nodes is rep...

Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

Directory of Open Access Journals (Sweden)

Dina Khattab

2014-01-01

Full Text Available This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied with RGB, HSV, CMY, XYZ, and YUV color spaces. The comparative study and experimental results using different color images show that RGB color space is the best color space representation for the set of the images used.

The Digital Image Processing And Quantitative Analysis In Microscopic Image Characterization

International Nuclear Information System (INIS)

Ardisasmita, M. Syamsa

2000-01-01

Many electron microscopes although have produced digital images, but not all of them are equipped with a supporting unit to process and analyse image data quantitatively. Generally the analysis of image has to be made visually and the measurement is realized manually. The development of mathematical method for geometric analysis and pattern recognition, allows automatic microscopic image analysis with computer. Image processing program can be used for image texture and structure periodic analysis by the application of Fourier transform. Because the development of composite materials. Fourier analysis in frequency domain become important for measure the crystallography orientation. The periodic structure analysis and crystal orientation are the key to understand many material properties like mechanical strength. stress, heat conductivity, resistance, capacitance and other material electric and magnetic properties. In this paper will be shown the application of digital image processing in microscopic image characterization and analysis in microscopic image

Automatic extraction of corpus callosum from midsagittal head MR image and examination of Alzheimer-type dementia objective diagnostic system in feature analysis

International Nuclear Information System (INIS)

Kaneko, Tomoyuki; Kodama, Naoki; Kaeriyama, Tomoharu; Fukumoto, Ichiro

2004-01-01

We studied the objective diagnosis of Alzheimer-type dementia based on changes in the corpus callosum. We examined midsagittal head MR images of 40 Alzheimer-type dementia patients (15 men and 25 women; mean age, 75.4±5.5 years) and 31 healthy elderly persons (10 men and 21 women; mean age, 73.4±7.5 years), 71 subjects altogether. First, the corpus callosum was automatically extracted from midsagittal head MR images. Next, Alzheimer-type dementia was compared with the healthy elderly individuals using the features of shape factor and six features of Co-occurrence Matrix from the corpus callosum. Automatic extraction of the corpus callosum succeeded in 64 of 71 individuals, for an extraction rate of 90.1%. A statistically significant difference was found in 7 of the 9 features between Alzheimer-type dementia patients and the healthy elderly adults. Discriminant analysis using the 7 features demonstrated a sensitivity rate of 82.4%, specificity of 89.3%, and overall accuracy of 85.5%. These results indicated the possibility of an objective diagnostic system for Alzheimer-type dementia using feature analysis based on change in the corpus callosum. (author)

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.

Science.gov (United States)

Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas

2013-01-01

Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis

Directory of Open Access Journals (Sweden)

Christian Held

2013-01-01

Full Text Available Introduction: Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. Methods: In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline′s modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. Results: This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. Conclusion: The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

Automatic imitation: A meta-analysis.

Science.gov (United States)

Cracco, Emiel; Bardi, Lara; Desmet, Charlotte; Genschow, Oliver; Rigoni, Davide; De Coster, Lize; Radkova, Ina; Deschrijver, Eliane; Brass, Marcel

2018-05-01

Automatic imitation is the finding that movement execution is facilitated by compatible and impeded by incompatible observed movements. In the past 15 years, automatic imitation has been studied to understand the relation between perception and action in social interaction. Although research on this topic started in cognitive science, interest quickly spread to related disciplines such as social psychology, clinical psychology, and neuroscience. However, important theoretical questions have remained unanswered. Therefore, in the present meta-analysis, we evaluated seven key questions on automatic imitation. The results, based on 161 studies containing 226 experiments, revealed an overall effect size of g z = 0.95, 95% CI [0.88, 1.02]. Moderator analyses identified automatic imitation as a flexible, largely automatic process that is driven by movement and effector compatibility, but is also influenced by spatial compatibility. Automatic imitation was found to be stronger for forced choice tasks than for simple response tasks, for human agents than for nonhuman agents, and for goalless actions than for goal-directed actions. However, it was not modulated by more subtle factors such as animacy beliefs, motion profiles, or visual perspective. Finally, there was no evidence for a relation between automatic imitation and either empathy or autism. Among other things, these findings point toward actor-imitator similarity as a crucial modulator of automatic imitation and challenge the view that imitative tendencies are an indicator of social functioning. The current meta-analysis has important theoretical implications and sheds light on longstanding controversies in the literature on automatic imitation and related domains. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Multi-spectral Image Analysis for Astaxanthin Coating Classification

DEFF Research Database (Denmark)

Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

2011-01-01

Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels...

Automatic system for detecting pornographic images

Science.gov (United States)

Ho, Kevin I. C.; Chen, Tung-Shou; Ho, Jun-Der

2002-09-01

Due to the dramatic growth of network and multimedia technology, people can more easily get variant information by using Internet. Unfortunately, it also makes the diffusion of illegal and harmful content much easier. So, it becomes an important topic for the Internet society to protect and safeguard Internet users from these content that may be encountered while surfing on the Net, especially children. Among these content, porno graphs cause more serious harm. Therefore, in this study, we propose an automatic system to detect still colour porno graphs. Starting from this result, we plan to develop an automatic system to search porno graphs or to filter porno graphs. Almost all the porno graphs possess one common characteristic that is the ratio of the size of skin region and non-skin region is high. Based on this characteristic, our system first converts the colour space from RGB colour space to HSV colour space so as to segment all the possible skin-colour regions from scene background. We also apply the texture analysis on the selected skin-colour regions to separate the skin regions from non-skin regions. Then, we try to group the adjacent pixels located in skin regions. If the ratio is over a given threshold, we can tell if the given image is a possible porno graph. Based on our experiment, less than 10% of non-porno graphs are classified as pornography, and over 80% of the most harmful porno graphs are classified correctly.

Automatic analysis of ultrasonic data

International Nuclear Information System (INIS)

Horteur, P.; Colin, J.; Benoist, P.; Bonis, M.; Paradis, L.

1986-10-01

This paper describes an automatic and self-contained data processing system, transportable on site, able to perform images such as ''A. Scan'', ''B. Scan'', ... to present very quickly the results of the control. It can be used in the case of pressure vessel inspection [fr

A new method for automatic tracking of facial landmarks in 3D motion captured images (4D).

Science.gov (United States)

Al-Anezi, T; Khambay, B; Peng, M J; O'Leary, E; Ju, X; Ayoub, A

2013-01-01

The aim of this study was to validate the automatic tracking of facial landmarks in 3D image sequences. 32 subjects (16 males and 16 females) aged 18-35 years were recruited. 23 anthropometric landmarks were marked on the face of each subject with non-permanent ink using a 0.5mm pen. The subjects were asked to perform three facial animations (maximal smile, lip purse and cheek puff) from rest position. Each animation was captured by the 3D imaging system. A single operator manually digitised the landmarks on the 3D facial models and their locations were compared with those of the automatically tracked ones. To investigate the accuracy of manual digitisation, the operator re-digitised the same set of 3D images of 10 subjects (5 male and 5 female) at 1 month interval. The discrepancies in x, y and z coordinates between the 3D position of the manual digitised landmarks and that of the automatic tracked facial landmarks were within 0.17mm. The mean distance between the manually digitised and the automatically tracked landmarks using the tracking software was within 0.55 mm. The automatic tracking of facial landmarks demonstrated satisfactory accuracy which would facilitate the analysis of the dynamic motion during facial animations. Copyright © 2012 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

CAnat: An algorithm for the automatic segmentation of anatomy of medical images

International Nuclear Information System (INIS)

Caon, M.; Gobert, L.; Mariusz, B.

2011-01-01

Full text: To develop a method to automatically categorise organs and tissues displayed in medical images. Dosimetry calculations using Monte Carlo methods require a mathematical representation of human anatomy e.g. a voxel phantom. For a whole body, their construction involves processing several hundred images to identify each organ and tissue-the process is very time-consuming. This project is developing a Computational Anatomy (CAnat) algorithm to automatically recognise and classify the different tissue in a tomographic image. Methods The algorithm utilizes the Statistical Region Merging technique (SRM). The SRM depends on one estimated parameter. The parameter is a measure of statistical complexity of the image and can be automatically adjusted to suit individual image features. This allows for automatic tuning of coarseness of the overall segmentation as well as object specific selection for further tasks. CAnat is tested on two CT images selected to represent different anatomical complexities. In the mid-thigh image, tissues/. regions of interest are air, fat, muscle, bone marrow and compact bone. In the pelvic image, fat, urinary bladder and anus/colon, muscle, cancellous bone, and compact bone. Segmentation results were evaluated using the Jaccard index which is a measure of set agreement. An index of one indicates perfect agreement between CAnat and manual segmentation. The Jaccard indices for the mid-thigh CT were 0.99, 0.89, 0.97, 0.63 and 0.88, respectively and for the pelvic CT were 0.99, 0.81, 0.77, 0.93, 0.53, 0.76, respectively. Conclusion The high accuracy preliminary segmentation results demonstrate the feasibility of the CAnat algorithm.

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

Science.gov (United States)

Villadsen, Jonas; Hansen, Hanne D; Jørgensen, Louise M; Keller, Sune H; Andersen, Flemming L; Petersen, Ida N; Knudsen, Gitte M; Svarer, Claus

2018-01-15

The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated. Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer. MRI and [ 11 C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [ 11 C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames. We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration. Copyright © 2017 Elsevier B.V. All rights reserved.

Mirion--a software package for automatic processing of mass spectrometric images.

Science.gov (United States)

Paschke, C; Leisner, A; Hester, A; Maass, K; Guenther, S; Bouschen, W; Spengler, B

2013-08-01

Mass spectrometric imaging (MSI) techniques are of growing interest for the Life Sciences. In recent years, the development of new instruments employing ion sources that are tailored for spatial scanning allowed the acquisition of large data sets. A subsequent data processing, however, is still a bottleneck in the analytical process, as a manual data interpretation is impossible within a reasonable time frame. The transformation of mass spectrometric data into spatial distribution images of detected compounds turned out to be the most appropriate method to visualize the results of such scans, as humans are able to interpret images faster and easier than plain numbers. Image generation, thus, is a time-consuming and complex yet very efficient task. The free software package "Mirion," presented in this paper, allows the handling and analysis of data sets acquired by mass spectrometry imaging. Mirion can be used for image processing of MSI data obtained from many different sources, as it uses the HUPO-PSI-based standard data format imzML, which is implemented in the proprietary software of most of the mass spectrometer companies. Different graphical representations of the recorded data are available. Furthermore, automatic calculation and overlay of mass spectrometric images promotes direct comparison of different analytes for data evaluation. The program also includes tools for image processing and image analysis.

Image fusion between whole body FDG PET images and whole body MRI images using a full-automatic mutual information-based multimodality image registration software

International Nuclear Information System (INIS)

Uchida, Yoshitaka; Nakano, Yoshitada; Fujibuchi, Toshiou; Isobe, Tomoko; Kazama, Toshiki; Ito, Hisao

2006-01-01

We attempted image fusion between whole body PET and whole body MRI of thirty patients using a full-automatic mutual information (MI) -based multimodality image registration software and evaluated accuracy of this method and impact of the coregistrated imaging on diagnostic accuracy. For 25 of 30 fused images in body area, translating gaps were within 6 mm in all axes and rotating gaps were within 2 degrees around all axes. In head and neck area, considerably much gaps caused by difference of head inclination at imaging occurred in 16 patients, however these gaps were able to decrease by fused separately. In 6 patients, diagnostic accuracy using PET/MRI fused images was superior compared by PET image alone. This work shows that whole body FDG PET images and whole body MRI images can be automatically fused using MI-based multimodality image registration software accurately and this technique can add useful information when evaluating FDG PET images. (author)

Comparison of automatic and visual methods used for image segmentation in Endodontics: a microCT study.

Science.gov (United States)

Queiroz, Polyane Mazucatto; Rovaris, Karla; Santaella, Gustavo Machado; Haiter-Neto, Francisco; Freitas, Deborah Queiroz

2017-01-01

To calculate root canal volume and surface area in microCT images, an image segmentation by selecting threshold values is required, which can be determined by visual or automatic methods. Visual determination is influenced by the operator's visual acuity, while the automatic method is done entirely by computer algorithms. To compare between visual and automatic segmentation, and to determine the influence of the operator's visual acuity on the reproducibility of root canal volume and area measurements. Images from 31 extracted human anterior teeth were scanned with a μCT scanner. Three experienced examiners performed visual image segmentation, and threshold values were recorded. Automatic segmentation was done using the "Automatic Threshold Tool" available in the dedicated software provided by the scanner's manufacturer. Volume and area measurements were performed using the threshold values determined both visually and automatically. The paired Student's t-test showed no significant difference between visual and automatic segmentation methods regarding root canal volume measurements (p=0.93) and root canal surface (p=0.79). Although visual and automatic segmentation methods can be used to determine the threshold and calculate root canal volume and surface, the automatic method may be the most suitable for ensuring the reproducibility of threshold determination.

Automatic food detection in egocentric images using artificial intelligence technology

Science.gov (United States)

Our objective was to develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable devic...

High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately.

Science.gov (United States)

Chen, Wenjin; Wong, Chung; Vosburgh, Evan; Levine, Arnold J; Foran, David J; Xu, Eugenia Y

2014-07-08

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application - SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model

Automatic Vessel Segmentation on Retinal Images

Institute of Scientific and Technical Information of China (English)

Chun-Yuan Yu; Chia-Jen Chang; Yen-Ju Yao; Shyr-Shen Yu

2014-01-01

Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hyper-tension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image. Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm. In this paper, for improving the drawbacks of multi-scale line detection, only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE (digital retinal images for vessel extraction) retinal database, which is much better than other methods.

Automatic measurement for solid state track detectors

International Nuclear Information System (INIS)

Ogura, Koichi

1982-01-01

Since in solid state track detectors, their tracks are measured with a microscope, observers are forced to do hard works that consume time and labour. This causes to obtain poor statistic accuracy or to produce personal error. Therefore, many researches have been done to aim at simplifying and automating track measurement. There are two categories in automating the measurement: simple counting of the number of tracks and the requirements to know geometrical elements such as the size of tracks or their coordinates as well as the number of tracks. The former is called automatic counting and the latter automatic analysis. The method to generally evaluate the number of tracks in automatic counting is the estimation of the total number of tracks in the total detector area or in a field of view of a microscope. It is suitable for counting when the track density is higher. The method to count tracks one by one includes the spark counting and the scanning microdensitometer. Automatic analysis includes video image analysis in which the high quality images obtained with a high resolution video camera are processed with a micro-computer, and the tracks are automatically recognized and measured by feature extraction. This method is described in detail. In many kinds of automatic measurements reported so far, frequently used ones are ''spark counting'' and ''video image analysis''. (Wakatsuki, Y.)

Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging

DEFF Research Database (Denmark)

Dam, Erik Bjørnager

2018-01-01

Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic...... image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance...... for segmentation of knee MRI using the fully automatic KneeIQ framework. The validation included a total of 1975 scans from both high-field and low-field MRI. The results demonstrated that the pre-processing method denoted Atlas Affine Normalization significantly removed scanner drift effects and ensured...

Automatic anatomically selective image enhancement in digital chest radiography

International Nuclear Information System (INIS)

Sezan, M.I.; Minerbo, G.N.; Schaetzing, R.

1989-01-01

The authors develop a technique for automatic anatomically selective enhancement of digital chest radiographs. Anatomically selective enhancement is motivated by the desire to simultaneously meet the different enhancement requirements of the lung field and the mediastinum. A recent peak detection algorithm and a set of rules are applied to the image histogram to determine automatically a gray-level threshold between the lung field and mediastinum. The gray-level threshold facilitates anatomically selective gray-scale modification and/or unsharp masking. Further, in an attempt to suppress possible white-band or black-band artifacts due to unsharp masking at sharp edges, local-contrast adaptivity is incorporated into anatomically selective unsharp masking by designing an anatomy-sensitive emphasis parameter which varies asymmetrically with positive and negative values of the local image contrast

Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches.

Science.gov (United States)

Cai, Wei; He, Baochun; Fan, Yingfang; Fang, Chihua; Jia, Fucang

2016-11-08

This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods- one interactive method, an in-house-developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)-based segmentation, and one automatic probabilistic atlas (PA)-guided segmentation method on clinical contrast-enhanced CT images. Forty-two datasets, including 27 normal liver and 15 space-occupying liver lesion patients, were retrospectively included in this study. The three methods - one semiautomatic 3DMIA, one automatic ASM-based, and one automatic PA-based liver volumetry - achieved an accuracy with VD (volume difference) of -1.69%, -2.75%, and 3.06% in the normal group, respectively, and with VD of -3.20%, -3.35%, and 4.14% in the space-occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excel-lent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p < 0.001). The semiautomatic interactive 3DMIA, automatic ASM-based, and automatic PA-based liver volum-etry agreed well with manual gold standard in both the normal liver group and the space-occupying lesion group. The ASM- and PA-based automatic segmentation have better efficiency in clinical use. © 2016 The Authors.

Automatic terrain modeling using transfinite element analysis

KAUST Repository

Collier, Nathan

2010-05-31

An automatic procedure for modeling terrain is developed based on L2 projection-based interpolation of discrete terrain data onto transfinite function spaces. The function space is refined automatically by the use of image processing techniques to detect regions of high error and the flexibility of the transfinite interpolation to add degrees of freedom to these areas. Examples are shown of a section of the Palo Duro Canyon in northern Texas.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

Science.gov (United States)

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

VISUAL PERCEPTION BASED AUTOMATIC RECOGNITION OF CELL MOSAICS IN HUMAN CORNEAL ENDOTHELIUMMICROSCOPY IMAGES

Directory of Open Access Journals (Sweden)

Yann Gavet

2011-05-01

Full Text Available The human corneal endothelium can be observed with two types of microscopes: classical optical microscope for ex-vivo imaging, and specular optical microscope for in-vivo imaging. The quality of the cornea is correlated to the endothelial cell density and morphometry. Automatic methods to analyze the human corneal endothelium images are still not totally efficient. Image analysis methods that focus only on cell contours do not give good results in presence of noise and of bad conditions of acquisition. More elaborated methods introduce regional informations in order to performthe cell contours completion, thus implementing the duality contour-region. Their good performance can be explained by their connections with several basic principles of human visual perception (Gestalt Theory and Marr's computational theory.

Automatic Generation of Wide Dynamic Range Image without Pseudo-Edge Using Integration of Multi-Steps Exposure Images

Science.gov (United States)

Migiyama, Go; Sugimura, Atsuhiko; Osa, Atsushi; Miike, Hidetoshi

Recently, digital cameras are offering technical advantages rapidly. However, the shot image is different from the sight image generated when that scenery is seen with the naked eye. There are blown-out highlights and crushed blacks in the image that photographed the scenery of wide dynamic range. The problems are hardly generated in the sight image. These are contributory cause of difference between the shot image and the sight image. Blown-out highlights and crushed blacks are caused by the difference of dynamic range between the image sensor installed in a digital camera such as CCD and CMOS and the human visual system. Dynamic range of the shot image is narrower than dynamic range of the sight image. In order to solve the problem, we propose an automatic method to decide an effective exposure range in superposition of edges. We integrate multi-step exposure images using the method. In addition, we try to erase pseudo-edges using the process to blend exposure values. Afterwards, we get a pseudo wide dynamic range image automatically.

SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

International Nuclear Information System (INIS)

Qiu, J; Yang, D

2015-01-01

Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

Energy Technology Data Exchange (ETDEWEB)

Qiu, J [Washington University in St Louis, Taian, Shandong (China); Yang, D [Washington University School of Medicine, St Louis, MO (United States)

2015-06-15

Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

Science.gov (United States)

Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

2017-12-01

The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

AUTOMATIC INTERPRETATION OF HIGH RESOLUTION SAR IMAGES: FIRST RESULTS OF SAR IMAGE SIMULATION FOR SINGLE BUILDINGS

Directory of Open Access Journals (Sweden)

J. Tao

2012-09-01

Full Text Available Due to the all-weather data acquisition capabilities, high resolution space borne Synthetic Aperture Radar (SAR plays an important role in remote sensing applications like change detection. However, because of the complex geometric mapping of buildings in urban areas, SAR images are often hard to interpret. SAR simulation techniques ease the visual interpretation of SAR images, while fully automatic interpretation is still a challenge. This paper presents a method for supporting the interpretation of high resolution SAR images with simulated radar images using a LiDAR digital surface model (DSM. Line features are extracted from the simulated and real SAR images and used for matching. A single building model is generated from the DSM and used for building recognition in the SAR image. An application for the concept is presented for the city centre of Munich where the comparison of the simulation to the TerraSAR-X data shows a good similarity. Based on the result of simulation and matching, special features (e.g. like double bounce lines, shadow areas etc. can be automatically indicated in SAR image.

Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

International Nuclear Information System (INIS)

Makili, L.; Vega, J.; Dormido-Canto, S.; Pastor, I.; Pereira, A.; Farias, G.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C.; Busch, P.

2010-01-01

An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

Energy Technology Data Exchange (ETDEWEB)

Makili, L. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Dormido-Canto, S., E-mail: sebas@dia.uned.e [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Pastor, I.; Pereira, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Farias, G. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Institut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

2010-07-15

An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

Color Segmentation Approach of Infrared Thermography Camera Image for Automatic Fault Diagnosis

International Nuclear Information System (INIS)

Djoko Hari Nugroho; Ari Satmoko; Budhi Cynthia Dewi

2007-01-01

Predictive maintenance based on fault diagnosis becomes very important in current days to assure the availability and reliability of a system. The main purpose of this research is to configure a computer software for automatic fault diagnosis based on image model acquired from infrared thermography camera using color segmentation approach. This technique detects hot spots in equipment of the plants. Image acquired from camera is first converted to RGB (Red, Green, Blue) image model and then converted to CMYK (Cyan, Magenta, Yellow, Key for Black) image model. Assume that the yellow color in the image represented the hot spot in the equipment, the CMYK image model is then diagnosed using color segmentation model to estimate the fault. The software is configured utilizing Borland Delphi 7.0 computer programming language. The performance is then tested for 10 input infrared thermography images. The experimental result shows that the software capable to detect the faulty automatically with performance value of 80 % from 10 sheets of image input. (author)

Automatic measurement of axial length of human eye using three-dimensional magnetic resonance imaging

International Nuclear Information System (INIS)

Watanabe, Masaki; Kiryu, Tohru

2011-01-01

The measurement of axial length and the evaluation of three dimensional (3D) form of an eye are essential to evaluate the mechanism of myopia progression. We propose a method of automatic measurement of axial length including adjustment of the pulse sequence of short-term scan which could suppress influence of eyeblink, using a magnetic resonance imaging (MRI) which acquires 3D images noninvasively. Acquiring T 2 -weighted images with 3.0 tesla MRI device and eight-channel phased-array head coil, we extracted left and right eye ball images, and then reconstructed 3D volume. The surface coordinates were calculated from 3D volume, fitting the ellipsoid model coordinates with the surface coordinates, and measured the axial length automatically. Measuring twenty one subjects, we compared the automatically measured values of axial length with the manually measured ones, then confirmed significant elongation in the axial length of myopia compared with that of emmetropia. Furthermore, there were no significant differences (P<0.05) between the means of automatic measurements and the manual ones. Accordingly, the automatic measurement process of axial length could be a tool for the elucidation of the mechanism of myopia progression, which would be suitable for evaluating the axial length easily and noninvasively. (author)

Automatic differentiation algorithms in model analysis

NARCIS (Netherlands)

Huiskes, M.J.

2002-01-01

Title: Automatic differentiation algorithms in model analysis
Author: M.J. Huiskes
Date: 19 March, 2002

In this thesis automatic differentiation algorithms and derivative-based methods

Adapting Mask-RCNN for Automatic Nucleus Segmentation

OpenAIRE

Johnson, Jeremiah W.

2018-01-01

Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for ...

Automatic extraction analysis of the anatomical functional area for normal brain 18F-FDG PET imaging

International Nuclear Information System (INIS)

Guo Wanhua; Jiang Xufeng; Zhang Liying; Lu Zhongwei; Li Peiyong; Zhu Chengmo; Zhang Jiange; Pan Jiapu

2003-01-01

Using self-designed automatic extraction software of brain functional area, the grey scale distribution of 18 F-FDG imaging and the relationship between the 18 F-FDG accumulation of brain anatomic function area and the 18 F-FDG injected dose, the level of glucose, the age, etc., were studied. According to the Talairach coordinate system, after rotation, drift and plastic deformation, the 18 F-FDG PET imaging was registered into the Talairach coordinate atlas, and then the average gray value scale ratios between individual brain anatomic functional area and whole brain area was calculated. Further more the statistics of the relationship between the 18 F-FDG accumulation of every brain anatomic function area and the 18 F-FDG injected dose, the level of glucose and the age were tested by using multiple stepwise regression model. After images' registration, smoothing and extraction, main cerebral cortex of the 18 F-FDG PET brain imaging can be successfully localized and extracted, such as frontal lobe, parietal lobe, occipital lobe, temporal lobe, cerebellum, brain ventricle, thalamus and hippocampus. The average ratios to the inner reference of every brain anatomic functional area were 1.01 ± 0.15. By multiple stepwise regression with the exception of thalamus and hippocampus, the grey scale of all the brain functional area was negatively correlated to the ages, but with no correlation to blood sugar and dose in all areas. To the 18 F-FDG PET imaging, the brain functional area extraction program could automatically delineate most of the cerebral cortical area, and also successfully reflect the brain blood and metabolic study, but extraction of the more detailed area needs further investigation

Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.

Science.gov (United States)

Chiu, Stephanie J; Izatt, Joseph A; O'Connell, Rachelle V; Winter, Katrina P; Toth, Cynthia A; Farsiu, Sina

2012-01-05

To automatically segment retinal spectral domain optical coherence tomography (SD-OCT) images of eyes with age-related macular degeneration (AMD) and various levels of image quality to advance the study of retinal pigment epithelium (RPE)+drusen complex (RPEDC) volume changes indicative of AMD progression. A general segmentation framework based on graph theory and dynamic programming was used to segment three retinal boundaries in SD-OCT images of eyes with drusen and geographic atrophy (GA). A validation study for eyes with nonneovascular AMD was conducted, forming subgroups based on scan quality and presence of GA. To test for accuracy, the layer thickness results from two certified graders were compared against automatic segmentation results for 220 B-scans across 20 patients. For reproducibility, automatic layer volumes were compared that were generated from 0° versus 90° scans in five volumes with drusen. The mean differences in the measured thicknesses of the total retina and RPEDC layers were 4.2 ± 2.8 and 3.2 ± 2.6 μm for automatic versus manual segmentation. When the 0° and 90° datasets were compared, the mean differences in the calculated total retina and RPEDC volumes were 0.28% ± 0.28% and 1.60% ± 1.57%, respectively. The average segmentation time per image was 1.7 seconds automatically versus 3.5 minutes manually. The automatic algorithm accurately and reproducibly segmented three retinal boundaries in images containing drusen and GA. This automatic approach can reduce time and labor costs and yield objective measurements that potentially reveal quantitative RPE changes in longitudinal clinical AMD studies. (ClinicalTrials.gov number, NCT00734487.).

Automatic caption generation for news images.

Science.gov (United States)

Feng, Yansong; Lapata, Mirella

2013-04-01

This paper is concerned with the task of automatically generating captions for images, which is important for many image-related applications. Examples include video and image retrieval as well as the development of tools that aid visually impaired individuals to access pictorial information. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned and colocated with thematically related documents. Our model learns to create captions from a database of news articles, the pictures embedded in them, and their captions, and consists of two stages. Content selection identifies what the image and accompanying article are about, whereas surface realization determines how to verbalize the chosen content. We approximate content selection with a probabilistic image annotation model that suggests keywords for an image. The model postulates that images and their textual descriptions are generated by a shared set of latent variables (topics) and is trained on a weakly labeled dataset (which treats the captions and associated news articles as image labels). Inspired by recent work in summarization, we propose extractive and abstractive surface realization models. Experimental results show that it is viable to generate captions that are pertinent to the specific content of an image and its associated article, while permitting creativity in the description. Indeed, the output of our abstractive model compares favorably to handwritten captions and is often superior to extractive methods.

Image processing. A system for the automatic sorting of chromosomes

International Nuclear Information System (INIS)

Najai, Amor

1977-01-01

The present paper deals with two aspects of the system: - an automata (specialized hardware) dedicated to image processing. Images are digitized, divided into sub-units and computations are carried out on their main parameters. - A software for the automatic recognition and sorting of chromosomes is implemented on a Multi-20 minicomputer, connected to the automata. (author) [fr

Analysis of engineering drawings and raster map images

CERN Document Server

Henderson, Thomas C

2013-01-01

Presents up-to-date methods and algorithms for the automated analysis of engineering drawings and digital cartographic maps Discusses automatic engineering drawing and map analysis techniques Covers detailed accounts of the use of unsupervised segmentation algorithms to map images

Image analysis for ophthalmological diagnosis image processing of Corvis ST images using Matlab

CERN Document Server

Koprowski, Robert

2016-01-01

This monograph focuses on the use of analysis and processing methods for images from the Corvis® ST tonometer. The presented analysis is associated with the quantitative, repeatable and fully automatic evaluation of the response of the eye, eyeball and cornea to an air-puff. All the described algorithms were practically implemented in MATLAB®. The monograph also describes and provides the full source code designed to perform the discussed calculations. As a result, this monograph is intended for scientists, graduate students and students of computer science and bioengineering as well as doctors wishing to expand their knowledge of modern diagnostic methods assisted by various image analysis and processing methods.

Image processing and pattern recognition with CVIPtools MATLAB toolbox: automatic creation of masks for veterinary thermographic images

Science.gov (United States)

Mishra, Deependra K.; Umbaugh, Scott E.; Lama, Norsang; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

CVIPtools is a software package for the exploration of computer vision and image processing developed in the Computer Vision and Image Processing Laboratory at Southern Illinois University Edwardsville. CVIPtools is available in three variants - a) CVIPtools Graphical User Interface, b) CVIPtools C library and c) CVIPtools MATLAB toolbox, which makes it accessible to a variety of different users. It offers students, faculty, researchers and any user a free and easy way to explore computer vision and image processing techniques. Many functions have been implemented and are updated on a regular basis, the library has reached a level of sophistication that makes it suitable for both educational and research purposes. In this paper, the detail list of the functions available in the CVIPtools MATLAB toolbox are presented and how these functions can be used in image analysis and computer vision applications. The CVIPtools MATLAB toolbox allows the user to gain practical experience to better understand underlying theoretical problems in image processing and pattern recognition. As an example application, the algorithm for the automatic creation of masks for veterinary thermographic images is presented.

Automatic quantitative analysis of liver functions by a computer system

International Nuclear Information System (INIS)

Shinpo, Takako

1984-01-01

In the previous paper, we confirmed the clinical usefulness of hepatic clearance (hepatic blood flow), which is the hepatic uptake and blood disappearance rate coefficients. These were obtained by the initial slope index of each minute during a period of five frames of a hepatogram by injecting sup(99m)Tc-Sn-colloid 37 MBq. To analyze the information simply, rapidly and accurately, we developed a automatic quantitative analysis for liver functions. Information was obtained every quarter minute during a period of 60 frames of the sequential image. The sequential counts were measured for the heart, whole liver, both left lobe and right lobes using a computer connected to a scintillation camera. We measured the effective hepatic blood flow, from the disappearance rate multiplied by the percentage of hepatic uptake as follows, (liver counts)/(tatal counts of the field) Our method of analysis automatically recorded the reappearance graph of the disappearance curve and uptake curve on the basis of the heart and the whole liver, respectively; and computed using BASIC language. This method makes it possible to obtain the image of the initial uptake of sup(99m)Tc-Sn-colloid into the liver by a small dose of it. (author)

A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system.

Science.gov (United States)

Liu, Yinlong; Song, Zhijian; Wang, Manning

2017-12-01

Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty. A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration. Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18-26 mm) and different support radii (2-6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm. This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

International Nuclear Information System (INIS)

STOYANOVA, R.S.; OCHS, M.F.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.

1999-01-01

Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Science.gov (United States)

Cai, Wei; He, Baochun; Fang, Chihua

2016-01-01

This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods— one interactive method, an in‐house‐developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)‐based segmentation, and one automatic probabilistic atlas (PA)‐guided segmentation method on clinical contrast‐enhanced CT images. Forty‐two datasets, including 27 normal liver and 15 space‐occupying liver lesion patients, were retrospectively included in this study. The three methods — one semiautomatic 3DMIA, one automatic ASM‐based, and one automatic PA‐based liver volumetry — achieved an accuracy with VD (volume difference) of −1.69%,−2.75%, and 3.06% in the normal group, respectively, and with VD of −3.20%,−3.35%, and 4.14% in the space‐occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (pvolumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (pvolumetry agreed well with manual gold standard in both the normal liver group and the space‐occupying lesion group. The ASM‐ and PA‐based automatic segmentation have better efficiency in clinical use. PACS number(s): 87.55.‐x PMID:27929487

An image analysis system for near-infrared (NIR) fluorescence lymph imaging

Science.gov (United States)

Zhang, Jingdan; Zhou, Shaohua Kevin; Xiang, Xiaoyan; Rasmussen, John C.; Sevick-Muraca, Eva M.

2011-03-01

Quantitative analysis of lymphatic function is crucial for understanding the lymphatic system and diagnosing the associated diseases. Recently, a near-infrared (NIR) fluorescence imaging system is developed for real-time imaging lymphatic propulsion by intradermal injection of microdose of a NIR fluorophore distal to the lymphatics of interest. However, the previous analysis software3, 4 is underdeveloped, requiring extensive time and effort to analyze a NIR image sequence. In this paper, we develop a number of image processing techniques to automate the data analysis workflow, including an object tracking algorithm to stabilize the subject and remove the motion artifacts, an image representation named flow map to characterize lymphatic flow more reliably, and an automatic algorithm to compute lymph velocity and frequency of propulsion. By integrating all these techniques to a system, the analysis workflow significantly reduces the amount of required user interaction and improves the reliability of the measurement.

The One to Multiple Automatic High Accuracy Registration of Terrestrial LIDAR and Optical Images

Science.gov (United States)

Wang, Y.; Hu, C.; Xia, G.; Xue, H.

2018-04-01

The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two - dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

NARCIS (Netherlands)

Makili, L.; Vega, J.; Dormido-Canto, S.; Pastor, I.; Pereira, A.; Farias, G.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M. C.; Busch, P.

2010-01-01

An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge,

Automatic Derivation of Statistical Data Analysis Algorithms: Planetary Nebulae and Beyond

Science.gov (United States)

Fischer, Bernd; Hajian, Arsen; Knuth, Kevin; Schumann, Johann

2004-04-01

AUTOBAYES is a fully automatic program synthesis system for the data analysis domain. Its input is a declarative problem description in form of a statistical model; its output is documented and optimized C/C++ code. The synthesis process relies on the combination of three key techniques. Bayesian networks are used as a compact internal representation mechanism which enables problem decompositions and guides the algorithm derivation. Program schemas are used as independently composable building blocks for the algorithm construction; they can encapsulate advanced algorithms and data structures. A symbolic-algebraic system is used to find closed-form solutions for problems and emerging subproblems. In this paper, we describe the application of AUTOBAYES to the analysis of planetary nebulae images taken by the Hubble Space Telescope. We explain the system architecture, and present in detail the automatic derivation of the scientists' original analysis as well as a refined analysis using clustering models. This study demonstrates that AUTOBAYES is now mature enough so that it can be applied to realistic scientific data analysis tasks.

Automatic classification of minimally invasive instruments based on endoscopic image sequences

Science.gov (United States)

Speidel, Stefanie; Benzko, Julia; Krappe, Sebastian; Sudra, Gunther; Azad, Pedram; Müller-Stich, Beat Peter; Gutt, Carsten; Dillmann, Rüdiger

2009-02-01

Minimally invasive surgery is nowadays a frequently applied technique and can be regarded as a major breakthrough in surgery. The surgeon has to adopt special operation-techniques and deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To analyze the current situation for context-aware assistance, we need intraoperatively gained sensor data and a model of the intervention. A situation consists of information about the performed activity, the used instruments, the surgical objects, the anatomical structures and defines the state of an intervention for a given moment in time. The endoscopic images provide a rich source of information which can be used for an image-based analysis. Different visual cues are observed in order to perform an image-based analysis with the objective to gain as much information as possible about the current situation. An important visual cue is the automatic recognition of the instruments which appear in the scene. In this paper we present the classification of minimally invasive instruments using the endoscopic images. The instruments are not modified by markers. The system segments the instruments in the current image and recognizes the instrument type based on three-dimensional instrument models.

BgCut: Automatic Ship Detection from UAV Images

Directory of Open Access Journals (Sweden)

Chao Xu

2014-01-01

foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

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

Science.gov (United States)

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

2017-12-01

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

Automatic construction of 3D-ASM intensity models by simulating image acquisition: application to myocardial gated SPECT studies.

Science.gov (United States)

Tobon-Gomez, Catalina; Butakoff, Constantine; Aguade, Santiago; Sukno, Federico; Moragas, Gloria; Frangi, Alejandro F

2008-11-01

Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.

Improvement in the performance of CAD for the Alzheimer-type dementia based on automatic extraction of temporal lobe from coronal MR images

International Nuclear Information System (INIS)

Kaeriyama, Tomoharu; Kodama, Naoki; Kaneko, Tomoyuki; Shimada, Tetsuo; Tanaka, Hiroyuki; Takeda, Ai; Fukumoto, Ichiro

2004-01-01

In this study, we extracted whole brain and temporal lobe images from MR images (26 healthy elderly controls and 34 Alzheimer-type dementia patients) by means of binarize, mask processing, template matching, Hough transformation, and boundary tracing etc. We assessed the extraction accuracy by comparing the extracted images to images extracts by a radiological technologist. The results of assessment by consistent rate; brain images 91.3±4.3%, right temporal lobe 83.3±6.9%, left temporal lobe 83.7±7.6%. Furthermore discriminant analysis using 6 textural features demonstrated sensitivity and specificity of 100% when the healthy elderly controls were compared to the Alzheimer-type dementia patients. Our research showed the possibility of automatic objective diagnosis of temporal lobe abnormalities by automatic extracted images of the temporal lobes. (author)

Image simulation for automatic license plate recognition

Science.gov (United States)

Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

2012-01-01

Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

Upgrade of the Automatic Analysis System in the TJ-II Thomson Scattering Diagnostic: New Image Recognition Classifier and Fault Condition Detection

Energy Technology Data Exchange (ETDEWEB)

Makili, L.; Dormido-Canto, S. [UNED, Madrid (Spain); Vega, J.; Pastor, I.; Pereira, A.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M. [Association EuratomCIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Instituut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

2009-07-01

Full text of publication follows: An automatic image classification system has been in operation for years in the TJ-II Thomson diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut o density during ECH heating. Each kind of image implies the execution of different application software. Therefore, the classification system was developed to launch the corresponding software in an automatic way. The method to recognize the several classes was based on a learning system, in particular Support Vector Machines (SVM). Since the first implementation of the classifier, a relevant improvement has been accomplished in the diagnostic: a new notch filter is in operation, having a larger stray-light rejection at the ruby wavelength than the previous filter. On the other hand, its location in the optical system has been modified. As a consequence, the stray light pattern in the CCD image is located in a different position. In addition to these transformations, the power of neutral beams injected in the TJ-II plasma has been increased about a factor of 2. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. The creation of a new model (also based on SVM) under the present conditions has been necessary. Finally, specific error conditions in the data acquisition process can automatically be detected now. The recovering process can be automated, thereby avoiding the loss of data in ensuing discharges. (authors)

Automatic anterior chamber angle assessment for HD-OCT images.

Science.gov (United States)

Tian, Jing; Marziliano, Pina; Baskaran, Mani; Wong, Hong-Tym; Aung, Tin

2011-11-01

Angle-closure glaucoma is a major blinding eye disease and could be detected by measuring the anterior chamber angle in the human eyes. High-definition OCT (Cirrus HD-OCT) is an emerging noninvasive, high-speed, and high-resolution imaging modality for the anterior segment of the eye. Here, we propose a novel algorithm which automatically detects a new landmark, Schwalbe's line, and measures the anterior chamber angle in the HD-OCT images. The distortion caused by refraction is corrected by dewarping the HD-OCT images, and three biometric measurements are defined to quantitatively assess the anterior chamber angle. The proposed algorithm was tested on 40 HD-OCT images of the eye and provided accurate measurements in about 1 second.

Automatic correspondence detection in mammogram and breast tomosynthesis images

Science.gov (United States)

Ehrhardt, Jan; Krüger, Julia; Bischof, Arpad; Barkhausen, Jörg; Handels, Heinz

2012-02-01

Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with the potential to combine the high contrast and high resolution of digital mammography with the advantages of 3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences between tomosynthesis images and previous mammographic exams of the same women have to be determined. In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram. We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following back projection. The advantages and limitations of both approaches are discussed and the performance of the methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data. Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis, visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for

Automatic classification of defects in weld pipe

International Nuclear Information System (INIS)

Anuar Mikdad Muad; Mohd Ashhar Hj Khalid; Abdul Aziz Mohamad; Abu Bakar Mhd Ghazali; Abdul Razak Hamzah

2000-01-01

With the advancement of computer imaging technology, the image on hard radiographic film can be digitized and stored in a computer and the manual process of defect recognition and classification may be replace by the computer. In this paper a computerized method for automatic detection and classification of common defects in film radiography of weld pipe is described. The detection and classification processes consist of automatic selection of interest area on the image and then classify common defects using image processing and special algorithms. Analysis of the attributes of each defect such as area, size, shape and orientation are carried out by the feature analysis process. These attributes reveal the type of each defect. These methods of defect classification result in high success rate. Our experience showed that sharp film images produced better results

Automatic classification of defects in weld pipe

International Nuclear Information System (INIS)

Anuar Mikdad Muad; Mohd Ashhar Khalid; Abdul Aziz Mohamad; Abu Bakar Mhd Ghazali; Abdul Razak Hamzah

2001-01-01

With the advancement of computer imaging technology, the image on hard radiographic film can be digitized and stored in a computer and the manual process of defect recognition and classification may be replaced by the computer. In this paper, a computerized method for automatic detection and classification of common defects in film radiography of weld pipe is described. The detection and classification processes consist of automatic selection of interest area on the image and then classify common defects using image processing and special algorithms. Analysis of the attributes of each defect such area, size, shape and orientation are carried out by the feature analysis process. These attributes reveal the type of each defect. These methods of defect classification result in high success rate. Our experience showed that sharp film images produced better results. (Author)

Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Directory of Open Access Journals (Sweden)

Xiangyu Zhuo

2017-04-01

Full Text Available Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles. As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

Automatic Tracking Of Remote Sensing Precipitation Data Using Genetic Algorithm Image Registration Based Automatic Morphing: September 1999 Storm Floyd Case Study

Science.gov (United States)

Chiu, L.; Vongsaard, J.; El-Ghazawi, T.; Weinman, J.; Yang, R.; Kafatos, M.

U Due to the poor temporal sampling by satellites, data gaps exist in satellite derived time series of precipitation. This poses a challenge for assimilating rain- fall data into forecast models. To yield a continuous time series, the classic image processing technique of digital image morphing has been used. However, the digital morphing technique was applied manually and that is time consuming. In order to avoid human intervention in the process, an automatic procedure for image morphing is needed for real-time operations. For this purpose, Genetic Algorithm Based Image Registration Automatic Morphing (GRAM) model was developed and tested in this paper. Specifically, automatic morphing technique was integrated with Genetic Algo- rithm and Feature Based Image Metamorphosis technique to fill in data gaps between satellite coverage. The technique was tested using NOWRAD data which are gener- ated from the network of NEXRAD radars. Time series of NOWRAD data from storm Floyd that occurred at the US eastern region on September 16, 1999 for 00:00, 01:00, 02:00,03:00, and 04:00am were used. The GRAM technique was applied to data col- lected at 00:00 and 04:00am. These images were also manually morphed. Images at 01:00, 02:00 and 03:00am were interpolated from the GRAM and manual morphing and compared with the original NOWRAD rainrates. The results show that the GRAM technique outperforms manual morphing. The correlation coefficients between the im- ages generated using manual morphing are 0.905, 0.900, and 0.905 for the images at 01:00, 02:00,and 03:00 am, while the corresponding correlation coefficients are 0.946, 0.911, and 0.913, respectively, based on the GRAM technique. Index terms ­ Remote Sensing, Image Registration, Hydrology, Genetic Algorithm, Morphing, NEXRAD

Automatic analysis of trabecular bone structure from knee MRI

DEFF Research Database (Denmark)

Marques, Joselene; Granlund, Rabia; Lillholm, Martin

2012-01-01

We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic......, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0...

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

Science.gov (United States)

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

2017-11-01

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

Automatic Detection of Microaneurysms in Color Fundus Images using a Local Radon Transform Method

Directory of Open Access Journals (Sweden)

Hamid Reza Pourreza

2009-03-01

Full Text Available Introduction: Diabetic retinopathy (DR is one of the most serious and most frequent eye diseases in the world and the most common cause of blindness in adults between 20 and 60 years of age. Following 15 years of diabetes, about 2% of the diabetic patients are blind and 10% suffer from vision impairment due to DR complications. This paper addresses the automatic detection of microaneurysms (MA in color fundus images, which plays a key role in computer-assisted early diagnosis of diabetic retinopathy. Materials and Methods: The algorithm can be divided into three main steps. The purpose of the first step or pre-processing is background normalization and contrast enhancement of the images. The second step aims to detect candidates, i.e., all patterns possibly corresponding to MA, which is achieved using a local radon transform, Then, features are extracted, which are used in the last step to automatically classify the candidates into real MA or other objects using the SVM method. A database of 100 annotated images was used to test the algorithm. The algorithm was compared to manually obtained gradings of these images. Results: The sensitivity of diagnosis for DR was 100%, with specificity of 90% and the sensitivity of precise MA localization was 97%, at an average number of 5 false positives per image. Discussion and Conclusion: Sensitivity and specificity of this algorithm make it one of the best methods in this field. Using the local radon transform in this algorithm eliminates the noise sensitivity for MA detection in retinal image analysis.

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

Science.gov (United States)

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

2018-02-01

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

Automatic brightness control algorithms and their effect on fluoroscopic imaging

International Nuclear Information System (INIS)

Quinn, P.W.; Gagne, R.M.

1989-01-01

This paper reports a computer model used to investigate the effect on dose and image quality of three automatic brightness control (ABC) algorithms used in the imaging of barium during general-purpose fluoroscopy. A model incorporating all aspects of image formation - i.e., x- ray production, phantom attenuation, and energy absorption in the CSI phosphor - was driven according to each ABC algorithm as a function of patient thickness. The energy absorbed in the phosphor was kept constant, while the changes in exposure, integral dose, organ dose, and contrast were monitored

Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

Science.gov (United States)

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

2013-05-01

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

Multispectral Image Analysis for Astaxanthin Coating Classification

DEFF Research Database (Denmark)

Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

2012-01-01

Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. The pellets were divided into two groups: one with pellets coated using synthetic astaxanthin in fish oil and the other with pellets coated...

Automatic Shadow Detection and Removal from a Single Image.

Science.gov (United States)

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

2016-03-01

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

Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest

Science.gov (United States)

Poczatek, J. Collin; Turck, Christoph W.; Lechene, Claude

2012-01-01

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php. PMID:22347386

Automatic food detection in egocentric images using artificial intelligence technology.

Science.gov (United States)

Jia, Wenyan; Li, Yuecheng; Qu, Ruowei; Baranowski, Thomas; Burke, Lora E; Zhang, Hong; Bai, Yicheng; Mancino, Juliet M; Xu, Guizhi; Mao, Zhi-Hong; Sun, Mingui

2018-03-26

To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

Automatic analysis of quality of images from X-ray digital flat detectors

International Nuclear Information System (INIS)

Le Meur, Y.

2009-04-01

Since last decade, medical imaging has grown up with the development of new digital imaging techniques. In the field of X-ray radiography, new detectors replace progressively older techniques, based on film or x-ray intensifiers. These digital detectors offer a higher sensibility and reduced overall dimensions. This work has been prepared with Trixell, the world leading company in flat detectors for medical radiography. It deals with quality control on digital images stemming from these detectors. High quality standards of medical imaging impose a close analysis of the defects that can appear on the images. This work describes a complete process for quality analysis of such images. A particular focus is given on the detection task of the defects, thanks to methods well adapted to our context of spatially correlated defects in noise background. (author)

Automatic segmentation and disease classification using cardiac cine MR images

NARCIS (Netherlands)

Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A.; Išgum, Ivana

2018-01-01

Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle

Automatic cumulative sums contour detection of FBP-reconstructed multi-object nuclear medicine images.

Science.gov (United States)

Protonotarios, Nicholas E; Spyrou, George M; Kastis, George A

2017-06-01

The problem of determining the contours of objects in nuclear medicine images has been studied extensively in the past, however most of the analysis has focused on a single object as opposed to multiple objects. The aim of this work is to develop an automated method for determining the contour of multiple objects in positron emission tomography (PET) and single photon emission computed tomography (SPECT) filtered backprojection (FBP) reconstructed images. These contours can be used for computing body edges for attenuation correction in PET and SPECT, as well as for eliminating streak artifacts outside the objects, which could be useful in compressive sensing reconstruction. Contour detection has been accomplished by applying a modified cumulative sums (CUSUM) scheme in the sinogram. Our approach automatically detects all objects in the image, without requiring a priori knowledge of the number of distinct objects in the reconstructed image. This method has been tested in simulated phantoms, such as an image-quality (IQ) phantom and two digital multi-object phantoms, as well as a real NEMA phantom and a clinical thoracic study. For this purpose, a GE Discovery PET scanner was employed. The detected contours achieved root mean square accuracy of 1.14 pixels, 1.69 pixels and 3.28 pixels and a Hausdorff distance of 3.13, 3.12 and 4.50 pixels, for the simulated image-quality phantom PET study, the real NEMA phantom and the clinical thoracic study, respectively. These results correspond to a significant improvement over recent results obtained in similar studies. Furthermore, we obtained an optimal sub-pattern assignment (OSPA) localization error of 0.94 and 1.48, for the two-objects and three-objects simulated phantoms, respectively. Our method performs efficiently for sets of convex objects and hence it provides a robust tool for automatic contour determination with precise results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of automatic extraction of the corpus callosum from magnetic resonance imaging of the head and examination of the early dementia objective diagnostic technique in feature analysis

International Nuclear Information System (INIS)

Kodama, Naoki; Kaneko, Tomoyuki

2005-01-01

We examined the objective diagnosis of dementia based on changes in the corpus callosum. We examined midsagittal head MR images of 17 early dementia patients (2 men and 15 women; mean age, 77.2±3.3 years) and 18 healthy elderly controls (2 men and 16 women; mean age, 73.8±6.5 years), 35 subjects altogether. First, the corpus callosum was automatically extracted from the MR images. Next, early dementia was compared with the healthy elderly individuals using 5 features of the straight-line methods, 5 features of the Run-Length Matrix, and 6 features of the Co-occurrence Matrix from the corpus callosum. Automatic extraction of the corpus callosum showed an accuracy rate of 84.1±3.7%. A statistically significant difference was found in 6 of the 16 features between early dementia patients and healthy elderly controls. Discriminant analysis using the 6 features demonstrated a sensitivity of 88.2% and specificity of 77.8%, with an overall accuracy of 82.9%. These results indicate that feature analysis based on changes in the corpus callosum can be used as an objective diagnostic technique for early dementia. (author)

Sensitivity analysis and design optimization through automatic differentiation

International Nuclear Information System (INIS)

Hovland, Paul D; Norris, Boyana; Strout, Michelle Mills; Bhowmick, Sanjukta; Utke, Jean

2005-01-01

Automatic differentiation is a technique for transforming a program or subprogram that computes a function, including arbitrarily complex simulation codes, into one that computes the derivatives of that function. We describe the implementation and application of automatic differentiation tools. We highlight recent advances in the combinatorial algorithms and compiler technology that underlie successful implementation of automatic differentiation tools. We discuss applications of automatic differentiation in design optimization and sensitivity analysis. We also describe ongoing research in the design of language-independent source transformation infrastructures for automatic differentiation algorithms

Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

International Nuclear Information System (INIS)

Hou, Zujun; Huang, Su

2008-01-01

Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized this process and suggest several improvements. To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising phase employs a 3D-extension of the Bayes-Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et al.'s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive comparisons with other sequential or iterative methods have been carried out using simulated and real images. The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed, but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels. A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal, inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF, GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline methods. (orig.)

STUDY OF AUTOMATIC IMAGE RECTIFICATION AND REGISTRATION OF SCANNED HISTORICAL AERIAL PHOTOGRAPHS

Directory of Open Access Journals (Sweden)

H. R. Chen

2016-06-01

Full Text Available Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

Learning representative features for facial images based on a modified principal component analysis

Science.gov (United States)

Averkin, Anton; Potapov, Alexey

2013-05-01

The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Fen Chen

2018-03-01

Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

Automatic landslide detection from LiDAR DTM derivatives by geographic-object-based image analysis based on open-source software

Science.gov (United States)

Knevels, Raphael; Leopold, Philip; Petschko, Helene

2017-04-01

With high-resolution airborne Light Detection and Ranging (LiDAR) data more commonly available, many studies have been performed to facilitate the detailed information on the earth surface and to analyse its limitation. Specifically in the field of natural hazards, digital terrain models (DTM) have been used to map hazardous processes such as landslides mainly by visual interpretation of LiDAR DTM derivatives. However, new approaches are striving towards automatic detection of landslides to speed up the process of generating landslide inventories. These studies usually use a combination of optical imagery and terrain data, and are designed in commercial software packages such as ESRI ArcGIS, Definiens eCognition, or MathWorks MATLAB. The objective of this study was to investigate the potential of open-source software for automatic landslide detection based only on high-resolution LiDAR DTM derivatives in a study area within the federal state of Burgenland, Austria. The study area is very prone to landslides which have been mapped with different methodologies in recent years. The free development environment R was used to integrate open-source geographic information system (GIS) software, such as SAGA (System for Automated Geoscientific Analyses), GRASS (Geographic Resources Analysis Support System), or TauDEM (Terrain Analysis Using Digital Elevation Models). The implemented geographic-object-based image analysis (GEOBIA) consisted of (1) derivation of land surface parameters, such as slope, surface roughness, curvature, or flow direction, (2) finding optimal scale parameter by the use of an objective function, (3) multi-scale segmentation, (4) classification of landslide parts (main scarp, body, flanks) by k-mean thresholding, (5) assessment of the classification performance using a pre-existing landslide inventory, and (6) post-processing analysis for the further use in landslide inventories. The results of the developed open-source approach demonstrated good

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

DEFF Research Database (Denmark)

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

2018-01-01

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

Comparison of human and automatic segmentations of kidneys from CT images

International Nuclear Information System (INIS)

Rao, Manjori; Stough, Joshua; Chi, Y.-Y.; Muller, Keith; Tracton, Gregg; Pizer, Stephen M.; Chaney, Edward L.

2005-01-01

Purpose: A controlled observer study was conducted to compare a method for automatic image segmentation with conventional user-guided segmentation of right and left kidneys from planning computerized tomographic (CT) images. Methods and materials: Deformable shape models called m-reps were used to automatically segment right and left kidneys from 12 target CT images, and the results were compared with careful manual segmentations performed by two human experts. M-rep models were trained based on manual segmentations from a collection of images that did not include the targets. Segmentation using m-reps began with interactive initialization to position the kidney model over the target kidney in the image data. Fully automatic segmentation proceeded through two stages at successively smaller spatial scales. At the first stage, a global similarity transformation of the kidney model was computed to position the model closer to the target kidney. The similarity transformation was followed by large-scale deformations based on principal geodesic analysis (PGA). During the second stage, the medial atoms comprising the m-rep model were deformed one by one. This procedure was iterated until no changes were observed. The transformations and deformations at both stages were driven by optimizing an objective function with two terms. One term penalized the currently deformed m-rep by an amount proportional to its deviation from the mean m-rep derived from PGA of the training segmentations. The second term computed a model-to-image match term based on the goodness of match of the trained intensity template for the currently deformed m-rep with the corresponding intensity data in the target image. Human and m-rep segmentations were compared using quantitative metrics provided in a toolset called Valmet. Metrics reported in this article include (1) percent volume overlap; (2) mean surface distance between two segmentations; and (3) maximum surface separation (Hausdorff distance

Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

Directory of Open Access Journals (Sweden)

Mitko Veta

Full Text Available The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1 pre-processing with color unmixing and morphological operators, 2 marker-controlled watershed segmentation at multiple scales and with different markers, 3 post-processing for rejection of false regions and 4 merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A and tested on a separate validation set of 18 cases (subset B. The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value and segmentation accuracy (Dice coefficient. The mean estimated sensitivity for subset A was 0.875 (±0.092 and for subset B 0.853 (±0.077. The mean estimated positive predictive value was 0.904 (±0.075 and 0.886 (±0.069 for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.

Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

Science.gov (United States)

Veta, Mitko; van Diest, Paul J; Kornegoor, Robert; Huisman, André; Viergever, Max A; Pluim, Josien P W

2013-01-01

The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.

Development of automatic image analysis methods for high-throughput and high-content screening

NARCIS (Netherlands)

Di, Zi

2013-01-01

This thesis focuses on the development of image analysis methods for ultra-high content analysis of high-throughput screens where cellular phenotype responses to various genetic or chemical perturbations that are under investigation. Our primary goal is to deliver efficient and robust image analysis

Comparison of screen-film combinations: results of a contrast detail study and interactive image quality analysis. Pt. 2. Linear assessment of grey scale ranges with interactive (automatic) image analysis

International Nuclear Information System (INIS)

Stamm, G.; Eichbaum, G.; Hagemann, G.

1997-01-01

The following three screen-film combinations were compared: (a) A combination of anticross-over film and UV-light emitting screens, (b) a combination of blue-light emitting screens and film, and (c) a conventional green fluorescing screen-film combination. Radiographs of a specially designed plexiglass phantom (0.2x0.2x0.12 m 3 ) with bar patterns of lead and plaster and of air, respectively were obtained using the following parameters: 12 pulse generator, 0.6 mm focus size, 4.7 mm aluminium prefilter, a grid with 40 lines/cm (12:1) and a focus-detector distance of 1.15 m. Image analysis was performed using an IBAS system and a Zeiss Kontron computer. Display conditions were the following: Display distance 0.12 m, a vario film objective 35/70 (Zeiss), a video camera tube with a Pb0 photocathode, 625 lines (Siemens Heimann), an IBAS image matrix of 512x512 pixels with a resolution of 7 lines/mm, the projected matrix area was 5000 μm 2 . Grey scale ranges were measured on a line perpendicular to the grouped bar patterns. The difference between the maximum and minimum density value served as signal. The spatial resolution of the detector system was measured when the signal value was three times higher than the standard deviation of the means of multiple density measurements. The results showed considerable advantages of the two new screen-film combinations as compared to the conventional screen-film combination. The result was contradictory to the findings with pure visual assessment of thresholds (part I) that had found no differences. The authors concluded that (automatic) interactive image analysis algorithms serve as an objective measure and are specifically advantageous when small differences in image quality are to be evaluated. (orig.) [de

Analysis and clinical usefullness of cardiac ECT images

International Nuclear Information System (INIS)

Hayashi, Makoto; Kagawa, Masaaki; Yamada, Yukinori

1983-01-01

We estimated basically and clinically myocardial ECT image and ECG gated cardiac blood-pool ECT image. ROC curve is used for the evaluation of the accuracy in diagnostic myocardial infarction. The accuracy in diagnostic of MI is superior in myocardial ECT image and ECT estimation is unnecessary skillfulness and experience. We can absene the whole defect of MI than planar image by using ECT. LVEDV between estimated volume and contrast volume is according to it and get one step for automatic analysis of cardiac volume. (author)

Automatic determination of the size of elliptical nanoparticles from AFM images

International Nuclear Information System (INIS)

Sedlář, Jiří; Zitová, Barbara; Kopeček, Jaromír; Flusser, Jan; Todorciuc, Tatiana; Kratochvílová, Irena

2013-01-01

The objective of this work was to develop an accurate method for automatic determination of the size of elliptical nanoparticles from atomic force microscopy (AFM) images that would yield results consistent with results of manual measurements by experts. The proposed method was applied on phenylpyridyldiketopyrrolopyrrole (PPDP), a granular organic material with a wide scale of application and highly sensitive particle-size properties. A PPDP layer consists of similarly sized elliptical particles (c. 100 nm × 50 nm) and its properties can be estimated from the average length and width of the particles. The developed method is based on segmentation of salient particles by the watershed transform and approximation of their shapes by ellipses computed by image moments; it estimates the lengths and widths of the particles by the major and minor axes, respectively, of the corresponding ellipses. Its results proved to be consistent with results of manual measurements by a trained expert. The comparison showed that the developed method could be used in practice for precise automatic measurement of PPDP particles in AFM images

Dual-model automatic detection of nerve-fibres in corneal confocal microscopy images.

Science.gov (United States)

Dabbah, M A; Graham, J; Petropoulos, I; Tavakoli, M; Malik, R A

2010-01-01

Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

Science.gov (United States)

Nguyen, Tan H.; Sridharan, Shamira; Macias, Virgilia; Kajdacsy-Balla, Andre; Melamed, Jonathan; Do, Minh N.; Popescu, Gabriel

2017-03-01

We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.

Semiautomated analysis of embryoscope images: Using localized variance of image intensity to detect embryo developmental stages.

Science.gov (United States)

Mölder, Anna; Drury, Sarah; Costen, Nicholas; Hartshorne, Geraldine M; Czanner, Silvester

2015-02-01

Embryo selection in in vitro fertilization (IVF) treatment has traditionally been done manually using microscopy at intermittent time points during embryo development. Novel technique has made it possible to monitor embryos using time lapse for long periods of time and together with the reduced cost of data storage, this has opened the door to long-term time-lapse monitoring, and large amounts of image material is now routinely gathered. However, the analysis is still to a large extent performed manually, and images are mostly used as qualitative reference. To make full use of the increased amount of microscopic image material, (semi)automated computer-aided tools are needed. An additional benefit of automation is the establishment of standardization tools for embryo selection and transfer, making decisions more transparent and less subjective. Another is the possibility to gather and analyze data in a high-throughput manner, gathering data from multiple clinics and increasing our knowledge of early human embryo development. In this study, the extraction of data to automatically select and track spatio-temporal events and features from sets of embryo images has been achieved using localized variance based on the distribution of image grey scale levels. A retrospective cohort study was performed using time-lapse imaging data derived from 39 human embryos from seven couples, covering the time from fertilization up to 6.3 days. The profile of localized variance has been used to characterize syngamy, mitotic division and stages of cleavage, compaction, and blastocoel formation. Prior to analysis, focal plane and embryo location were automatically detected, limiting precomputational user interaction to a calibration step and usable for automatic detection of region of interest (ROI) regardless of the method of analysis. The results were validated against the opinion of clinical experts. © 2015 International Society for Advancement of Cytometry. © 2015 International

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

Science.gov (United States)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Automatic Radiometric Normalization of Multitemporal Satellite Imagery

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg; Schmidt, Michael

2004-01-01

with normalization using orthogonal regression. The procedure is applied to Landsat TM images over Nevada, Landsat ETM+ images over Morocco, and SPOT HRV images over Kenya. Results from this new automatic, combined MAD/orthogonal regression method, based on statistical analysis of test pixels not used in the actual...

A prototype distributed object-oriented architecture for image-based automatic laser alignment

International Nuclear Information System (INIS)

Stout, E.A.; Kamm, V.J.M.; Spann, J.M.; Van Arsdall, P.J.

1996-01-01

Designing a computer control system for the National Ignition Facility (NIF) is a complex undertaking because of the system's large size and its distributed nature. The controls team is addressing that complexity by adopting the object-oriented programming paradigm, designing reusable software frameworks, and using the Common Object Request Broker Architecture (CORBA) for distribution. A prototype system for image-based automatic laser alignment has been developed to evaluate and gain experience with CORBA and OOP in a small distributed system. The prototype is also important in evaluating alignment concepts, image processing techniques, speed and accuracy of automatic alignment objectives for the NIF, and control hardware for aligment devices. The prototype system has met its inital objectives and provides a basis for continued development

Automatic Matching of Large Scale Images and Terrestrial LIDAR Based on App Synergy of Mobile Phone

Science.gov (United States)

Xia, G.; Hu, C.

2018-04-01

The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.

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

Science.gov (United States)

Deng, Zhipeng; Lei, Lin; Zhou, Shilin

2015-10-01

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

Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

Science.gov (United States)

Link, Daphna; Braginsky, Michael B; Joskowicz, Leo; Ben Sira, Liat; Harel, Shaul; Many, Ariel; Tarrasch, Ricardo; Malinger, Gustavo; Artzi, Moran; Kapoor, Cassandra; Miller, Elka; Ben Bashat, Dafna

2018-01-01

Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). The developed method showed high correlation with manual segmentation (r2 = 0.9183, p user independent, applicable with retrospective data, and is suggested for use in routine clinical practice. © 2017 S. Karger AG, Basel.

Dynamic Analysis of a Pendulum Dynamic Automatic Balancer

Directory of Open Access Journals (Sweden)

Jin-Seung Sohn

2007-01-01

Full Text Available The automatic dynamic balancer is a device to reduce the vibration from unbalanced mass of rotors. Instead of considering prevailing ball automatic dynamic balancer, pendulum automatic dynamic balancer is analyzed. For the analysis of dynamic stability and behavior, the nonlinear equations of motion for a system are derived with respect to polar coordinates by the Lagrange's equations. The perturbation method is applied to investigate the dynamic behavior of the system around the equilibrium position. Based on the linearized equations, the dynamic stability of the system around the equilibrium positions is investigated by the eigenvalue analysis.

Visual mismatch negativity indicates automatic, task-independent detection of artistic image composition in abstract artworks.

Science.gov (United States)

Menzel, Claudia; Kovács, Gyula; Amado, Catarina; Hayn-Leichsenring, Gregor U; Redies, Christoph

2018-05-06

In complex abstract art, image composition (i.e., the artist's deliberate arrangement of pictorial elements) is an important aesthetic feature. We investigated whether the human brain detects image composition in abstract artworks automatically (i.e., independently of the experimental task). To this aim, we studied whether a group of 20 original artworks elicited a visual mismatch negativity when contrasted with a group of 20 images that were composed of the same pictorial elements as the originals, but in shuffled arrangements, which destroy artistic composition. We used a passive oddball paradigm with parallel electroencephalogram recordings to investigate the detection of image type-specific properties. We observed significant deviant-standard differences for the shuffled and original images, respectively. Furthermore, for both types of images, differences in amplitudes correlated with the behavioral ratings of the images. In conclusion, we show that the human brain can detect composition-related image properties in visual artworks in an automatic fashion. Copyright © 2018 Elsevier B.V. All rights reserved.

An image based system to automatically and objectivelly score the degreeof redness and scaling in psoriasi lesions

DEFF Research Database (Denmark)

Gomez, David Delgado; Ersbøll, Bjarne Kjær; Carstensen, Jens Michael

2004-01-01

In this work, a combined statistical and image analysis method to automatically evaluate the severity of scaling in psoriasis lesions is proposed. The method separates the different regions of the disease in the image and scores the degree of scaling based on the properties of these areas. The pr...... that the obtained scores are highly correlated with scores made by doctors. This and the fact that the obtained measures are continuous indicate the proposed method is a suitable tool to evaluate the lesion and to track the evolution of dermatological diseases....

Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

Science.gov (United States)

Xiao, Zhitao; Zhang, Xinpeng; Geng, Lei; Zhang, Fang; Wu, Jun; Tong, Jun; Ogunbona, Philip O; Shan, Chunyan

2017-10-26

Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

International Nuclear Information System (INIS)

Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sebastien

2007-01-01

The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis

Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

Energy Technology Data Exchange (ETDEWEB)

Fripp, Jurgen [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia); Crozier, Stuart [School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD 4072 (Australia); Warfield, Simon K [Computational Radiology Laboratory, Harvard Medical School, Children' s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 (United States); Ourselin, Sebastien [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia)

2007-03-21

The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

Automatic Detection of Changes on Mars Surface from High-Resolution Orbital Images

Science.gov (United States)

Sidiropoulos, Panagiotis; Muller, Jan-Peter

2017-04-01

Over the last 40 years Mars has been extensively mapped by several NASA and ESA orbital missions, generating a large image dataset comprised of approximately 500,000 high-resolution images (of citizen science can be employed for training and verification it is unsuitable for planetwide systematic change detection. In this work, we introduce a novel approach in planetary image change detection, which involves a batch-mode automatic change detection pipeline that identifies regions that have changed. This is tested in anger, on tens of thousands of high-resolution images over the MC11 quadrangle [5], acquired by CTX, HRSC, THEMIS-VIS and MOC-NA instruments [1]. We will present results which indicate a substantial level of activity in this region of Mars, including instances of dynamic natural phenomena that haven't been cataloged in the planetary science literature before. We will demonstrate the potential and usefulness of such an automatic approach in planetary science change detection. Acknowledgments: The research leading to these results has received funding from the STFC "MSSL Consolidated Grant" ST/K000977/1 and partial support from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement n° 607379. References: [1] P. Sidiropoulos and J. - P. Muller (2015) On the status of orbital high-resolution repeat imaging of Mars for the observation of dynamic surface processes. Planetary and Space Science, 117: 207-222. [2] O. Aharonson, et al. (2003) Slope streak formation and dust deposition rates on Mars. Journal of Geophysical Research: Planets, 108(E12):5138 [3] A. McEwen, et al. (2011) Seasonal flows on warm martian slopes. Science, 333 (6043): 740-743. [4] S. Byrne, et al. (2009) Distribution of mid-latitude ground ice on mars from new impact craters. Science, 325(5948):1674-1676. [5] K. Gwinner, et al (2016) The High Resolution Stereo Camera (HRSC) of Mars Express and its approach to science analysis and mapping for Mars and

Automatic segmentation and 3-dimensional display based on the knowledge of head MRI images

International Nuclear Information System (INIS)

Suzuki, Hidetomo; Toriwaki, Jun-ichiro.

1987-01-01

In this paper we present a procedure which automatically extracts soft tissues, such as subcutaneous fat, brain, and cerebral ventricle, from the multislice MRI images of head region, and displays their 3-dimensional images. Segmentation of soft tissues is done by use of an iterative thresholding. In order to select the optimum threshold value automatically, we introduce a measure to evaluate the goodness of segmentation into this procedure. When the measure satisfies given conditions, iteration of thresholding terminates, and the final result of segmentation is extracted by using the current threshold value. Since this procedure can execute segmentation and calculation of the goodness measure in each slice automatically, it remarkably decreases efforts of users. Moreover, the 3-dimensional display of the segmented tissues shows that this procedure can extract the shape of each soft tissue with reasonable precision for clinical use. (author)

A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

International Nuclear Information System (INIS)

Benkirane, A.; Auger, G.; Chbihi, A.; Bloyet, D.; Plagnol, E.

1994-01-01

This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ''classical'' automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append

A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

Energy Technology Data Exchange (ETDEWEB)

Benkirane, A; Auger, G; Chbihi, A [Grand Accelerateur National d` Ions Lourds (GANIL), 14 - Caen (France); Bloyet, D [Caen Univ., 14 (France); Plagnol, E [Paris-11 Univ., 91 - Orsay (France). Inst. de Physique Nucleaire

1994-12-31

This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ``classical`` automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append.

An application of image processing techniques in computed tomography image analysis

DEFF Research Database (Denmark)

McEvoy, Fintan

2007-01-01

number of animals and image slices, automation of the process was desirable. The open-source and free image analysis program ImageJ was used. A macro procedure was created that provided the required functionality. The macro performs a number of basic image processing procedures. These include an initial...... process designed to remove the scanning table from the image and to center the animal in the image. This is followed by placement of a vertical line segment from the mid point of the upper border of the image to the image center. Measurements are made between automatically detected outer and inner...... boundaries of subcutaneous adipose tissue along this line segment. This process was repeated as the image was rotated (with the line position remaining unchanged) so that measurements around the complete circumference were obtained. Additionally, an image was created showing all detected boundary points so...

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

Science.gov (United States)

Liu, Xi; Zhou, Mei; Qiu, Song; Sun, Li; Liu, Hongying; Li, Qingli; Wang, Yiting

2017-12-01

Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

Neural-network classifiers for automatic real-world aerial image recognition

Science.gov (United States)

Greenberg, Shlomo; Guterman, Hugo

1996-08-01

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Automatic anatomy recognition on CT images with pathology

Science.gov (United States)

Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Semi-automatic construction of reference standards for evaluation of image registration

NARCIS (Netherlands)

Murphy, K.; Ginneken, van B.; Klein, S.; Staring, M.; Hoop, de B.J.; Viergever, M.A.; Pluim, J.P.W.

2011-01-01

Quantitative evaluation of image registration algorithms is a difficult and under-addressed issue due to the lack of a reference standard in most registration problems. In this work a method is presented whereby detailed reference standard data may be constructed in an efficient semi-automatic

Some selected quantitative methods of thermal image analysis in Matlab.

Science.gov (United States)

Koprowski, Robert

2016-05-01

The paper presents a new algorithm based on some selected automatic quantitative methods for analysing thermal images. It shows the practical implementation of these image analysis methods in Matlab. It enables to perform fully automated and reproducible measurements of selected parameters in thermal images. The paper also shows two examples of the use of the proposed image analysis methods for the area of ​​the skin of a human foot and face. The full source code of the developed application is also provided as an attachment. The main window of the program during dynamic analysis of the foot thermal image. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

International Nuclear Information System (INIS)

Feng Shuli; Peng Mingchen; Li Kuncheng

2004-01-01

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

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

Science.gov (United States)

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

2015-03-01

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

A workflow for the automatic segmentation of organelles in electron microscopy image stacks

Science.gov (United States)

Perez, Alex J.; Seyedhosseini, Mojtaba; Deerinck, Thomas J.; Bushong, Eric A.; Panda, Satchidananda; Tasdizen, Tolga; Ellisman, Mark H.

2014-01-01

Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime. PMID:25426032

An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

Science.gov (United States)

Lee, Unseok; Chang, Sungyul; Putra, Gian Anantrio; Kim, Hyoungseok; Kim, Dong Hwan

2018-01-01

A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

Combining Stereo SECCHI COR2 and HI1 Images for Automatic CME Front Edge Tracking

Science.gov (United States)

Kirnosov, Vladimir; Chang, Lin-Ching; Pulkkinen, Antti

2016-01-01

COR2 coronagraph images are the most commonly used data for coronal mass ejection (CME) analysis among the various types of data provided by the STEREO (Solar Terrestrial Relations Observatory) SECCHI (Sun-Earth Connection Coronal and Heliospheric Investigation) suite of instruments. The field of view (FOV) in COR2 images covers 215 solar radii (Rs) that allow for tracking the front edge of a CME in its initial stage to forecast the lead-time of a CME and its chances of reaching the Earth. However, estimating the lead-time of a CME using COR2 images gives a larger lead-time, which may be associated with greater uncertainty. To reduce this uncertainty, CME front edge tracking should be continued beyond the FOV of COR2 images. Therefore, heliospheric imager (HI1) data that covers 1590 Rs FOV must be included. In this paper, we propose a novel automatic method that takes both COR2 and HI1 images into account and combine the results to track the front edges of a CME continuously. The method consists of two modules: pre-processing and tracking. The pre-processing module produces a set of segmented images, which contain the signature of a CME, for both COR2 and HI1 separately. In addition, the HI1 images are resized and padded, so that the center of the Sun is the central coordinate of the resized HI1 images. The resulting COR2 andHI1 image set is then fed into the tracking module to estimate the position angle (PA) and track the front edge of a CME. The detected front edge is then used to produce a height-time profile that is used to estimate the speed of a CME. The method was validated using 15 CME events observed in the period from January 1, 2008 to August 31, 2009. The results demonstrate that the proposed method is effective for CME front edge tracking in both COR2 and HI1 images. Using this method, the CME front edge can now be tracked automatically and continuously in a much larger range, i.e., from 2 to 90 Rs, for the first time. These improvement scan greatly

Automatic Microaneurysm Detection and Characterization Through Digital Color Fundus Images

Energy Technology Data Exchange (ETDEWEB)

Martins, Charles; Veras, Rodrigo; Ramalho, Geraldo; Medeiros, Fatima; Ushizima, Daniela

2008-08-29

Ocular fundus images can provide information about retinal, ophthalmic, and even systemic diseases such as diabetes. Microaneurysms (MAs) are the earliest sign of Diabetic Retinopathy, a frequently observed complication in both type 1 and type 2 diabetes. Robust detection of MAs in digital color fundus images is critical in the development of automated screening systems for this kind of disease. Automatic grading of these images is being considered by health boards so that the human grading task is reduced. In this paper we describe segmentation and the feature extraction methods for candidate MAs detection.We show that the candidate MAs detected with the methodology have been successfully classified by a MLP neural network (correct classification of 84percent).

AN AUTOMATIC PROCEDURE FOR COMBINING DIGITAL IMAGES AND LASER SCANNER DATA

Directory of Open Access Journals (Sweden)

W. Moussa

2012-07-01

Full Text Available Besides improving both the geometry and the visual quality of the model, the integration of close-range photogrammetry and terrestrial laser scanning techniques directs at filling gaps in laser scanner point clouds to avoid modeling errors, reconstructing more details in higher resolution and recovering simple structures with less geometric details. Thus, within this paper a flexible approach for the automatic combination of digital images and laser scanner data is presented. Our approach comprises two methods for data fusion. The first method starts by a marker-free registration of digital images based on a point-based environment model (PEM of a scene which stores the 3D laser scanner point clouds associated with intensity and RGB values. The PEM allows the extraction of accurate control information for the direct computation of absolute camera orientations with redundant information by means of accurate space resection methods. In order to use the computed relations between the digital images and the laser scanner data, an extended Helmert (seven-parameter transformation is introduced and its parameters are estimated. Precedent to that, in the second method, the local relative orientation parameters of the camera images are calculated by means of an optimized Structure and Motion (SaM reconstruction method. Then, using the determined transformation parameters results in having absolute oriented images in relation to the laser scanner data. With the resulting absolute orientations we have employed robust dense image reconstruction algorithms to create oriented dense image point clouds, which are automatically combined with the laser scanner data to form a complete detailed representation of a scene. Examples of different data sets are shown and experimental results demonstrate the effectiveness of the presented procedures.

Automatic intra-modality brain image registration method

International Nuclear Information System (INIS)

Whitaker, J.M.; Ardekani, B.A.; Braun, M.

1996-01-01

Full text: Registration of 3D images of brain of the same or different subjects has potential importance in clinical diagnosis, treatment planning and neurological research. The broad aim of our work is to produce an automatic and robust intra-modality, brain image registration algorithm for intra-subject and inter-subject studies. Our algorithm is composed of two stages. Initial alignment is achieved by finding the values of nine transformation parameters (representing translation, rotation and scale) that minimise the nonoverlapping regions of the head. This is achieved by minimisation of the sum of the exclusive OR of two binary head images, produced using the head extraction procedure described by Ardekani et al. (J Comput Assist Tomogr, 19:613-623, 1995). The initial alignment successfully determines the scale parameters and gross translation and rotation parameters. Fine alignment uses an objective function described for inter-modality registration in Ardekani et al. (ibid.). The algorithm segments one of the images to be aligned into a set of connected components using K-means clustering. Registration is achieved by minimising the K-means variance of the segmentation induced in the other image. Similarity of images of the same modality makes the method attractive for intra-modality registration. A 3D MR image, with voxel dimensions, 2x2x6 mm, was misaligned. The registered image shows visually accurate registration. The average displacement of a pixel from its correct location was measured to be 3.3 mm. The algorithm was tested on intra-subject MR images and was found to produce good qualitative results. Using the data available, the algorithm produced promising qualitative results in intra-subject registration. Further work is necessary in its application to intersubject registration, due to large variability in brain structure between subjects. Clinical evaluation of the algorithm for selected applications is required

Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images

Science.gov (United States)

Suzani, Amin; Rasoulian, Abtin; Seitel, Alexander; Fels, Sidney; Rohling, Robert N.; Abolmaesumi, Purang

2015-03-01

This paper proposes an automatic method for vertebra localization, labeling, and segmentation in multi-slice Magnetic Resonance (MR) images. Prior work in this area on MR images mostly requires user interaction while our method is fully automatic. Cubic intensity-based features are extracted from image voxels. A deep learning approach is used for simultaneous localization and identification of vertebrae. The localized points are refined by local thresholding in the region of the detected vertebral column. Thereafter, a statistical multi-vertebrae model is initialized on the localized vertebrae. An iterative Expectation Maximization technique is used to register the vertebral body of the model to the image edges and obtain a segmentation of the lumbar vertebral bodies. The method is evaluated by applying to nine volumetric MR images of the spine. The results demonstrate 100% vertebra identification and a mean surface error of below 2.8 mm for 3D segmentation. Computation time is less than three minutes per high-resolution volumetric image.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

Science.gov (United States)

Han, Guanghui; Liu, Xiabi; Zheng, Guangyuan; Wang, Murong; Huang, Shan

2018-06-06

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis

Multi-atlas-based automatic 3D segmentation for prostate brachytherapy in transrectal ultrasound images

Science.gov (United States)

Nouranian, Saman; Mahdavi, S. Sara; Spadinger, Ingrid; Morris, William J.; Salcudean, S. E.; Abolmaesumi, P.

2013-03-01

One of the commonly used treatment methods for early-stage prostate cancer is brachytherapy. The standard of care for planning this procedure is segmentation of contours from transrectal ultrasound (TRUS) images, which closely follow the prostate boundary. This process is currently performed either manually or using semi-automatic techniques. This paper introduces a fully automatic segmentation algorithm which uses a priori knowledge of contours in a reference data set of TRUS volumes. A non-parametric deformable registration method is employed to transform the atlas prostate contours to a target image coordinates. All atlas images are sorted based on their registration results and the highest ranked registration results are selected for decision fusion. A Simultaneous Truth and Performance Level Estimation algorithm is utilized to fuse labels from registered atlases and produce a segmented target volume. In this experiment, 50 patient TRUS volumes are obtained and a leave-one-out study on TRUS volumes is reported. We also compare our results with a state-of-the-art semi-automatic prostate segmentation method that has been clinically used for planning prostate brachytherapy procedures and we show comparable accuracy and precision within clinically acceptable runtime.

Automatic segmentation of rotational x-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures.

Science.gov (United States)

Manzke, Robert; Meyer, Carsten; Ecabert, Olivier; Peters, Jochen; Noordhoek, Niels J; Thiagalingam, Aravinda; Reddy, Vivek Y; Chan, Raymond C; Weese, Jürgen

2010-02-01

Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of approximately 4 -10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing 1) automatic and manual segmentations of intra-procedural 3-D RA data, 2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and 3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of 1) approximately 1.3 mm compared with manual 3-D RA segmentations 2) approximately 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and 3

Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

NARCIS (Netherlands)

Weijers, G.; Starke, A.; Haudum, A.; Thijssen, J.M.; Rehage, J.; Korte, C.L. de

2010-01-01

The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT

Energy Technology Data Exchange (ETDEWEB)

Scholtz, Jan-Erik, E-mail: janerikscholtz@gmail.com; Wichmann, Julian L.; Kaup, Moritz; Fischer, Sebastian; Kerl, J. Matthias; Lehnert, Thomas; Vogl, Thomas J.; Bauer, Ralf W.

2015-03-15

Highlights: •Automatic segmentation and labeling of the thoracolumbar spine. •Automatically generated double-angulated and aligned axial images of spine segments. •High grade of accurateness for the symmetric depiction of anatomical structures. •Time-saving and may improve workflow in daily practice. -- Abstract: Objectives: To evaluate software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine on CT in terms of accuracy, potential for time savings and workflow improvement. Material and methods: 77 patients (28 women, 49 men, mean age 65.3 ± 14.4 years) with known or suspected spinal disorders (degenerative spine disease n = 32; disc herniation n = 36; traumatic vertebral fractures n = 9) underwent 64-slice MDCT with thin-slab reconstruction. Time for automatic labeling of the thoracolumbar spine and reconstruction of double-angulated axial images of the pathological vertebrae was compared with manually performed reconstruction of anatomical aligned axial images. Reformatted images of both reconstruction methods were assessed by two observers regarding accuracy of symmetric depiction of anatomical structures. Results: In 33 cases double-angulated axial images were created in 1 vertebra, in 28 cases in 2 vertebrae and in 16 cases in 3 vertebrae. Correct automatic labeling was achieved in 72 of 77 patients (93.5%). Errors could be manually corrected in 4 cases. Automatic labeling required 1 min in average. In cases where anatomical aligned axial images of 1 vertebra were created, reconstructions made by hand were significantly faster (p < 0.05). Automatic reconstruction was time-saving in cases of 2 and more vertebrae (p < 0.05). Both reconstruction methods revealed good image quality with excellent inter-observer agreement. Conclusion: The evaluated software for automatic labeling and anatomically aligned, double-angulated axial image reconstruction of the thoracolumbar spine on CT is time

Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images.

Science.gov (United States)

Sanz-Requena, Roberto; Prats-Montalbán, José Manuel; Martí-Bonmatí, Luis; Alberich-Bayarri, Ángel; García-Martí, Gracián; Pérez, Rosario; Ferrer, Alberto

2015-08-01

To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate. © 2014 Wiley Periodicals, Inc.

MR-based automatic delineation of volumes of interest in human brain PET images using probability maps

DEFF Research Database (Denmark)

Svarer, Claus; Madsen, Karina; Hasselbalch, Steen G.

2005-01-01

subjects' MR-images, where VOI sets have been defined manually. High-resolution structural MR-images and 5-HT(2A) receptor binding PET-images (in terms of (18)F-altanserin binding) from 10 healthy volunteers and 10 patients with mild cognitive impairment were included for the analysis. A template including...... 35 VOIs was manually delineated on the subjects' MR images. Through a warping algorithm template VOI sets defined from each individual were transferred to the other subjects MR-images and the voxel overlap was compared to the VOI set specifically drawn for that particular individual. Comparisons were...... delineation of the VOI set. The approach was also shown to work equally well in individuals with pronounced cerebral atrophy. Probability-map-based automatic delineation of VOIs is a fast, objective, reproducible, and safe way to assess regional brain values from PET or SPECT scans. In addition, the method...

Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

Energy Technology Data Exchange (ETDEWEB)

Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Garg, Seema [University of North Carolina; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

2011-01-01

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

Automatic block-matching registration to improve lung tumor localization during image-guided radiotherapy

Science.gov (United States)

Robertson, Scott Patrick

To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed "blocks", are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.

Automated image analysis of atomic force microscopy images of rotavirus particles

International Nuclear Information System (INIS)

Venkataraman, S.; Allison, D.P.; Qi, H.; Morrell-Falvey, J.L.; Kallewaard, N.L.; Crowe, J.E.; Doktycz, M.J.

2006-01-01

A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM

Automated image analysis of atomic force microscopy images of rotavirus particles

Energy Technology Data Exchange (ETDEWEB)

Venkataraman, S. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Allison, D.P. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Biochemistry, Cellular, and Molecular Biology, University of Tennessee, Knoxville, TN 37996 (United States); Molecular Imaging Inc. Tempe, AZ, 85282 (United States); Qi, H. [Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Morrell-Falvey, J.L. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Kallewaard, N.L. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Crowe, J.E. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Doktycz, M.J. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States)]. E-mail: doktyczmj@ornl.gov

2006-06-15

A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM.

Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging

Science.gov (United States)

Thresholding is an important step in the segmentation of image features, and the existing methods are not all effective when the image histogram exhibits a unimodal pattern, which is common in defect detection of fruit. This study was aimed at developing a general automatic thresholding methodology ...

Automatic extraction of via in the CT image of PCB

Science.gov (United States)

Liu, Xifeng; Hu, Yuwei

2018-04-01

In modern industry, the nondestructive testing of printed circuit board (PCB) can prevent effectively the system failure and is becoming more and more important. In order to detect the via in the PCB base on the CT image automatically accurately and reliably, a novel algorithm for via extraction based on weighting stack combining the morphologic character of via is designed. Every slice data in the vertical direction of the PCB is superimposed to enhanced vias target. The OTSU algorithm is used to segment the slice image. OTSU algorithm of thresholding gray level images is efficient for separating an image into two classes where two types of fairly distinct classes exist in the image. Randomized Hough Transform was used to locate the region of via in the segmented binary image. Then the 3D reconstruction of via based on sequence slice images was done by volume rendering. The accuracy of via positioning and detecting from a CT images of PCB was demonstrated by proposed algorithm. It was found that the method is good in veracity and stability for detecting of via in three dimensional.

Automatic teeth axes calculation for well-aligned teeth using cost profile analysis along teeth center arch.

Science.gov (United States)

Kim, Gyehyun; Lee, Jeongjin; Seo, Jinwook; Lee, Wooshik; Shin, Yeong-Gil; Kim, Bohyoung

2012-04-01

In dental implantology and virtual dental surgery planning using computed tomography (CT) images, the examination of the axes of neighboring and/or biting teeth is important to improve the performance of the masticatory system as well as the aesthetic beauty. However, due to its high connectivity to neighboring teeth and jawbones, a tooth and/or its axis is very elusive to automatically identify in dental CT images. This paper presents a novel method of automatically calculating individual teeth axes. The planes separating the individual teeth are automatically calculated using cost profile analysis along the teeth center arch. In this calculation, a novel plane cost function, which considers the intensity and the gradient, is proposed to favor the teeth separation planes crossing the teeth interstice and suppress the possible inappropriately detected separation planes crossing the soft pulp. The soft pulp and dentine of each individually separated tooth are then segmented by a fast marching method with two newly proposed speed functions considering their own specific anatomical characteristics. The axis of each tooth is finally calculated using principal component analysis on the segmented soft pulp and dentine. In experimental results using 20 clinical datasets, the average angle and minimum distance differences between the teeth axes manually specified by two dentists and automatically calculated by the proposed method were 1.94° ± 0.61° and 1.13 ± 0.56 mm, respectively. The proposed method identified the individual teeth axes accurately, demonstrating that it can give dentists substantial assistance during dental surgery such as dental implant placement and orthognathic surgery.

Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software

Directory of Open Access Journals (Sweden)

Tânia Perestrelo

2017-08-01

Full Text Available Image-based assays, such as alkaline phosphatase staining or immunocytochemistry for pluripotent markers, are common methods used in the stem cell field to assess pluripotency. Although an increased number of image-analysis approaches have been described, there is still a lack of software availability to automatically quantify pluripotency in large images after pluripotency staining. To address this need, we developed a robust and rapid image processing software, Pluri-IQ, which allows the automatic evaluation of pluripotency in large low-magnification images. Using mouse embryonic stem cells (mESC as a model, we combined an automated segmentation algorithm with a supervised machine-learning platform to classify colonies as pluripotent, mixed, or differentiated. In addition, Pluri-IQ allows the automatic comparison between different culture conditions. This efficient user-friendly open-source software can be easily implemented in images derived from pluripotent cells or cells that express pluripotent markers (e.g., OCT4-GFP and can be routinely used, decreasing image assessment bias.

Automatic Picking of Foraminifera: Design of the Foraminifera Image Recognition and Sorting Tool (FIRST) Prototype and Results of the Image Classification Scheme

Science.gov (United States)

de Garidel-Thoron, T.; Marchant, R.; Soto, E.; Gally, Y.; Beaufort, L.; Bolton, C. T.; Bouslama, M.; Licari, L.; Mazur, J. C.; Brutti, J. M.; Norsa, F.

2017-12-01

Foraminifera tests are the main proxy carriers for paleoceanographic reconstructions. Both geochemical and taxonomical studies require large numbers of tests to achieve statistical relevance. To date, the extraction of foraminifera from the sediment coarse fraction is still done by hand and thus time-consuming. Moreover, the recognition of morphotypes, ecologically relevant, requires some taxonomical skills not easily taught. The automatic recognition and extraction of foraminifera would largely help paleoceanographers to overcome these issues. Recent advances in automatic image classification using machine learning opens the way to automatic extraction of foraminifera. Here we detail progress on the design of an automatic picking machine as part of the FIRST project. The machine handles 30 pre-sieved samples (100-1000µm), separating them into individual particles (including foraminifera) and imaging each in pseudo-3D. The particles are classified and specimens of interest are sorted either for Individual Foraminifera Analyses (44 per slide) and/or for classical multiple analyses (8 morphological classes per slide, up to 1000 individuals per hole). The classification is based on machine learning using Convolutional Neural Networks (CNNs), similar to the approach used in the coccolithophorid imaging system SYRACO. To prove its feasibility, we built two training image datasets of modern planktonic foraminifera containing approximately 2000 and 5000 images each, corresponding to 15 & 25 morphological classes. Using a CNN with a residual topology (ResNet) we achieve over 95% correct classification for each dataset. We tested the network on 160,000 images from 45 depths of a sediment core from the Pacific ocean, for which we have human counts. The current algorithm is able to reproduce the downcore variability in both Globigerinoides ruber and the fragmentation index (r2 = 0.58 and 0.88 respectively). The FIRST prototype yields some promising results for high

Development of automatic navigation measuring system using template-matching software in image guided neurosurgery

International Nuclear Information System (INIS)

Watanabe, Yohei; Hayashi, Yuichiro; Fujii, Masazumi; Wakabayashi, Toshihiko; Kimura, Miyuki; Tsuzaka, Masatoshi; Sugiura, Akihiro

2010-01-01

An image-guided neurosurgery and neuronavigation system based on magnetic resonance imaging has been used as an indispensable tool for resection of brain tumors. Therefore, accuracy of the neuronavigation system, provided by periodic quality assurance (QA), is essential for image-guided neurosurgery. Two types of accuracy index, fiducial registration error (FRE) and target registration error (TRE), have been used to evaluate navigation accuracy. FRE shows navigation accuracy on points that have been registered. On the other hand, TRE shows navigation accuracy on points such as tumor, skin, and fiducial markers. This study shows that TRE is more reliable than FRE. However, calculation of TRE is a time-consuming, subjective task. Software for QA was developed to compute TRE. This software calculates TRE automatically by an image processing technique, such as automatic template matching. TRE was calculated by the software and compared with the results obtained by manual calculation. Using the software made it possible to achieve a reliable QA system. (author)

Fully automatic and reference-marker-free image stitching method for full-spine and full-leg imaging with computed radiography

Science.gov (United States)

Wang, Xiaohui; Foos, David H.; Doran, James; Rogers, Michael K.

2004-05-01

Full-leg and full-spine imaging with standard computed radiography (CR) systems requires several cassettes/storage phosphor screens to be placed in a staggered arrangement and exposed simultaneously to achieve an increased imaging area. A method has been developed that can automatically and accurately stitch the acquired sub-images without relying on any external reference markers. It can detect and correct the order, orientation, and overlap arrangement of the subimages for stitching. The automatic determination of the order, orientation, and overlap arrangement of the sub-images consists of (1) constructing a hypothesis list that includes all cassette/screen arrangements, (2) refining hypotheses based on a set of rules derived from imaging physics, (3) correlating each consecutive sub-image pair in each hypothesis and establishing an overall figure-of-merit, (4) selecting the hypothesis of maximum figure-of-merit. The stitching process requires the CR reader to over scan each CR screen so that the screen edges are completely visible in the acquired sub-images. The rotational displacement and vertical displacement between two consecutive sub-images are calculated by matching the orientation and location of the screen edge in the front image and its corresponding shadow in the back image. The horizontal displacement is estimated by maximizing the correlation function between the two image sections in the overlap region. Accordingly, the two images are stitched together. This process is repeated for the newly stitched composite image and the next consecutive sub-image until a full-image composite is created. The method has been evaluated in both phantom experiments and clinical studies. The standard deviation of image misregistration is below one image pixel.

Fast and Automatic Ultrasound Simulation from CT Images

Directory of Open Access Journals (Sweden)

Weijian Cong

2013-01-01

Full Text Available Ultrasound is currently widely used in clinical diagnosis because of its fast and safe imaging principles. As the anatomical structures present in an ultrasound image are not as clear as CT or MRI. Physicians usually need advance clinical knowledge and experience to distinguish diseased tissues. Fast simulation of ultrasound provides a cost-effective way for the training and correlation of ultrasound and the anatomic structures. In this paper, a novel method is proposed for fast simulation of ultrasound from a CT image. A multiscale method is developed to enhance tubular structures so as to simulate the blood flow. The acoustic response of common tissues is generated by weighted integration of adjacent regions on the ultrasound propagation path in the CT image, from which parameters, including attenuation, reflection, scattering, and noise, are estimated simultaneously. The thin-plate spline interpolation method is employed to transform the simulation image between polar and rectangular coordinate systems. The Kaiser window function is utilized to produce integration and radial blurring effects of multiple transducer elements. Experimental results show that the developed method is very fast and effective, allowing realistic ultrasound to be fast generated. Given that the developed method is fully automatic, it can be utilized for ultrasound guided navigation in clinical practice and for training purpose.

Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

Science.gov (United States)

Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

2010-03-01

Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

Automatic Registration Method for Fusion of ZY-1-02C Satellite Images

Directory of Open Access Journals (Sweden)

Qi Chen

2013-12-01

Full Text Available Automatic image registration (AIR has been widely studied in the fields of medical imaging, computer vision, and remote sensing. In various cases, such as image fusion, high registration accuracy should be achieved to meet application requirements. For satellite images, the large image size and unstable positioning accuracy resulting from the limited manufacturing technology of charge-coupled device, focal plane distortion, and unrecorded spacecraft jitter lead to difficulty in obtaining agreeable corresponding points for registration using only area-based matching or feature-based matching. In this situation, a coarse-to-fine matching strategy integrating two types of algorithms is proven feasible and effective. In this paper, an AIR method for application to the fusion of ZY-1-02C satellite imagery is proposed. First, the images are geometrically corrected. Coarse matching, based on scale invariant feature transform, is performed for the subsampled corrected images, and a rough global estimation is made with the matching results. Harris feature points are then extracted, and the coordinates of the corresponding points are calculated according to the global estimation results. Precise matching is conducted, based on normalized cross correlation and least squares matching. As complex image distortion cannot be precisely estimated, a local estimation using the structure of triangulated irregular network is applied to eliminate the false matches. Finally, image resampling is conducted, based on local affine transformation, to achieve high-precision registration. Experiments with ZY-1-02C datasets demonstrate that the accuracy of the proposed method meets the requirements of fusion application, and its efficiency is also suitable for the commercial operation of the automatic satellite data process system.

Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

Science.gov (United States)

Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

2017-03-01

Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images

Science.gov (United States)

Lingley-Papadopoulos, Colleen A.; Loew, Murray H.; Zara, Jason M.

2009-07-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images.

Science.gov (United States)

Lingley-Papadopoulos, Colleen A; Loew, Murray H; Zara, Jason M

2009-01-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

Science.gov (United States)

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-01

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different

Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts.

Science.gov (United States)

Zhou, Zhuhuang; Wu, Weiwei; Wu, Shuicai; Tsui, Po-Hsiang; Lin, Chung-Chih; Zhang, Ling; Wang, Tianfu

2014-10-01

Computerized tumor segmentation on breast ultrasound (BUS) images remains a challenging task. In this paper, we proposed a new method for semi-automatic tumor segmentation on BUS images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. The only interaction required was to select two diagonal points to determine a region of interest (ROI) on an input image. The ROI image was shrunken by a factor of 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by a factor of 2 using bicubic interpolation, and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with OpenCV 2.4.3 and Visual Studio 2010 and tested for 38 BUS images with benign tumors and 31 BUS images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate (TP) of 91.7%, a false positive (FP) rate of 11.9%, and a similarity (SI) rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation. © The Author(s) 2014.

Neural network for automatic analysis of motility data

DEFF Research Database (Denmark)

Jakobsen, Erik; Kruse-Andersen, S; Kolberg, Jens Godsk

1994-01-01

comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neurocomputing has potential advantages for automatic analysis of gastrointestinal motility data.......Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure...

Paediatric Automatic Phonological Analysis Tools (APAT).

Science.gov (United States)

Saraiva, Daniela; Lousada, Marisa; Hall, Andreia; Jesus, Luis M T

2017-12-01

To develop the pediatric Automatic Phonological Analysis Tools (APAT) and to estimate inter and intrajudge reliability, content validity, and concurrent validity. The APAT were constructed using Excel spreadsheets with formulas. The tools were presented to an expert panel for content validation. The corpus used in the Portuguese standardized test Teste Fonético-Fonológico - ALPE produced by 24 children with phonological delay or phonological disorder was recorded, transcribed, and then inserted into the APAT. Reliability and validity of APAT were analyzed. The APAT present strong inter- and intrajudge reliability (>97%). The content validity was also analyzed (ICC = 0.71), and concurrent validity revealed strong correlations between computerized and manual (traditional) methods. The development of these tools contributes to fill existing gaps in clinical practice and research, since previously there were no valid and reliable tools/instruments for automatic phonological analysis, which allowed the analysis of different corpora.

Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

Science.gov (United States)

Sanz-Requena, Roberto; Moratal, David; García-Sánchez, Diego Ramón; Bodí, Vicente; Rieta, José Joaquín; Sanchis, Juan Manuel

2007-03-01

Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames.

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

Science.gov (United States)

Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

2015-03-01

Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

Anatomy-based automatic detection and segmentation of major vessels in thoracic CTA images

International Nuclear Information System (INIS)

Zou Xiaotao; Liang Jianming; Wolf, M.; Salganicoff, M.; Krishnan, A.; Nadich, D.P.

2007-01-01

Existing approaches for automated computerized detection of pulmonary embolism (PE) using computed tomography angiography (CTA) usually focus on segmental and sub-segmental emboli. The goal of our current research is to extend our existing approach to automated detection of central PE. In order to detect central emboli, the major vessels must be first identified and segmented automatically. This submission presents an anatomy-based method for automatic computerized detection and segmentation of aortas and main pulmonary arteries in CTA images. (orig.)

Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model.

Science.gov (United States)

Zakeri, Fahimeh Sadat; Setarehdan, Seyed Kamaledin; Norouzi, Somayye

2017-10-01

Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market. This paper presents a deterministic-statistical strategy for automatic media-adventitia border detection by a fourfold algorithm. First, a smoothed initial contour is extracted based on the classification in the sparse representation framework which is combined with the dynamic directional convolution vector field. Next, an active contour model is utilized for the propagation of the initial contour toward the interested borders. Finally, the extracted contour is refined in the leakage, side branch openings and calcification regions based on the image texture patterns. The performance of the proposed algorithm is evaluated by comparing the results to those manually traced borders by an expert on 312 different IVUS images obtained from four different patients. The statistical analysis of the results demonstrates the efficiency of the proposed method in the media-adventitia border detection with enough consistency in the leakage and calcification regions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Image analysis in the evaluation of the physiological potential of maize seeds1

Directory of Open Access Journals (Sweden)

Crislaine Aparecida Gomes Pinto

Full Text Available The Seed Analysis System (SAS is used in the image analysis of seeds and seedlings, and has the potential for use in the control of seed quality. The aim of this research was to adapt the methodology of image analysis of maize seedlings by SAS, and to verify the potential use of this equipment in the evaluation of the physiological potential of maize seeds. Nine batches of two maize hybrids were characterised by means of the following tests and determinations: germination, first count, accelerated ageing, cold test, seedling emergence at 25 and 30ºC, and speed of emergence index. The image analysis experiment was carried out in a factorial scheme of 9 batches x 4 methods of analysis of the seedling images (with and without the use of NWF as substrate, and with and without manual correction of the images. Images of the seedlings were evaluated using the average lengths of the coleoptile, roots and seedlings; and by the automatic and manual indices of vigour, uniformity and growth produced by the SAS. Use of blue NWF afffects the initial development of maize seedlings. The physiological potential of maize seeds can be evaluated in seedlings which are seeded on white paper towels at a temperature of 25 °C and evaluated on the third day. Image analysis should be carried out with the SAS software using automatic calibration and with no correction of the seedling images. Use of SAS equipment for the analysis of seedling images is a potential tool in evaluating the physiological quality of maize seeds.

Automated analysis of phantom images for the evaluation of long-term reproducibility in digital mammography

International Nuclear Information System (INIS)

Gennaro, G; Ferro, F; Contento, G; Fornasin, F; Di Maggio, C

2007-01-01

The performance of an automatic software package was evaluated with phantom images acquired by a full-field digital mammography unit. After the validation, the software was used, together with a Leeds TORMAS test object, to model the image acquisition process. Process modelling results were used to evaluate the sensitivity of the method in detecting changes of exposure parameters from routine image quality measurements in digital mammography, which is the ultimate purpose of long-term reproducibility tests. Image quality indices measured by the software included the mean pixel value and standard deviation of circular details and surrounding background, contrast-to-noise ratio and relative contrast; detail counts were also collected. The validation procedure demonstrated that the software localizes the phantom details correctly and the difference between automatic and manual measurements was within few grey levels. Quantitative analysis showed sufficient sensitivity to relate fluctuations in exposure parameters (kV p or mAs) to variations in image quality indices. In comparison, detail counts were found less sensitive in detecting image quality changes, even when limitations due to observer subjectivity were overcome by automatic analysis. In conclusion, long-term reproducibility tests provided by the Leeds TORMAS phantom with quantitative analysis of multiple IQ indices have been demonstrated to be effective in predicting causes of deviation from standard operating conditions and can be used to monitor stability in full-field digital mammography

Profiling School Shooters: Automatic Text-Based Analysis

Directory of Open Access Journals (Sweden)

Yair eNeuman

2015-06-01

Full Text Available School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various charateristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by six school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters' texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/priorization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology.

Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

Directory of Open Access Journals (Sweden)

I. Cruz-Aceves

2013-01-01

Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

Science.gov (United States)

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

2016-01-01

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and

Automatic Chessboard Detection for Intrinsic and Extrinsic Camera Parameter Calibration

Directory of Open Access Journals (Sweden)

Jose María Armingol

2010-03-01

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

Automatic localization of landmark sets in head CT images with regression forests for image registration initialization

Science.gov (United States)

Zhang, Dongqing; Liu, Yuan; Noble, Jack H.; Dawant, Benoit M.

2016-03-01

Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.

Efficient Semi-Automatic 3D Segmentation for Neuron Tracing in Electron Microscopy Images

Science.gov (United States)

Jones, Cory; Liu, Ting; Cohan, Nathaniel Wood; Ellisman, Mark; Tasdizen, Tolga

2015-01-01

0.1. Background In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming. 0.2. New Method We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links. 0.3. Results We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results. 0.4. Comparison with Existing Methods Post-automatic correction methods have also been used in [1] and [2]. These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as [3] and [4] and are inherently different than our method. 0.5. Conclusion Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication. PMID:25769273

Automatic anatomy recognition in whole-body PET/CT images

Energy Technology Data Exchange (ETDEWEB)

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

2016-01-15

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

Automatic anatomy recognition in whole-body PET/CT images

International Nuclear Information System (INIS)

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

2016-01-01

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

Cost-benefit analysis of the ATM automatic deposit service

Directory of Open Access Journals (Sweden)

Ivica Županović

2015-03-01

Full Text Available Bankers and other financial experts have analyzed the value of automated teller machines (ATM in terms of growing consumer demand, rising costs of technology development, decreasing profitability and market share. This paper presents a step-by-step cost-benefit analysis of the ATM automatic deposit service. The first step is to determine user attitudes towards using ATM automatic deposit service by using the Technology Acceptance Model (TAM. The second step is to determine location priorities for ATMs that provide automatic deposit services using the Analytic Hierarchy Process (AHP model. The results of the previous steps enable a highly efficient application of cost-benefit analysis for evaluating costs and benefits of automatic deposit services. To understand fully the proposed procedure outside of theoretical terms, a real-world application of a case study is conducted.

Automatic segmentation of MR brain images of preterm infants using supervised classification.

Science.gov (United States)

Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

2015-09-01

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

Automatic luminous reflections detector using global threshold with increased luminosity contrast in images

Science.gov (United States)

Silva, Ricardo Petri; Naozuka, Gustavo Taiji; Mastelini, Saulo Martiello; Felinto, Alan Salvany

2018-01-01

The incidence of luminous reflections (LR) in captured images can interfere with the color of the affected regions. These regions tend to oversaturate, becoming whitish and, consequently, losing the original color information of the scene. Decision processes that employ images acquired from digital cameras can be impaired by the LR incidence. Such applications include real-time video surgeries, facial, and ocular recognition. This work proposes an algorithm called contrast enhancement of potential LR regions, which is a preprocessing to increase the contrast of potential LR regions, in order to improve the performance of automatic LR detectors. In addition, three automatic detectors were compared with and without the employment of our preprocessing method. The first one is a technique already consolidated in the literature called the Chang-Tseng threshold. We propose two automatic detectors called adapted histogram peak and global threshold. We employed four performance metrics to evaluate the detectors, namely, accuracy, precision, exactitude, and root mean square error. The exactitude metric is developed by this work. Thus, a manually defined reference model was created. The global threshold detector combined with our preprocessing method presented the best results, with an average exactitude rate of 82.47%.

Automated Image Analysis in Undetermined Sections of Human Permanent Third Molars

DEFF Research Database (Denmark)

Bjørndal, Lars; Darvann, Tron Andre; Bro-Nielsen, Morten

1997-01-01

. Sixty-three photomicrographs (x100), equally distributed among the three sectioning profiles, were scanned in a high-resolution scanner to produce images for the analysis. After initial user interaction for the description of training classes on one image, an automatic segmentation of the images...... sectioning profiles should be analysed. The use of advanced image processing on undemineralized tooth sections provides a rational foundation for further work on the reactions of the odontoblasts to external injuries including dental caries....

AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

Directory of Open Access Journals (Sweden)

C. Xu

2016-06-01

Full Text Available Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

Energy Technology Data Exchange (ETDEWEB)

Chang, X; Yang, D [Washington University in St Louis, St Louis, MO (United States)

2015-06-15

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

International Nuclear Information System (INIS)

Chang, X; Yang, D

2015-01-01

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

Science.gov (United States)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

AUTOMATIC FAULT RECOGNITION OF PHOTOVOLTAIC MODULES BASED ON STATISTICAL ANALYSIS OF UAV THERMOGRAPHY

Directory of Open Access Journals (Sweden)

D. Kim

2017-08-01

Full Text Available As a malfunctioning PV (Photovoltaic cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle. The proposed algorithm uses statistical analysis of thermal intensity (surface temperature characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

Examination of the semi-automatic calculation technique of vegetation cover rate by digital camera images.

Science.gov (United States)

Takemine, S.; Rikimaru, A.; Takahashi, K.

The rice is one of the staple foods in the world High quality rice production requires periodically collecting rice growth data to control the growth of rice The height of plant the number of stem the color of leaf is well known parameters to indicate rice growth Rice growth diagnosis method based on these parameters is used operationally in Japan although collecting these parameters by field survey needs a lot of labor and time Recently a laborsaving method for rice growth diagnosis is proposed which is based on vegetation cover rate of rice Vegetation cover rate of rice is calculated based on discriminating rice plant areas in a digital camera image which is photographed in nadir direction Discrimination of rice plant areas in the image was done by the automatic binarization processing However in the case of vegetation cover rate calculation method depending on the automatic binarization process there is a possibility to decrease vegetation cover rate against growth of rice In this paper a calculation method of vegetation cover rate was proposed which based on the automatic binarization process and referred to the growth hysteresis information For several images obtained by field survey during rice growing season vegetation cover rate was calculated by the conventional automatic binarization processing and the proposed method respectively And vegetation cover rate of both methods was compared with reference value obtained by visual interpretation As a result of comparison the accuracy of discriminating rice plant areas was increased by the proposed

Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial

International Nuclear Information System (INIS)

Geraghty, John P; Grogan, Garry; Ebert, Martin A

2013-01-01

This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients (‘benchmarking cases’), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 “RADAR” trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations < 0.4 cm across the majority of image slices). Although there was some variation in interpretation of the superior-inferior (cranio-caudal) extent of rectum, human-observer contours were typically within a mean 0.6 cm of automatically-defined contours. Prostate structures were more consistent for the HR case than the IR case with all human observers segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation

AUTOMATIC ORIENTATION OF LARGE BLOCKS OF OBLIQUE IMAGES

Directory of Open Access Journals (Sweden)

E. Rupnik

2013-05-01

Full Text Available Nowadays, multi-camera platforms combining nadir and oblique cameras are experiencing a revival. Due to their advantages such as ease of interpretation, completeness through mitigation of occluding areas, as well as system accessibility, they have found their place in numerous civil applications. However, automatic post-processing of such imagery still remains a topic of research. Configuration of cameras poses a challenge on the traditional photogrammetric pipeline used in commercial software and manual measurements are inevitable. For large image blocks it is certainly an impediment. Within theoretical part of the work we review three common least square adjustment methods and recap on possible ways for a multi-camera system orientation. In the practical part we present an approach that successfully oriented a block of 550 images acquired with an imaging system composed of 5 cameras (Canon Eos 1D Mark III with different focal lengths. Oblique cameras are rotated in the four looking directions (forward, backward, left and right by 45° with respect to the nadir camera. The workflow relies only upon open-source software: a developed tool to analyse image connectivity and Apero to orient the image block. The benefits of the connectivity tool are twofold: in terms of computational time and success of Bundle Block Adjustment. It exploits the georeferenced information provided by the Applanix system in constraining feature point extraction to relevant images only, and guides the concatenation of images during the relative orientation. Ultimately an absolute transformation is performed resulting in mean re-projection residuals equal to 0.6 pix.

Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

Science.gov (United States)

Wang, Gaochao; Tse, Peter W.; Yuan, Maodan

2018-02-01

Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.

Accuracy of Automatic Cephalometric Software on Landmark Identification

Science.gov (United States)

Anuwongnukroh, N.; Dechkunakorn, S.; Damrongsri, S.; Nilwarat, C.; Pudpong, N.; Radomsutthisarn, W.; Kangern, S.

2017-11-01

This study was to assess the accuracy of an automatic cephalometric analysis software in the identification of cephalometric landmarks. Thirty randomly selected digital lateral cephalograms of patients undergoing orthodontic treatment were used in this study. Thirteen landmarks (S, N, Or, A-point, U1T, U1A, B-point, Gn, Pog, Me, Go, L1T, and L1A) were identified on the digital image by an automatic cephalometric software and on cephalometric tracing by manual method. Superimposition of printed image and manual tracing was done by registration at the soft tissue profiles. The accuracy of landmarks located by the automatic method was compared with that of the manually identified landmarks by measuring the mean differences of distances of each landmark on the Cartesian plane where X and Y coordination axes passed through the center of ear rod. One-Sample T test was used to evaluate the mean differences. Statistically significant mean differences (pmean differences in both horizontal and vertical directions. Small mean differences (mean differences were found for A-point (3.0 4mm) in vertical direction. Only 5 of 13 landmarks (38.46%; S, N, Gn, Pog, and Go) showed no significant mean difference between the automatic and manual landmarking methods. It is concluded that if this automatic cephalometric analysis software is used for orthodontic diagnosis, the orthodontist must correct or modify the position of landmarks in order to increase the accuracy of cephalometric analysis.

System for the automatic analysis of defects in X-ray imaging

International Nuclear Information System (INIS)

Favier, C.; Thomas, G.; Brebant, C.; Mogavero, R.

1984-05-01

A radiological device was developed to obtain direct digitized views. A set of algorithms has been developed and demonstrated for the automatic evaluation of weldings. Some results concerning electronuclear fuel pin weldings are presented [fr

Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

Science.gov (United States)

Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping

2014-01-01

The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

Directory of Open Access Journals (Sweden)

Yu Guo

2014-01-01

Full Text Available The combination of positron emission tomography (PET and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

Automatic measurement system for light element isotope analysis

International Nuclear Information System (INIS)

Satake, Hiroshi; Ikegami, Kouichi.

1990-01-01

The automatic measurement system for the light element isotope analysis was developed by installing the specially designed inlet system which was controlled by a computer. The microcomputer system contains specific interface boards for the inlet system and the mass spectrometer, Micromass 602 E. All the components of the inlet and the computer system installed are easily available in Japan. Ten samples can be automatically measured as a maximum of. About 160 minutes are required for 10 measurements of δ 18 O values of CO 2 . Thus four samples can be measured per an hour using this system, while usually three samples for an hour using the manual operation. The automatized analysis system clearly has an advantage over the conventional method. This paper describes the details of this automated system, such as apparatuses used, the control procedure and the correction for reliable measurement. (author)

Automatic IVUS segmentation of atherosclerotic plaque with Stop & Go snake

NARCIS (Netherlands)

Brunenberg, E.J.L.; Pujol, O.; Haar Romenij, ter B.M.; Radeva, P.; Lelieveldt, B.P.F.; Haverkort, B.; de Laat, C.T.A.M.; Heijnsdijk, J.W.J.

2006-01-01

Since the upturn of intravascular ultrasound (IVUS)as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was

Automatic detection of solar features in HSOS full-disk solar images using guided filter

Science.gov (United States)

Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

2018-02-01

A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT.

Science.gov (United States)

Scholtz, Jan-Erik; Wichmann, Julian L; Kaup, Moritz; Fischer, Sebastian; Kerl, J Matthias; Lehnert, Thomas; Vogl, Thomas J; Bauer, Ralf W

2015-03-01

To evaluate software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine on CT in terms of accuracy, potential for time savings and workflow improvement. 77 patients (28 women, 49 men, mean age 65.3±14.4 years) with known or suspected spinal disorders (degenerative spine disease n=32; disc herniation n=36; traumatic vertebral fractures n=9) underwent 64-slice MDCT with thin-slab reconstruction. Time for automatic labeling of the thoracolumbar spine and reconstruction of double-angulated axial images of the pathological vertebrae was compared with manually performed reconstruction of anatomical aligned axial images. Reformatted images of both reconstruction methods were assessed by two observers regarding accuracy of symmetric depiction of anatomical structures. In 33 cases double-angulated axial images were created in 1 vertebra, in 28 cases in 2 vertebrae and in 16 cases in 3 vertebrae. Correct automatic labeling was achieved in 72 of 77 patients (93.5%). Errors could be manually corrected in 4 cases. Automatic labeling required 1min in average. In cases where anatomical aligned axial images of 1 vertebra were created, reconstructions made by hand were significantly faster (pquality with excellent inter-observer agreement. The evaluated software for automatic labeling and anatomically aligned, double-angulated axial image reconstruction of the thoracolumbar spine on CT is time-saving when reconstructions of 2 and more vertebrae are performed. Checking results of automatic labeling is necessary to prevent errors in labeling. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

CERN Document Server

Kainmueller, Dagmar

2014-01-01

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

Automatic Welding System of Aluminum Pipe by Monitoring Backside Image of Molten Pool Using Vision Sensor

Science.gov (United States)

Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

An automatic welding system using Tungsten Inert Gas (TIG) welding with vision sensor for welding of aluminum pipe was constructed. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position and moving welding torch with the AC welding machine. The monitoring system consists of a vision sensor using a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Neural network model for welding speed control were constructed to perform the process automatically. From the experimental results it shows the effectiveness of the control system confirmed by good detection of molten pool and sound weld of experimental result.

Automatic structural scene digitalization.

Science.gov (United States)

Tang, Rui; Wang, Yuhan; Cosker, Darren; Li, Wenbin

2017-01-01

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.

Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial.

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Geraghty, John P; Grogan, Garry; Ebert, Martin A

2013-04-30

This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients ('benchmarking cases'), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 "RADAR" trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation algorithms based on image-registration as in iPlan, it is apparent that agreement between observer and automatic segmentation will be a function of patient-specific image characteristics, particularly for anatomy with poor contrast definition. For this reason, it is suggested that automatic registration based on transformation of a single reference dataset

A cloud-based system for automatic glaucoma screening.

Science.gov (United States)

Fengshou Yin; Damon Wing Kee Wong; Ying Quan; Ai Ping Yow; Ngan Meng Tan; Gopalakrishnan, Kavitha; Beng Hai Lee; Yanwu Xu; Zhuo Zhang; Jun Cheng; Jiang Liu

2015-08-01

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases including glaucoma. However, these systems are usually standalone software with basic functions only, limiting their usage in a large scale. In this paper, we introduce an online cloud-based system for automatic glaucoma screening through the use of medical image-based pattern classification technologies. It is designed in a hybrid cloud pattern to offer both accessibility and enhanced security. Raw data including patient's medical condition and fundus image, and resultant medical reports are collected and distributed through the public cloud tier. In the private cloud tier, automatic analysis and assessment of colour retinal fundus images are performed. The ubiquitous anywhere access nature of the system through the cloud platform facilitates a more efficient and cost-effective means of glaucoma screening, allowing the disease to be detected earlier and enabling early intervention for more efficient intervention and disease management.

A combined use of multispectral and SAR images for ship detection and characterization through object based image analysis

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Aiello, Martina; Gianinetto, Marco

2017-10-01

Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels' position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R2=0.85 and estimation of heading R2=0.92, computed as the average of R2 values obtained for both optical and radar images.

Flame analysis using image processing techniques

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Her Jie, Albert Chang; Zamli, Ahmad Faizal Ahmad; Zulazlan Shah Zulkifli, Ahmad; Yee, Joanne Lim Mun; Lim, Mooktzeng

2018-04-01

This paper presents image processing techniques with the use of fuzzy logic and neural network approach to perform flame analysis. Flame diagnostic is important in the industry to extract relevant information from flame images. Experiment test is carried out in a model industrial burner with different flow rates. Flame features such as luminous and spectral parameters are extracted using image processing and Fast Fourier Transform (FFT). Flame images are acquired using FLIR infrared camera. Non-linearities such as thermal acoustic oscillations and background noise affect the stability of flame. Flame velocity is one of the important characteristics that determines stability of flame. In this paper, an image processing method is proposed to determine flame velocity. Power spectral density (PSD) graph is a good tool for vibration analysis where flame stability can be approximated. However, a more intelligent diagnostic system is needed to automatically determine flame stability. In this paper, flame features of different flow rates are compared and analyzed. The selected flame features are used as inputs to the proposed fuzzy inference system to determine flame stability. Neural network is used to test the performance of the fuzzy inference system.

Automatic volumetry on MR brain images can support diagnostic decision making

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Aviv Richard I

2008-05-01

Full Text Available Abstract Background Diagnostic decisions in clinical imaging currently rely almost exclusively on visual image interpretation. This can lead to uncertainty, for example in dementia disease, where some of the changes resemble those of normal ageing. We hypothesized that extracting volumetric data from patients' MR brain images, relating them to reference data and presenting the results as a colour overlay on the grey scale data would aid diagnostic readers in classifying dementia disease versus normal ageing. Methods A proof-of-concept forced-choice reader study was designed using MR brain images from 36 subjects. Images were segmented into 43 regions using an automatic atlas registration-based label propagation procedure. Seven subjects had clinically probable AD, the remaining 29 of a similar age range were used as controls. Seven of the control subject data sets were selected at random to be presented along with the seven AD datasets to two readers, who were blinded to all clinical and demographic information except age and gender. Readers were asked to review the grey scale MR images and to record their choice of diagnosis (AD or non-AD along with their confidence in this decision. Afterwards, readers were given the option to switch on a false-colour overlay representing the relative size of the segmented structures. Colorization was based on the size rank of the test subject when compared with a reference group consisting of the 22 control subjects who were not used as review subjects. The readers were then asked to record whether and how the additional information had an impact on their diagnostic confidence. Results The size rank colour overlays were useful in 18 of 28 diagnoses, as determined by their impact on readers' diagnostic confidence. A not useful result was found in 6 of 28 cases. The impact of the additional information on diagnostic confidence was significant (p Conclusion Volumetric anatomical information extracted from brain

Multispectral analysis of multimodal images

Energy Technology Data Exchange (ETDEWEB)

Kvinnsland, Yngve; Brekke, Njaal (Dept. of Surgical Sciences, Univ. of Bergen, Bergen (Norway)); Taxt, Torfinn M.; Gruener, Renate (Dept. of Biomedicine, Univ. of Bergen, Bergen (Norway))

2009-02-15

An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially as unlimited number of images can be combined, and tissue properties across the images can be extracted automatically. Materials and methods. We have developed a software solution for MSA containing two algorithms for unsupervised classification, an EM-algorithm finding multinormal class descriptions and the k-means clustering algorithm, and two for supervised classification, a Bayesian classifier using multinormal class descriptions and a kNN-algorithm. The software has an efficient user interface for the creation and manipulation of class descriptions, and it has proper tools for displaying the results. Results. The software has been tested on different sets of images. One application is to segment cross-sectional images of brain tissue (T1- and T2-weighted MR images) into its main normal tissues and brain tumors. Another interesting set of images are the perfusion maps and diffusion maps, derived images from raw MR images. The software returns segmentation that seem to be sensible. Discussion. The MSA software appears to be a valuable tool for image analysis with multimodal images at hand. It readily gives a segmentation of image volumes that visually seems to be sensible. However, to really learn how to use MSA, it will be necessary to gain more insight into what tissues the different segments contain, and the upcoming work will therefore be focused on examining the tissues through for example histological sections.

Automatic Description Generation from Images : A Survey of Models, Datasets, and Evaluation Measures

NARCIS (Netherlands)

Bernardi, Raffaella; Cakici, Ruket; Elliott, Desmond; Erdem, Aykut; Erdem, Erkut; Ikizler-Cinbis, Nazli; Keller, Frank; Muscat, Adrian; Plank, Barbara

2016-01-01

Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem,

Automatic identification of corrosion damage using image processing techniques

Energy Technology Data Exchange (ETDEWEB)

Bento, Mariana P.; Ramalho, Geraldo L.B.; Medeiros, Fatima N.S. de; Ribeiro, Elvis S. [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil); Medeiros, Luiz C.L. [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

2009-07-01

This paper proposes a Nondestructive Evaluation (NDE) method for atmospheric corrosion detection on metallic surfaces using digital images. In this study, the uniform corrosion is characterized by texture attributes extracted from co-occurrence matrix and the Self Organizing Mapping (SOM) clustering algorithm. We present a technique for automatic inspection of oil and gas storage tanks and pipelines of petrochemical industries without disturbing their properties and performance. Experimental results are promising and encourage the possibility of using this methodology in designing trustful and robust early failure detection systems. (author)

An Imaging And Graphics Workstation For Image Sequence Analysis

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Mostafavi, Hassan

1990-01-01

This paper describes an application-specific engineering workstation designed and developed to analyze imagery sequences from a variety of sources. The system combines the software and hardware environment of the modern graphic-oriented workstations with the digital image acquisition, processing and display techniques. The objective is to achieve automation and high throughput for many data reduction tasks involving metric studies of image sequences. The applications of such an automated data reduction tool include analysis of the trajectory and attitude of aircraft, missile, stores and other flying objects in various flight regimes including launch and separation as well as regular flight maneuvers. The workstation can also be used in an on-line or off-line mode to study three-dimensional motion of aircraft models in simulated flight conditions such as wind tunnels. The system's key features are: 1) Acquisition and storage of image sequences by digitizing real-time video or frames from a film strip; 2) computer-controlled movie loop playback, slow motion and freeze frame display combined with digital image sharpening, noise reduction, contrast enhancement and interactive image magnification; 3) multiple leading edge tracking in addition to object centroids at up to 60 fields per second from both live input video or a stored image sequence; 4) automatic and manual field-of-view and spatial calibration; 5) image sequence data base generation and management, including the measurement data products; 6) off-line analysis software for trajectory plotting and statistical analysis; 7) model-based estimation and tracking of object attitude angles; and 8) interface to a variety of video players and film transport sub-systems.

Automatic Pedestrian Crossing Detection and Impairment Analysis Based on Mobile Mapping System

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Liu, X.; Zhang, Y.; Li, Q.

2017-09-01

Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians' lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

AUTOMATIC PEDESTRIAN CROSSING DETECTION AND IMPAIRMENT ANALYSIS BASED ON MOBILE MAPPING SYSTEM

Directory of Open Access Journals (Sweden)

X. Liu

2017-09-01

Full Text Available Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians’ lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

A complete software application for automatic registration of x-ray mammography and magnetic resonance images

International Nuclear Information System (INIS)

Solves-Llorens, J. A.; Rupérez, M. J.; Monserrat, C.; Feliu, E.; García, M.; Lloret, M.

2014-01-01

Purpose: This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x-ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x-ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa. Methods: The application automatically segments magnetic resonance images and x-ray images using the C-Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods. Results: The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration. Conclusions: A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the

A complete software application for automatic registration of x-ray mammography and magnetic resonance images

Energy Technology Data Exchange (ETDEWEB)

Solves-Llorens, J. A.; Rupérez, M. J., E-mail: mjruperez@labhuman.i3bh.es; Monserrat, C. [LabHuman, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia (Spain); Feliu, E.; García, M. [Hospital Clínica Benidorm, Avda. Alfonso Puchades, 8, 03501 Benidorm (Alicante) (Spain); Lloret, M. [Hospital Universitari y Politècnic La Fe, Bulevar Sur, 46026 Valencia (Spain)

2014-08-15

Purpose: This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x-ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x-ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa. Methods: The application automatically segments magnetic resonance images and x-ray images using the C-Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods. Results: The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration. Conclusions: A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning.

Science.gov (United States)

Li, Guang; Wei, Jie; Huang, Hailiang; Gaebler, Carl Philipp; Yuan, Amy; Deasy, Joseph O

2015-12-01

To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients ( f v ) to account for most of the spectrum energy (Σ f v 2 ). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves ( R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors

Improved sampling and analysis of images in corneal confocal microscopy.

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Schaldemose, E L; Fontain, F I; Karlsson, P; Nyengaard, J R

2017-10-01

Corneal confocal microscopy (CCM) is a noninvasive clinical method to analyse and quantify corneal nerve fibres in vivo. Although the CCM technique is in constant progress, there are methodological limitations in terms of sampling of images and objectivity of the nerve quantification. The aim of this study was to present a randomized sampling method of the CCM images and to develop an adjusted area-dependent image analysis. Furthermore, a manual nerve fibre analysis method was compared to a fully automated method. 23 idiopathic small-fibre neuropathy patients were investigated using CCM. Corneal nerve fibre length density (CNFL) and corneal nerve fibre branch density (CNBD) were determined in both a manual and automatic manner. Differences in CNFL and CNBD between (1) the randomized and the most common sampling method, (2) the adjusted and the unadjusted area and (3) the manual and automated quantification method were investigated. The CNFL values were significantly lower when using the randomized sampling method compared to the most common method (p = 0.01). There was not a statistical significant difference in the CNBD values between the randomized and the most common sampling method (p = 0.85). CNFL and CNBD values were increased when using the adjusted area compared to the standard area. Additionally, the study found a significant increase in the CNFL and CNBD values when using the manual method compared to the automatic method (p ≤ 0.001). The study demonstrated a significant difference in the CNFL values between the randomized and common sampling method indicating the importance of clear guidelines for the image sampling. The increase in CNFL and CNBD values when using the adjusted cornea area is not surprising. The observed increases in both CNFL and CNBD values when using the manual method of nerve quantification compared to the automatic method are consistent with earlier findings. This study underlines the importance of improving the analysis of the

Algorithm for automatic image dodging of unmanned aerial vehicle images using two-dimensional radiometric spatial attributes

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Li, Wenzhuo; Sun, Kaimin; Li, Deren; Bai, Ting

2016-07-01

Unmanned aerial vehicle (UAV) remote sensing technology has come into wide use in recent years. The poor stability of the UAV platform, however, produces more inconsistencies in hue and illumination among UAV images than other more stable platforms. Image dodging is a process used to reduce these inconsistencies caused by different imaging conditions. We propose an algorithm for automatic image dodging of UAV images using two-dimensional radiometric spatial attributes. We use object-level image smoothing to smooth foreground objects in images and acquire an overall reference background image by relative radiometric correction. We apply the Contourlet transform to separate high- and low-frequency sections for every single image, and replace the low-frequency section with the low-frequency section extracted from the corresponding region in the overall reference background image. We apply the inverse Contourlet transform to reconstruct the final dodged images. In this process, a single image must be split into reasonable block sizes with overlaps due to large pixel size. Experimental mosaic results show that our proposed method reduces the uneven distribution of hue and illumination. Moreover, it effectively eliminates dark-bright interstrip effects caused by shadows and vignetting in UAV images while maximally protecting image texture information.

Development of an automatic prompt gamma-ray activation analysis system

International Nuclear Information System (INIS)

Osawa, Takahito

2013-01-01

An automatic prompt gamma-ray activation analysis system was developed and installed at the Japan Research Reactor No. 3 Modified (JRR-3M). The main control software, referred to as AutoPGA, was developed using LabVIEW 2011 and the hand-made program can control all functions of the analytical system. The core of the new system is an automatic sample exchanger and measurement system with several additional automatic control functions integrated into the system. Up to fourteen samples can be automatically measured by the system. (author)

A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.

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Panicker, Rani Oomman; Soman, Biju; Saini, Gagan; Rajan, Jeny

2016-01-01

Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming; a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research.

AUTOMATIC BUILDING OUTLINING FROM MULTI-VIEW OBLIQUE IMAGES

Directory of Open Access Journals (Sweden)

J. Xiao

2012-07-01

Full Text Available Automatic building detection plays an important role in many applications. Multiple overlapped airborne images as well as lidar point clouds are among the most popular data sources used for this purpose. Multi-view overlapped oblique images bear both height and colour information, and additionally we explicitly have access to the vertical extent of objects, therefore we explore the usability of this data source solely to detect and outline buildings in this paper. The outline can then be used for further 3D modelling. In the previous work, building hypotheses are generated using a box model based on detected façades from four directions. In each viewing direction, façade edges extracted from images and height information by stereo matching from an image pair is used for the façade detection. Given that many façades were missing due to occlusion or lack of texture whilst building roofs can be viewed in most images, this work mainly focuses on improve the building box outline by adding roof information. Stereo matched point cloud generated from oblique images are combined with the features from images. Initial roof patches are located in the point cloud. Then AdaBoost is used to integrate geometric and radiometric attributes extracted from oblique image on grid pixel level with the aim to refine the roof area. Generalized contours of the roof pixels are taken as building outlines. The preliminary test has been done by training with five buildings and testing around sixty building clusters. The proposed method performs well concerning covering the irregular roofs as well as improve the sides location of slope roof buildings. Outline result comparing with cadastral map shows almost all above 70% completeness and correctness in an area-based assessment, as well as 20% to 40% improvement in correctness with respect to our previous work.

Diffraction imaging and velocity analysis using oriented velocity continuation

KAUST Repository

Decker, Luke

2014-08-05

We perform seismic diffraction imaging and velocity analysis by separating diffractions from specular reflections and decomposing them into slope components. We image slope components using extrapolation in migration velocity in time-space-slope coordinates. The extrapolation is described by a convection-type partial differential equation and implemented efficiently in the Fourier domain. Synthetic and field data experiments show that the proposed algorithm is able to detect accurate time-migration velocities by automatically measuring the flatness of events in dip-angle gathers.

A novel automatic quantification method for high-content screening analysis of DNA double strand-break response.

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Feng, Jingwen; Lin, Jie; Zhang, Pengquan; Yang, Songnan; Sa, Yu; Feng, Yuanming

2017-08-29

High-content screening is commonly used in studies of the DNA damage response. The double-strand break (DSB) is one of the most harmful types of DNA damage lesions. The conventional method used to quantify DSBs is γH2AX foci counting, which requires manual adjustment and preset parameters and is usually regarded as imprecise, time-consuming, poorly reproducible, and inaccurate. Therefore, a robust automatic alternative method is highly desired. In this manuscript, we present a new method for quantifying DSBs which involves automatic image cropping, automatic foci-segmentation and fluorescent intensity measurement. Furthermore, an additional function was added for standardizing the measurement of DSB response inhibition based on co-localization analysis. We tested the method with a well-known inhibitor of DSB response. The new method requires only one preset parameter, which effectively minimizes operator-dependent variations. Compared with conventional methods, the new method detected a higher percentage difference of foci formation between different cells, which can improve measurement accuracy. The effects of the inhibitor on DSB response were successfully quantified with the new method (p = 0.000). The advantages of this method in terms of reliability, automation and simplicity show its potential in quantitative fluorescence imaging studies and high-content screening for compounds and factors involved in DSB response.

Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Hassan Mohamed

2018-05-01

Full Text Available Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV ensembles for Support Vector Machines (SVM, K-Nearest Neighbor (K-NN, and Bagging (BAG classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.

Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms

Science.gov (United States)

Benmoussat, M. S.; Guillaume, M.; Caulier, Y.; Spinnler, K.

2013-11-01

A fully-automatic approach based on the use of induction thermography and detection algorithms is proposed to inspect industrial metallic parts containing different surface and sub-surface anomalies such as open cracks, open and closed notches with different sizes and depths. A practical experimental setup is developed, where lock-in and pulsed thermography (LT and PT, respectively) techniques are used to establish a dataset of thermal images for three different mockups. Data cubes are constructed by stacking up the temporal sequence of thermogram images. After the reduction of the data space dimension by means of denoising and dimensionality reduction methods; anomaly detection algorithms are applied on the reduced data cubes. The dimensions of the reduced data spaces are automatically calculated with arbitrary criterion. The results show that, when reduced data cubes are used, the anomaly detection algorithms originally developed for hyperspectral data, the well-known Reed and Xiaoli Yu detector (RX) and the regularized adaptive RX (RARX), give good detection performances for both surface and sub-surface defects in a non-supervised way.

DAF: differential ACE filtering image quality assessment by automatic color equalization

Science.gov (United States)

Ouni, S.; Chambah, M.; Saint-Jean, C.; Rizzi, A.

2008-01-01

Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. But in reality, objective quality metrics do not necessarily correlate well with perceived quality [1]. Plus, some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their usage in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation is the most used and most efficient approach up to now. But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements [2,3]. Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration. The ACE method, for Automatic Color Equalization [4,6], is an algorithm for digital images unsupervised enhancement. It is based on a new computational approach that tries to model the perceptual response of our vision system merging the Gray World and White Patch equalization mechanisms in a global and local way. Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information from the environment efficaciously. Moreover ACE can be run in an unsupervised manner. Hence it is very useful as a digital film restoration tool since no a priori information is available. In this paper we deepen the investigation of using the ACE algorithm as a basis for a reference free image quality evaluation. This new metric called DAF for Differential ACE Filtering [7] is an objective quality measure that can be used in several image restoration and image quality assessment systems. In this paper, we compare on different image databases, the results obtained with DAF and with some subjective image quality assessments (Mean Opinion Score MOS as measure of perceived image quality). We study also the correlation between objective measure and MOS. In our experiments, we have used for the first image

Embryonic Heart Morphogenesis from Confocal Microscopy Imaging and Automatic Segmentation

Directory of Open Access Journals (Sweden)

Hongda Mao

2013-01-01

Full Text Available Embryonic heart morphogenesis (EHM is a complex and dynamic process where the heart transforms from a single tube into a four-chambered pump. This process is of great biological and clinical interest but is still poorly understood for two main reasons. On the one hand, the existing imaging modalities for investigating EHM suffered from either limited penetration depth or limited spatial resolution. On the other hand, current works typically adopted manual segmentation, which was tedious, subjective, and time consuming considering the complexity of developing heart geometry and the large size of images. In this paper, we propose to utilize confocal microscopy imaging with tissue optical immersion clearing technique to image the heart at different stages of development for EHM study. The imaging method is able to produce high spatial resolution images and achieve large penetration depth at the same time. Furthermore, we propose a novel convex active contour model for automatic image segmentation. The model has the ability to deal with intensity fall-off in depth which is characterized by confocal microscopy images. We acquired the images of embryonic quail hearts from day 6 to day 14 of incubation for EHM study. The experimental results were promising and provided us with an insight view of early heart growth pattern and also paved the road for data-driven heart growth modeling.

Automated Image Analysis of Lung Branching Morphogenesis from Microscopic Images of Fetal Rat Explants

Science.gov (United States)

Rodrigues, Pedro L.; Rodrigues, Nuno F.; Duque, Duarte; Granja, Sara; Correia-Pinto, Jorge; Vilaça, João L.

2014-01-01

Background. Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development from microscopic images. Methods. The outer contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers. PMID:25250057

Automated Image Analysis of Lung Branching Morphogenesis from Microscopic Images of Fetal Rat Explants

Directory of Open Access Journals (Sweden)

Pedro L. Rodrigues

2014-01-01

Full Text Available Background. Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development from microscopic images. Methods. The outer contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers.

Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images.

Science.gov (United States)

Pang, Jincheng; Özkucur, Nurdan; Ren, Michael; Kaplan, David L; Levin, Michael; Miller, Eric L

2015-11-01

Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.

Image analysis for material characterisation

Science.gov (United States)

Livens, Stefan

In this thesis, a number of image analysis methods are presented as solutions to two applications concerning the characterisation of materials. Firstly, we deal with the characterisation of corrosion images, which is handled using a multiscale texture analysis method based on wavelets. We propose a feature transformation that deals with the problem of rotation invariance. Classification is performed with a Learning Vector Quantisation neural network and with combination of outputs. In an experiment, 86,2% of the images showing either pit formation or cracking, are correctly classified. Secondly, we develop an automatic system for the characterisation of silver halide microcrystals. These are flat crystals with a triangular or hexagonal base and a thickness in the 100 to 200 nm range. A light microscope is used to image them. A novel segmentation method is proposed, which allows to separate agglomerated crystals. For the measurement of shape, the ratio between the largest and the smallest radius yields the best results. The thickness measurement is based on the interference colours that appear for light reflected at the crystals. The mean colour of different thickness populations is determined, from which a calibration curve is derived. With this, the thickness of new populations can be determined accurately.

Multispectral UV imaging for surface analysis of MUPS tablets with special focus on the pellet distribution

DEFF Research Database (Denmark)

Novikova, Anna; Carstensen, Jens Michael; Rades, Thomas

2016-01-01

In the present study the applicability of multispectral UV imaging in combination with multivariate image analysis for surface evaluation of MUPS tablets was investigated with respect to the differentiation of the API pellets from the excipients matrix, estimation of the drug content as well as p...... image analysis is a promising approach for the automatic quality control of MUPS tablets during the manufacturing process....

Extended -Regular Sequence for Automated Analysis of Microarray Images

Directory of Open Access Journals (Sweden)

Jin Hee-Jeong

2006-01-01

Full Text Available Microarray study enables us to obtain hundreds of thousands of expressions of genes or genotypes at once, and it is an indispensable technology for genome research. The first step is the analysis of scanned microarray images. This is the most important procedure for obtaining biologically reliable data. Currently most microarray image processing systems require burdensome manual block/spot indexing work. Since the amount of experimental data is increasing very quickly, automated microarray image analysis software becomes important. In this paper, we propose two automated methods for analyzing microarray images. First, we propose the extended -regular sequence to index blocks and spots, which enables a novel automatic gridding procedure. Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images. Experimental results show that the proposed methods are more reliable and convenient than the commercial tools.

Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration.

Science.gov (United States)

Chang, Herng-Hua; Li, Cheng-Yuan; Gallogly, Audrey Haihong

2018-02-01

Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components. To hasten the computation, parallel computing based on the graphics processing unit (GPU) strategy is explored with emphasis on memory allocations and thread distributions. To automate the filtration, image texture feature analysis associated with machine learning is investigated. Among the 98 candidate features, the sequential forward floating selection scheme is employed to acquire the optimal texture features for regularization. Subsequently, a two-stage classifier that consists of support vector machines and artificial neural networks is established to predict the filter parameters for automation. A speedup gain of 757 was reached to process an entire MR image volume of 256 × 256 × 256 pixels, which completed within 0.5 s. Automatic restoration results revealed high accuracy with an ensemble average relative error of 0.53 ± 0.85% in terms of the peak signal-to-noise ratio. This self-regulating trilateral filter outperformed many state-of-the-art noise reduction methods both qualitatively and quantitatively. We believe that this new image restoration algorithm is of potential in many brain MR image processing applications that require expedition and automation.

Semi-automatic watershed medical image segmentation methods for customized cancer radiation treatment planning simulation

International Nuclear Information System (INIS)

Kum Oyeon; Kim Hye Kyung; Max, N.

2007-01-01

A cancer radiation treatment planning simulation requires image segmentation to define the gross tumor volume, clinical target volume, and planning target volume. Manual segmentation, which is usual in clinical settings, depends on the operator's experience and may, in addition, change for every trial by the same operator. To overcome this difficulty, we developed semi-automatic watershed medical image segmentation tools using both the top-down watershed algorithm in the insight segmentation and registration toolkit (ITK) and Vincent-Soille's bottom-up watershed algorithm with region merging. We applied our algorithms to segment two- and three-dimensional head phantom CT data and to find pixel (or voxel) numbers for each segmented area, which are needed for radiation treatment optimization. A semi-automatic method is useful to avoid errors incurred by both human and machine sources, and provide clear and visible information for pedagogical purpose. (orig.)

Historical Image Registration and Land-Use Land-Cover Change Analysis

Directory of Open Access Journals (Sweden)

Fang-Ju Jao

2014-12-01

Full Text Available Historical aerial images are important to retain past ground surface information. The land-use land-cover change in the past can be identified using historical aerial images. Automatic historical image registration and stitching is essential because the historical image pose information was usually lost. In this study, the Scale Invariant Feature Transform (SIFT algorithm was used for feature extraction. Subsequently, the present study used the automatic affine transformation algorithm for historical image registration, based on SIFT features and control points. This study automatically determined image affine parameters and simultaneously transformed from an image coordinate system to a ground coordinate system. After historical aerial image registration, the land-use land-cover change was analyzed between two different years (1947 and 1975 at the Tseng Wen River estuary. Results show that sandbars and water zones were transformed into a large number of fish ponds between 1947 and 1975.

Semi-automatic analysis of standard uptake values in serial PET/CT studies in patients with lung cancer and lymphoma

Directory of Open Access Journals (Sweden)

Ly John

2012-04-01

Full Text Available Abstract Background Changes in maximum standardised uptake values (SUVmax between serial PET/CT studies are used to determine disease progression or regression in oncologic patients. To measure these changes manually can be time consuming in a clinical routine. A semi-automatic method for calculation of SUVmax in serial PET/CT studies was developed and compared to a conventional manual method. The semi-automatic method first aligns the serial PET/CT studies based on the CT images. Thereafter, the reader selects an abnormal lesion in one of the PET studies. After this manual step, the program automatically detects the corresponding lesion in the other PET study, segments the two lesions and calculates the SUVmax in both studies as well as the difference between the SUVmax values. The results of the semi-automatic analysis were compared to that of a manual SUVmax analysis using a Philips PET/CT workstation. Three readers did the SUVmax readings in both methods. Sixteen patients with lung cancer or lymphoma who had undergone two PET/CT studies were included. There were a total of 26 lesions. Results Linear regression analysis of changes in SUVmax show that intercepts and slopes are close to the line of identity for all readers (reader 1: intercept = 1.02, R2 = 0.96; reader 2: intercept = 0.97, R2 = 0.98; reader 3: intercept = 0.99, R2 = 0.98. Manual and semi-automatic method agreed in all cases whether SUVmax had increased or decreased between the serial studies. The average time to measure SUVmax changes in two serial PET/CT examinations was four to five times longer for the manual method compared to the semi-automatic method for all readers (reader 1: 53.7 vs. 10.5 s; reader 2: 27.3 vs. 6.9 s; reader 3: 47.5 vs. 9.5 s; p Conclusions Good agreement was shown in assessment of SUVmax changes between manual and semi-automatic method. The semi-automatic analysis was four to five times faster to perform than the manual analysis. These findings show the

Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

Science.gov (United States)

Sentana, I. W. B.; Jawas, N.; Asri, S. A.

2018-01-01

Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

Automatic generation of absolute myocardial blood flow images using [15O]H2O and a clinical PET/CT scanner.

Science.gov (United States)

Harms, Hendrik J; Knaapen, Paul; de Haan, Stefan; Halbmeijer, Rick; Lammertsma, Adriaan A; Lubberink, Mark

2011-05-01

Parametric imaging of absolute myocardial blood flow (MBF) using [(15)O]H(2)O enables determination of MBF with high spatial resolution. The aim of this study was to develop a method for generating reproducible, high-quality and quantitative parametric MBF images with minimal user intervention. Nineteen patients referred for evaluation of MBF underwent rest and adenosine stress [(15)O]H(2)O positron emission tomography (PET) scans. Ascending aorta and right ventricular (RV) cavity volumes of interest (VOIs) were used as input functions. Implementation of a basis function method (BFM) of the single-tissue model with an additional correction for RV spillover was used to generate parametric images. The average segmental MBF derived from parametric images was compared with MBF obtained using nonlinear least-squares regression (NLR) of VOI data. Four segmentation algorithms were evaluated for automatic extraction of input functions. Segmental MBF obtained using these input functions was compared with MBF obtained using manually defined input functions. The average parametric MBF showed a high agreement with NLR-derived MBF [intraclass correlation coefficient (ICC) = 0.984]. For each segmentation algorithm there was at least one implementation that yielded high agreement (ICC > 0.9) with manually obtained input functions, although MBF calculated using each algorithm was at least 10% higher. Cluster analysis with six clusters yielded the highest agreement (ICC = 0.977), together with good segmentation reproducibility (coefficient of variation of MBF generated automatically using cluster analysis and a implementation of a BFM of the single-tissue model with additional RV spillover correction.

Quantitative right and left ventricular functional analysis during gated whole-chest MDCT: A feasibility study comparing automatic segmentation to semi-manual contouring

International Nuclear Information System (INIS)

Coche, Emmanuel; Walker, Matthew J.; Zech, Francis; Crombrugghe, Rodolphe de; Vlassenbroek, Alain

2010-01-01

Purpose: To evaluate the feasibility of an automatic, whole-heart segmentation algorithm for measuring global heart function from gated, whole-chest MDCT images. Material and methods: 15 patients with suspicion of PE underwent whole-chest contrast-enhanced MDCT with retrospective ECG synchronization. Two observers computed right and left ventricular functional indices using a semi-manual and an automatic whole-heart segmentation algorithm. The two techniques were compared using Bland-Altman analysis and paired Student's t-test. Measurement reproducibility was calculated using intraclass correlation coefficient. Results: Ventricular analysis with automatic segmentation was successful in 13/15 (86%) and in 15/15 (100%) patients for the right ventricle and left ventricle, respectively. Reproducibility of measurements for both ventricles was perfect (ICC: 1.00) and very good for automatic and semi-manual measurements, respectively. Ventricular volumes and functional indices except right ventricular ejection fraction obtained from the automatic method were significantly higher for the RV compared to the semi-manual methods. Conclusions: The automatic, whole-heart segmentation algorithm enabled highly reproducible global heart function to be rapidly obtained in patients undergoing gated whole-chest MDCT for assessment of acute chest pain with suspicion of pulmonary embolism.

Automatic analysis of charged particle spectra

International Nuclear Information System (INIS)

Seres, Z.; Kiss, A.

1975-11-01

A computer program system is developed for off-line automatic analysis of a series of charged particle spectra measured by solid-state detectors and collected on magnetic tapes. The procedure results in complete angular distributions for the excited levels of the final nucleus up to about 15 MeV. (orig.) [de

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

DEFF Research Database (Denmark)

Jørgensen, Peter Stanley; Bowen, Jacob R.

2008-01-01

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

Image processing. A system for the automatic sorting of chromosomes; Traitement d'images - Applications au classement des chromosomes

Energy Technology Data Exchange (ETDEWEB)

Najai, Amor

1977-05-27

The present paper deals with two aspects of the system: - an automata (specialized hardware) dedicated to image processing. Images are digitized, divided into sub-units and computations are carried out on their main parameters. - A software for the automatic recognition and sorting of chromosomes is implemented on a Multi-20 minicomputer, connected to the automata. (author) [French] Nous decrivons un systeme automatique de classification de chromosomes. Il se compose de: - l'A.S.T.I., Automate Specialise de Traitement d'Images permettant de numeriser celles-ci, d'isoler des sous-images, d'effectuer des calculs sur leurs parametres principaux. - Un programme de reconnaissance et de classification automatique des chromosomes implante sur un mini-ordinateur MULTI-20, couple a l'A.S.T.I. (auteur)

Semi-automatic geographic atrophy segmentation for SD-OCT images.

Science.gov (United States)

Chen, Qiang; de Sisternes, Luis; Leng, Theodore; Zheng, Luoluo; Kutzscher, Lauren; Rubin, Daniel L

2013-01-01

Geographic atrophy (GA) is a condition that is associated with retinal thinning and loss of the retinal pigment epithelium (RPE) layer. It appears in advanced stages of non-exudative age-related macular degeneration (AMD) and can lead to vision loss. We present a semi-automated GA segmentation algorithm for spectral-domain optical coherence tomography (SD-OCT) images. The method first identifies and segments a surface between the RPE and the choroid to generate retinal projection images in which the projection region is restricted to a sub-volume of the retina where the presence of GA can be identified. Subsequently, a geometric active contour model is employed to automatically detect and segment the extent of GA in the projection images. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to qualitatively and quantitatively evaluate the proposed GA segmentation method. Experimental results suggest that the proposed algorithm can achieve high segmentation accuracy. The mean GA overlap ratios between our proposed method and outlines drawn in the SD-OCT scans, our method and outlines drawn in the fundus auto-fluorescence (FAF) images, and the commercial software (Carl Zeiss Meditec proprietary software, Cirrus version 6.0) and outlines drawn in FAF images were 72.60%, 65.88% and 59.83%, respectively.

Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

International Nuclear Information System (INIS)

Neubert, A.; Yang, Z.; Engstrom, C.; Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S.; Fripp, J.

2016-01-01

Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

Energy Technology Data Exchange (ETDEWEB)

Neubert, A., E-mail: ales.neubert@csiro.au [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane 4029 (Australia); Yang, Z. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 (China); Engstrom, C. [School of Human Movement Studies, University of Queensland, Brisbane 4072 (Australia); Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072 (Australia); Fripp, J. [The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, 4029 (Australia)

2016-10-15

Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

Development and preliminary validation of an automatic digital ...

African Journals Online (AJOL)

Amanda Chulayo

2017-10-02

Oct 2, 2017 ... based on an automatic digital analysis system (ADAS) that allows the capture of a series of real-time ... image analysing technology, with the accelerated advance of the hardware and software ..... Enables use of car battery.

Automatic prostate localization on cone-beam CT scans for high precision image-guided radiotherapy

NARCIS (Netherlands)

Smitsmans, Monique H. P.; de Bois, Josien; Sonke, Jan-Jakob; Betgen, Anja; Zijp, Lambert J.; Jaffray, David A.; Lebesque, Joos V.; van Herk, Marcel

2005-01-01

PURPOSE: Previously, we developed an automatic three-dimensional gray-value registration (GR) method for fast prostate localization that could be used during online or offline image-guided radiotherapy. The method was tested on conventional computed tomography (CT) scans. In this study, the

A Novel, Automatic Quality Control Scheme for Real Time Image Transmission

Directory of Open Access Journals (Sweden)

S. Ramachandran

2002-01-01

Full Text Available A novel scheme to compute energy on-the-fly and thereby control the quality of the image frames dynamically is presented along with its FPGA implementation. This scheme is suitable for incorporation in image compression systems such as video encoders. In this new scheme, processing is automatically stopped when the desired quality is achieved for the image being processed by using a concept called pruning. Pruning also increases the processing speed by a factor of more than two when compared to the conventional method of processing without pruning. An MPEG-2 encoder implemented using this scheme is capable of processing good quality monochrome and color images of sizes up to 1024 × 768 pixels at the rate of 42 and 28 frames per second, respectively, with a compression ratio of over 17:1. The encoder is also capable of working in the fixed pruning level mode with user programmable features.

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

Directory of Open Access Journals (Sweden)

Tatiana M. Pinho

2017-01-01

Full Text Available Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is a major concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range devices. However, this issue can be handled by High Dynamic Range (HDR images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

Automatic fog detection for public safety by using camera images

Science.gov (United States)

Pagani, Giuliano Andrea; Roth, Martin; Wauben, Wiel

2017-04-01

Fog and reduced visibility have considerable impact on the performance of road, maritime, and aeronautical transportation networks. The impact ranges from minor delays to more serious congestions or unavailability of the infrastructure and can even lead to damage or loss of lives. Visibility is traditionally measured manually by meteorological observers using landmarks at known distances in the vicinity of the observation site. Nowadays, distributed cameras facilitate inspection of more locations from one remote monitoring center. The main idea is, however, still deriving the visibility or presence of fog by an operator judging the scenery and the presence of landmarks. Visibility sensors are also used, but they are rather costly and require regular maintenance. Moreover, observers, and in particular sensors, give only visibility information that is representative for a limited area. Hence the current density of visibility observations is insufficient to give detailed information on the presence of fog. Cameras are more and more deployed for surveillance and security reasons in cities and for monitoring traffic along main transportation ways. In addition to this primary use of cameras, we consider cameras as potential sensors to automatically identify low visibility conditions. The approach that we follow is to use machine learning techniques to determine the presence of fog and/or to make an estimation of the visibility. For that purpose a set of features are extracted from the camera images such as the number of edges, brightness, transmission of the image dark channel, fractal dimension. In addition to these image features, we also consider meteorological variables such as wind speed, temperature, relative humidity, and dew point as additional features to feed the machine learning model. The results obtained with a training and evaluation set consisting of 10-minute sampled images for two KNMI locations over a period of 1.5 years by using decision trees methods

Digital Imaging and Piezo-dispenser Actuator in Automatic Flocculation Control

Directory of Open Access Journals (Sweden)

Jani TOMPERI

2012-01-01

Full Text Available This study presents an image-based on-line control system for a coiled pipe flocculator. A digital imaging technique developed previously is utilized to measure the characteristic floc size and a high-pressure piezo-dispenser is introduced for accurate dosing and rapid mixing of the flocculant. The controller is a conventional PI controller. Step change experiments on feed water quality, flow rate and desired floc size have been carried out for controller tuning and testing. The paper shows that the piezo-dispenser provides better flocculation results than a conventional dosing pump, and the flocculation result can be automatically controlled even when the feed water quality rapidly changes. The proposed flocculator is a simple, inexpensive and practical system for long-term laboratory tests to investigate the functionality of flocculants on varying feed waters.

Automatic track counting with an optic RAM-based instrument

International Nuclear Information System (INIS)

Staderini, E.M.; Castellano, Alfredo

1986-01-01

A new image sensor, the optic RAM, is now used in a microprocessor controlled instrument to read and digitize images from CR39 solid state nuclear track detectors. The system performs image analysis, filtering, tracks counting and evaluation in a fully automatic way, not requiring an optic microscope, nor photographic or television devices. The proposed system is a very compact and low power device. (author)

Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network

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Vasuki, Perumal; Roomi, S. Mohamed Mansoor

2013-01-01

Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.

AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES

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J. Kang

2016-06-01

Full Text Available In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.

Feeding People's Curiosity: Leveraging the Cloud for Automatic Dissemination of Mars Images

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Knight, David; Powell, Mark

2013-01-01

Smartphones and tablets have made wireless computing ubiquitous, and users expect instant, on-demand access to information. The Mars Science Laboratory (MSL) operations software suite, MSL InterfaCE (MSLICE), employs a different back-end image processing architecture compared to that of the Mars Exploration Rovers (MER) in order to better satisfy modern consumer-driven usage patterns and to offer greater server-side flexibility. Cloud services are a centerpiece of the server-side architecture that allows new image data to be delivered automatically to both scientists using MSLICE and the general public through the MSL website (http://mars.jpl.nasa.gov/msl/).

Automatic adjustment of display window (gray-level condition) for MR images using neural networks

International Nuclear Information System (INIS)

Ohhashi, Akinami; Nambu, Kyojiro.

1992-01-01

We have developed a system to automatically adjust the display window width and level (WWL) for MR images using neural networks. There were three main points in the development of our system as follows: 1) We defined an index for the clarity of a displayed image, and called 'EW'. EW is a quantitative measure of the clarity of an image displayed in a certain WWL, and can be derived from the difference between gray-level with the WWL adjusted by a human expert and with a certain WWL. 2) We extracted a group of six features from a gray-level histogram of a displayed image. We designed two neural networks which are able to learn the relationship between these features and the desired output (teaching signal), 'EQ', which is normalized to 0 to 1.0 from EW. Two neural networks were used to share the patterns to be learned; one learns a variety of patterns with less accuracy, and the other learns similar patterns with accuracy. Learning was performed using a back-propagation method. As a result, the neural networks after learning are able to provide a quantitative measure, 'Q', of the clarity of images displayed in the designated WWL. 3) Using the 'Hill climbing' method, we have been able to determine the best possible WWL for a displaying image. We have tested this technique for MR brain images. The results show that this system can adjust WWL comparable to that adjusted by a human expert for the majority of test images. The neural network is effective for the automatic adjustment of the display window for MR images. We are now studying the application of this method to MR images of another regions. (author)

Micronuclei frequency in circulating erythrocytes from rainbow trout (Oncorhynchus mykiss) subjected to radiation, an image analysis and flow cytometric study

International Nuclear Information System (INIS)

Schultz, N.; Norrgren, L.; Grawe, J.; Johannisson, A.; Medhage, O.

1993-01-01

Rainbow trout (oncorhynchus mykiss) were exposed to a single X-ray dose of 4 Gy. The frequency of micronuclei in the peripheral erythrocytes was investigated at regular intervals up to 58 days after the exposure. A flow cytometric method and a semi-automatic image analysis method were used to estimate the micronuclei frequency. The results show that both methods can detect an increased frequency of micronuclei in peripheral erythrocytes from exposed fish. However, the semi-automatic image analysis method was the most stable and sensitive. (Author)

Image analysis and machine learning for detecting malaria.

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Poostchi, Mahdieh; Silamut, Kamolrat; Maude, Richard J; Jaeger, Stefan; Thoma, George

2018-04-01

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis. Published by Elsevier Inc.

Automatic detection of micro-aneurysms in retinal images based on curvelet transform and morphological operations

Science.gov (United States)

Mohammad Alipour, Shirin Hajeb; Rabbani, Hossein

2013-09-01

Diabetic retinopathy (DR) is one of the major complications of diabetes that changes the blood vessels of the retina and distorts patient vision that finally in high stages can lead to blindness. Micro-aneurysms (MAs) are one of the first pathologies associated with DR. The number and the location of MAs are very important in grading of DR. Early diagnosis of micro-aneurysms (MAs) can reduce the incidence of blindness. As MAs are tiny area of blood protruding from vessels in the retina and their size is about 25 to 100 microns, automatic detection of these tiny lesions is still challenging. MAs occurring in the macula can lead to visual loss. Also the position of a lesion such as MAs relative to the macula is a useful feature for analysis and classification of different stages of DR. Because MAs are more distinguishable in fundus fluorescin angiography (FFA) compared to color fundus images, we introduce a new method based on curvelet transform and morphological operations for MAs detection in FFA images. As vessels and MAs are the bright parts of FFA image, firstly extracted vessels by curvelet transform are removed from image. Then morphological operations are applied on resulted image for detecting MAs.

CIE L*a*b*: comparison of digital images obtained photographically by manual and automatic modes

Directory of Open Access Journals (Sweden)

Fabiana Takatsui

2012-12-01

Full Text Available The aim of this study was to analyze the color alterations performed by the CIE L*a*b* system in the digital imaging of shade guide tabs, which were obtained photographically according to the automatic and manual modes. This study also sought to examine the observers' agreement in quantifying the coordinates. Four Vita Lumin Vaccum shade guide tabs were used: A3.5, B1, B3 and C4. An EOS Canon digital camera was used to record the digital images of the shade tabs, and the images were processed using Adobe Photoshop software. A total of 80 observations (five replicates of each shade according to two observers in two modes, specifically, automatic and manual were obtained, leading to color values of L*, a* and b*. The color difference (ΔE between the modes was calculated and classified as either clinically acceptable or unacceptable. The results indicated that there was agreement between the two observers in obtaining the L*, a* and b* values related to all guides. However, the B1, B3, and C4 shade tabs had ΔE values classified as clinically acceptable (ΔE = 0.44, ΔE = 2.04 and ΔE = 2.69, respectively. The A3.5 shade tab had a ΔE value classified as clinically unacceptable (ΔE = 4.17, as it presented higher values for luminosity in the automatic mode (L* = 54.0 than in the manual mode (L* = 50.6. It was concluded that the B1, B3 and C4 shade tabs can be used at any of the modes in digital camera (manual or automatic, which was a different finding from that observed for the A3.5 shade tab.

An Automatic Segmentation Method Combining an Active Contour Model and a Classification Technique for Detecting Polycomb-group Proteinsin High-Throughput Microscopy Images.

Science.gov (United States)

Gregoretti, Francesco; Cesarini, Elisa; Lanzuolo, Chiara; Oliva, Gennaro; Antonelli, Laura

2016-01-01

The large amount of data generated in biological experiments that rely on advanced microscopy can be handled only with automated image analysis. Most analyses require a reliable cell image segmentation eventually capable of detecting subcellular structures.We present an automatic segmentation method to detect Polycomb group (PcG) proteins areas isolated from nuclei regions in high-resolution fluorescent cell image stacks. It combines two segmentation algorithms that use an active contour model and a classification technique serving as a tool to better understand the subcellular three-dimensional distribution of PcG proteins in live cell image sequences. We obtained accurate results throughout several cell image datasets, coming from different cell types and corresponding to different fluorescent labels, without requiring elaborate adjustments to each dataset.

An Automatic Framework Using Space-Time Processing and TR-MUSIC for Subsurface and Through-Wall Multitarget Imaging

Directory of Open Access Journals (Sweden)

Si-hao Tan

2012-01-01

Full Text Available We present an automatic framework combined space-time signal processing with Time Reversal electromagnetic (EM inversion for subsurface and through-wall multitarget imaging using electromagnetic waves. This framework is composed of a frequency-wavenumber (FK filter to suppress direct wave and medium bounce, a FK migration algorithm to automatically estimate the number of targets and identify target regions, which can be used to reduce the computational complexity of the following imaging algorithm, and a EM inversion algorithm using Time Reversal Multiple Signal Classification (TR-MUSIC to reconstruct hidden objects. The feasibility of the framework is demonstrated with simulated data generated by GPRMAX.

CALIPSO: an interactive image analysis software package for desktop PACS workstations

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Ratib, Osman M.; Huang, H. K.

1990-07-01

The purpose of this project is to develop a low cost workstation for quantitative analysis of multimodality images using a Macintosh II personal computer. In the current configuration the Macintosh operates as a stand alone workstation where images are imported either from a central PACS server through a standard Ethernet network or recorded through video digitizer board. The CALIPSO software developed contains a large variety ofbasic image display and manipulation tools. We focused our effort however on the design and implementation ofquantitative analysis methods that can be applied to images from different imaging modalities. Analysis modules currently implemented include geometric and densitometric volumes and ejection fraction calculation from radionuclide and cine-angiograms Fourier analysis ofcardiac wall motion vascular stenosis measurement color coded parametric display of regional flow distribution from dynamic coronary angiograms automatic analysis ofmyocardial distribution ofradiolabelled tracers from tomoscintigraphic images. Several of these analysis tools were selected because they use similar color coded andparametric display methods to communicate quantitative data extracted from the images. 1. Rationale and objectives of the project Developments of Picture Archiving and Communication Systems (PACS) in clinical environment allow physicians and radiologists to assess radiographic images directly through imaging workstations (''). This convenient access to the images is often limited by the number of workstations available due in part to their high cost. There is also an increasing need for quantitative analysis ofthe images. During thepast decade