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Sample records for vessel segmentation algorithm

  1. Segmentation of vessels: the corkscrew algorithm

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    Wesarg, Stefan; Firle, Evelyn A.

    2004-05-01

    Medical imaging is nowadays much more than only providing data for diagnosis. It also links 'classical' diagnosis to modern forms of treatment such as image guided surgery. Those systems require the identification of organs, anatomical regions of the human body etc., i. e. the segmentation of structures from medical data sets. The algorithms used for these segmentation tasks strongly depend on the object to be segmented. One structure which plays an important role in surgery planning are vessels that are found everywhere in the human body. Several approaches for their extraction already exist. However, there is no general one which is suitable for all types of data or all sorts of vascular structures. This work presents a new algorithm for the segmentation of vessels. It can be classified as a skeleton-based approach working on 3D data sets, and has been designed for a reliable segmentation of coronary arteries. The algorithm is a semi-automatic extraction technique requiring the definition of the start and end the point of the (centerline) path to be found. A first estimation of the vessel's centerline is calculated and then corrected iteratively by detecting the vessel's border perpendicular to the centerline. We used contrast enhanced CT data sets of the thorax for testing our approach. Coronary arteries have been extracted from the data sets using the 'corkscrew algorithm' presented in this work. The segmentation turned out to be robust even if moderate breathing artifacts were present in the data sets.

  2. Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation.

    Science.gov (United States)

    Phellan, Renzo; Forkert, Nils D

    2017-11-01

    Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented flux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona-Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). The filters were evaluated and compared based on time-of-flight MRA datasets and corresponding manual segmentations from 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave-one-out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting nonenhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion-based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular-shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM

  3. Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images.

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    Wang, Rui; Li, Chao; Wang, Jie; Wei, Xiaoer; Li, Yuehua; Zhu, Yuemin; Zhang, Su

    2015-02-15

    Cerebrovascular segmentation plays an important role in medical diagnosis. This study was conducted to develop a threshold segmentation algorithm for automatic extraction and volumetric quantification of cerebral vessels on brain magnetic resonance angiography (MRA) images. The MRA images of 10 individuals were acquired using a 3 Tesla MR scanner (Intera-achieva SMI-2.1, Philips Medical Systems). Otsu's method was used to divide the brain MRA images into two parts, namely, foreground and background regions. To extract the cerebral vessels, we performed the threshold segmentation algorithm on the foreground region by comparing two different statistical distributions. Automatically segmented vessels were compared with manually segmented vessels. Different similarity metrics were used to assess the changes in segmentation performance as a function of a weighted parameter w used in segmentation algorithm. Varying w from 2 to 100 resulted in a false positive rate ranging from 117% to 3.21%, and a false negative rate ranging from 8.23% to 28.97%. The Dice similarity coefficient (DSC), which reflected the segmentation accuracy, initially increased and then decreased as w increased. The suggested range of values for w is [10, 20] given that the maximum DSC (e.g., DSC=0.84) was obtained within this range. The performance of our method was validated by comparing with manual segmentation. The proposed threshold segmentation method can be used to accurately and efficiently extract cerebral vessels from brain MRA images. Threshold segmentation may be used for studies focusing on three-dimensional visualization and volumetric quantification of cerebral vessels. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. A hybrid lung and vessel segmentation algorithm for computer aided detection of pulmonary embolism

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    Raghupathi, Laks; Lakare, Sarang

    2009-02-01

    Advances in multi-detector technology have made CT pulmonary angiography (CTPA) a popular radiological tool for pulmonary emboli (PE) detection. CTPA provide rich detail of lung anatomy and is a useful diagnostic aid in highlighting even very small PE. However analyzing hundreds of slices is laborious and time-consuming for the practicing radiologist which may also cause misdiagnosis due to the presence of various PE look-alike. Computer-aided diagnosis (CAD) can be a potential second reader in providing key diagnostic information. Since PE occurs only in vessel arteries, it is important to mark this region of interest (ROI) during CAD preprocessing. In this paper, we present a new lung and vessel segmentation algorithm for extracting contrast-enhanced vessel ROI in CTPA. Existing approaches to segmentation either provide only the larger lung area without highlighting the vessels or is computationally prohibitive. In this paper, we propose a hybrid lung and vessel segmentation which uses an initial lung ROI and determines the vessels through a series of refinement steps. We first identify a coarse vessel ROI by finding the "holes" from the lung ROI. We then use the initial ROI as seed-points for a region-growing process while carefully excluding regions which are not relevant. The vessel segmentation mask covers 99% of the 259 PE from a real-world set of 107 CTPA. Further, our algorithm increases the net sensitivity of a prototype CAD system by 5-9% across all PE categories in the training and validation data sets. The average run-time of algorithm was only 100 seconds on a standard workstation.

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

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

    2015-01-01

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

  6. Image Segmentation Algorithms Overview

    OpenAIRE

    Yuheng, Song; Hao, Yan

    2017-01-01

    The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a predi...

  7. Vessel segmentation in screening mammograms

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    Mordang, J. J.; Karssemeijer, N.

    2015-03-01

    Blood vessels are a major cause of false positives in computer aided detection systems for the detection of breast cancer. Therefore, the purpose of this study is to construct a framework for the segmentation of blood vessels in screening mammograms. The proposed framework is based on supervised learning using a cascade classifier. This cascade classifier consists of several stages where in each stage a GentleBoost classifier is trained on Haar-like features. A total of 30 cases were included in this study. In each image, vessel pixels were annotated by selecting pixels on the centerline of the vessel, control samples were taken by annotating a region without any visible vascular structures. This resulted in a total of 31,000 pixels marked as vascular and over 4 million control pixels. After training, the classifier assigns a vesselness likelihood to the pixels. The proposed framework was compared to three other vessel enhancing methods, i) a vesselness filter, ii) a gaussian derivative filter, and iii) a tubeness filter. The methods were compared in terms of area under the receiver operating characteristics curves, the Az values. The Az value of the cascade approach is 0:85. This is superior to the vesselness, Gaussian, and tubeness methods, with Az values of 0:77, 0:81, and 0:78, respectively. From these results, it can be concluded that our proposed framework is a promising method for the detection of vessels in screening mammograms.

  8. Vessel-guided airway tree segmentation

    DEFF Research Database (Denmark)

    Lo, Pechin Chien Pau; Sporring, Jon; Ashraf, Haseem

    2010-01-01

    method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the airway appearance model produces more complete airway segmentations, leading to on average 20% longer trees, and 50% less leakage......This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained...... to differentiate between airway and non-airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, airway tree segmentations. A vessel orientation...

  9. Effect of a novel motion correction algorithm (SSF) on the image quality of coronary CTA with intermediate heart rates: Segment-based and vessel-based analyses

    Energy Technology Data Exchange (ETDEWEB)

    Li, Qianwen, E-mail: qianwen18@126.com; Li, Pengyu, E-mail: lipyu818@gmail.com; Su, Zhuangzhi, E-mail: suzhuangzhi@xwh.ccmu.edu.cn; Yao, Xinyu, E-mail: 314985151@qq.com; Wang, Yan, E-mail: wy19851121@126.com; Wang, Chen, E-mail: fskwangchen@gmail.com; Du, Xiangying, E-mail: duxying_xw@163.com; Li, Kuncheng, E-mail: kuncheng.li@gmail.com

    2014-11-15

    Highlights: • SSF provided better image quality than single-sector and bi-sector reconstruction among the intermediate heart rates (65–75 bpm). • Evidence for the application of prospective ECG-triggered coronary CTA with SSF onto an expanded heart rate range. • Information about the inconsistent effectiveness of SSF among the segments of coronary artery. - Abstract: Purpose: To evaluate the effect of SnapShot Freeze (SSF) reconstruction at an intermediate heart-rate (HR) range (65–75 bpm) and compare this method with single-sector reconstruction and bi-sector reconstruction on segmental and vessel bases in retrospective coronary computed tomography angiography (CCTA). Materials and methods: Retrospective electrocardiogram-gated CCTA was performed on 37 consecutive patients with HR between 65 and 75 bpm using a 64-row CT scanner. Retrospective single-sector reconstruction, bi-sector reconstruction, and SSF were performed for each patient. Multi-phase single-sector reconstruction was performed to select the optimal phase. SSF and bi-sector images were also reconstructed at the optimal phase. The images were interpreted in an intent-to-diagnose fashion by two experienced readers using a 5-point scale, with 3 points as diagnostically acceptable. Image quality among the three reconstruction groups were compared on per-patient, per-vessel, and per-segment bases. Results: The average HR of the enrolled patients was 69.4 ± 2.7 bpm. A total of 111 vessels and 481 coronary segments were assessed. SSF provided significantly higher interpretability of the coronary segments than bi-sector reconstructions. The qualified and excellent rates of SSF (97.9% and 82.3%) were significantly higher than those of single-sector (92.9% and 66.3%) and bi-sector (90.9% and 64.7%) reconstructions. The image quality score (IQS) using SSF was also significantly higher than those of single-sector and bi-sector reconstructions both on per-patient and per-vessel bases. On per-segment

  10. Automatic Segmentation of Vessels in In-Vivo Ultrasound Scans

    DEFF Research Database (Denmark)

    Tamimi-Sarnikowski, Philip; Brink-Kjær, Andreas; Moshavegh, Ramin

    2017-01-01

    presents a fully automatic segmentation algorithm, for robustly segmenting the vessel lumen in longitudinal B-mode ultrasound images. The automatic segmentation is performed using a combination of B-mode and power Doppler images. The proposed algorithm includes a series of preprocessing steps, and performs...... a vessel segmentation by use of the marker-controlled watershed transform. The ultrasound images used in the study were acquired using the bk3000 ultrasound scanner (BK Ultrasound, Herlev, Denmark) with two transducers ”8L2 Linear” and ”10L2w Wide Linear” (BK Ultrasound, Herlev, Denmark). The algorithm...... was evaluated empirically and applied to a dataset of in-vivo 1770 images recorded from 8 healthy subjects. The segmentation results were compared to manual delineation performed by two experienced users. The results showed a sensitivity and specificity of 90.41 ± 11.2 % and 97.93 ± 5.7 % (mean ± standard...

  11. FRAMEWORK FOR COMPARING SEGMENTATION ALGORITHMS

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    G. Sithole

    2015-05-01

    Full Text Available The notion of a ‘Best’ segmentation does not exist. A segmentation algorithm is chosen based on the features it yields, the properties of the segments (point sets it generates, and the complexity of its algorithm. The segmentation is then assessed based on a variety of metrics such as homogeneity, heterogeneity, fragmentation, etc. Even after an algorithm is chosen its performance is still uncertain because the landscape/scenarios represented in a point cloud have a strong influence on the eventual segmentation. Thus selecting an appropriate segmentation algorithm is a process of trial and error. Automating the selection of segmentation algorithms and their parameters first requires methods to evaluate segmentations. Three common approaches for evaluating segmentation algorithms are ‘goodness methods’, ‘discrepancy methods’ and ‘benchmarks’. Benchmarks are considered the most comprehensive method of evaluation. This paper shortcomings in current benchmark methods are identified and a framework is proposed that permits both a visual and numerical evaluation of segmentations for different algorithms, algorithm parameters and evaluation metrics. The concept of the framework is demonstrated on a real point cloud. Current results are promising and suggest that it can be used to predict the performance of segmentation algorithms.

  12. Recent Advancements in Retinal Vessel Segmentation.

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    L Srinidhi, Chetan; Aparna, P; Rajan, Jeny

    2017-04-01

    Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.

  13. An automated method for accurate vessel segmentation

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    Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; (Tim Cheng, Kwang-Ting

    2017-05-01

    Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm’s growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008

  14. Multiscale Vessel-guided Airway Tree Segmentation

    DEFF Research Database (Denmark)

    Lo, Pechin Chien Pau; Sporring, Jon; de Bruijne, Marleen

    2009-01-01

    This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. The method uses a voxel classification based appearance model, which involves the use of a classifier...... is evaluated within EXACT’09 on a diverse set of CT scans. Results show a favorable combination of a relatively large portion of the tree detected correctly with very few false positives....

  15. Optic disc segmentation: level set methods and blood vessels inpainting

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    Almazroa, A.; Sun, Weiwei; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

    2017-03-01

    Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head (ONH) pathology such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of ONH abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique is applied. The algorithm is evaluated using a new retinal fundus image dataset called RIGA (Retinal Images for Glaucoma Analysis). In the case of low quality images, a double level set is applied in which the first level set is considered to be a localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as its agreement with manual markings by six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid is 83.9%, and the best agreement is observed between the results of the algorithm and manual markings in 379 images.

  16. Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

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    Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

    2017-03-01

    Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

  17. Segmenting Retinal Blood Vessels With Deep Neural Networks.

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    Liskowski, Pawel; Krawiec, Krzysztof

    2016-11-01

    The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400[Formula: see text]000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97 ). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87 ), and fares well on pathological cases.

  18. Liver vessel segmentation based on extreme learning machine.

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    Zeng, Ye Zhan; Zhao, Yu Qian; Liao, Miao; Zou, Bei Ji; Wang, Xiao Fang; Wang, Wei

    2016-05-01

    Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity. Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  19. An algorithm for segmenting range imagery

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    Roberts, R.S.

    1997-03-01

    This report describes the technical accomplishments of the FY96 Cross Cutting and Advanced Technology (CC&AT) project at Los Alamos National Laboratory. The project focused on developing algorithms for segmenting range images. The image segmentation algorithm developed during the project is described here. In addition to segmenting range images, the algorithm can fuse multiple range images thereby providing true 3D scene models. The algorithm has been incorporated into the Rapid World Modelling System at Sandia National Laboratory.

  20. Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA

    DEFF Research Database (Denmark)

    Schaap, Michiel; van Walsum, Theo; Neefjes, Lisan

    2011-01-01

    This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated...... with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation...... and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than...

  1. Spectral clustering algorithms for ultrasound image segmentation.

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    Archip, Neculai; Rohling, Robert; Cooperberg, Peter; Tahmasebpour, Hamid; Warfield, Simon K

    2005-01-01

    Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The extension of the NCut technique to the unsupervised clustering is first described. The novel segmentation algorithm is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. Comparisons with the classical NCut algorithm are also presented. Finally, segmentation results on other types of medical images are shown.

  2. An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

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    Rasha Al Shehhi

    2016-01-01

    Full Text Available This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH morphology and context and graph-analysis algorithms (e.g., Dijkstra path. The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

  3. Lung vessel segmentation in CT images using graph-cuts

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    Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

    2016-03-01

    Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

  4. An efficient algorithm for color image segmentation

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    Shikha Yadav

    2016-09-01

    Full Text Available In field of image processing, image segmentation plays an important role that focus on splitting the whole image into segments. Representation of an image so that it can be more easily analysed and involves more information is an important segmentation goal. The process of partitioning an image can be usually realized by Region based, Boundary based or edge based method. In this work a hybrid approach is followed that combines improved bee colony optimization and Tabu search for color image segmentation. The results produced from this hybrid approach are compared with non-sorted particle swarm optimization, non-sorted genetic algorithm and improved bee colony optimization. Results show that the Hybrid algorithm has better or somewhat similar performance as compared to other algorithms that are based on population. The algorithm is successfully implemented on MATLAB.

  5. Brain blood vessel segmentation using line-shaped profiles

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    Babin, Danilo; Pižurica, Aleksandra; De Vylder, Jonas; Vansteenkiste, Ewout; Philips, Wilfried

    2013-11-01

    Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

  6. Blood vessel segmentation in magnetic resonance angiography imagery

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    Kozaitis, S. P.; Chandramohan, R.

    2011-06-01

    Small blood vessels may be difficult to detect in magnetic resonance angiography due to the lack of blood flow caused by disease or injury. Our method, which uses a block-matching denoising approach to segment blood vessels, works well in the presence of noise. We examined extended regions of an image to determine whether they contained blood vessels by fitting a Gaussian mixture model to a region's histogram. Then, dissimilar regions were denoised separately. This approach was beneficial in low-contrast settings. It can be used to detect higher-order blood vessels that may be difficult to detect under normal conditions.

  7. Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation.

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    Javidi, Malihe; Pourreza, Hamid-Reza; Harati, Ahad

    2017-02-01

    Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image. To extract blood vessel, two separate dictionaries, for vessel and non-vessel, capable of providing reconstructive and discriminative information of the retinal image are learned. In the test step, an unseen retinal image is divided into overlapping patches and classified to vessel and non-vessel patches. Then, a voting scheme is applied to generate the binary vessel map. The proposed vessel segmentation method can achieve the accuracy of 95% and a sensitivity of 75% in the same range of specificity 97% on two public datasets. The results show that the proposed method can achieve comparable results to existing methods and decrease false positive vessels in abnormal retinal images with pathological regions. Microaneurysm (MA) is the earliest sign of DR that appears as a small red dot on the surface of the retina. Despite several attempts to develop automated MA detection systems, it is still a challenging problem. In this paper, a method for MA detection, which is similar to our vessel segmentation approach, is proposed. In our method, a candidate detection algorithm based on the Morlet wavelet is applied to identify all possible MA candidates. In the next step, two discriminative dictionaries with the ability to distinguish MA from non-MA object are learned. These dictionaries are then used to classify the detected candidate objects. The evaluations indicate that the proposed MA detection method achieves higher average sensitivity about 2

  8. Interactive segmentation techniques algorithms and performance evaluation

    CERN Document Server

    He, Jia; Kuo, C-C Jay

    2013-01-01

    This book focuses on interactive segmentation techniques, which have been extensively studied in recent decades. Interactive segmentation emphasizes clear extraction of objects of interest, whose locations are roughly indicated by human interactions based on high level perception. This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection. A comparative analysis of these methods will be provided

  9. Robust hepatic vessel segmentation using multi deep convolution network

    Science.gov (United States)

    Kitrungrotsakul, Titinunt; Han, Xian-Hua; Iwamoto, Yutaro; Foruzan, Amir Hossein; Lin, Lanfen; Chen, Yen-Wei

    2017-03-01

    Extraction of blood vessels of the organ is a challenging task in the area of medical image processing. It is really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the hepatic vessels from computed tomography (CT) image. We proposed novel deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of three deep convolution neural networks to extract features from difference planes of CT data. The three networks have share features at the first convolution layer but will separately learn their own features in the second layer. All three networks will join again at the top layer. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 12 CT volumes which training data are randomly generate from 5 CT volumes and 7 using for test. Our network can yield an average dice coefficient 0.830, while 3D deep convolution neural network can yield around 0.7 and multi-scale can yield only 0.6.

  10. Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation.

    Science.gov (United States)

    Annunziata, Roberto; Garzelli, Andrea; Ballerini, Lucia; Mecocci, Alessandro; Trucco, Emanuele

    2016-07-01

    Accurate vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper, we present a new unsupervised vessel segmentation approach to address this problem. A novel inpainting filter, called neighborhood estimator before filling, is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset and 95.81% on the HRF dataset.

  11. Vessel-guided airway segmentation based on voxel classification

    DEFF Research Database (Denmark)

    Lo, Pechin Chien Pau; Sporring, Jon; Ashraf, Haseem

    2008-01-01

    This paper presents a method for improving airway tree segmentation using vessel orientation information. We use the fact that an airway branch is always accompanied by an artery, with both structures having similar orientations. This work is based on a  voxel classification airway segmentation...... of the surroundings of a voxel, estimated based on a tube model, is to that of a neighboring vessel. The proposed method is tested on 20 CT images from different subjects selected randomly from a lung cancer screening study. Length of the airway branches from the results of the proposed method are significantly...

  12. Multi-level deep supervised networks for retinal vessel segmentation.

    Science.gov (United States)

    Mo, Juan; Zhang, Lei

    2017-12-01

    Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

  13. Optimization of reactor pressure vessel internals segmentation in Korea

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Byung-Sik [Dankook Univ., Chungnam (Korea, Republic of). Dept. of Nuclear Engineering

    2017-11-15

    One of the most challenging tasks during plant decommissioning is the removal of highly radioactive internal components from the reactor pressure vessel (RPV). For RPV internals dismantling, it is essential that all activities are thoroughly planned and discussed in the early stage of the decommissioning project. One of the key activities in the detailed planning is to prepare the segmentation and packaging plan that describes the sequential steps required to segment, separate, and package each individual component of RPV, based on an activation analysis and component characterization study.

  14. A computational algorithm addressing how vessel length might depend on vessel diameter

    Science.gov (United States)

    Jing Cai; Shuoxin Zhang; Melvin T. Tyree

    2010-01-01

    The objective of this method paper was to examine a computational algorithm that may reveal how vessel length might depend on vessel diameter within any given stem or species. The computational method requires the assumption that vessels remain approximately constant in diameter over their entire length. When this method is applied to three species or hybrids in the...

  15. Speech Emotion Recognition Using Voiced Segment Selection Algorithm

    NARCIS (Netherlands)

    Gu, Yu; Postma, Eric; Lin, H.X.; van den Herik, Jaap; Kaminka, Gal; Fox, Maria; Bouquet, Paolo; Hüllermeier, Eyke; Dignum, Virginia; Dignum, Frank; van Harmelen, FRank

    2016-01-01

    Speech emotion recognition (SER) poses one of the major challenges in human-machine interaction. We propose a new algorithm, the Voiced Segment Selection (VSS) algorithm, which can produce an accurate segmentation of speech signals. The VSS algorithm deals with the voiced signal segment as the

  16. Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain

    Science.gov (United States)

    Phellan, Renzo; Lindner, Thomas; Falcão, Alexandre X.; Forkert, Nils D.

    2017-03-01

    4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.

  17. Automatic segmentation of abdominal vessels for improved pancreas localization

    Science.gov (United States)

    Farag, Amal; Liu, Jiamin; Summers, Ronald M.

    2014-03-01

    Accurate automatic detection and segmentation of abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis as well as computer-aided diagnosis. The large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges making the task very difficult. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. Due to the close proximity of the pancreas to numerous surrounding organs within the abdominal cavity the organ shifts according to the conditions of the organs within the abdomen, as such the pancreas is constantly changing. Combining these challenges with typically found patient-to-patient variations and scanning conditions the pancreas becomes harder to localize. In this paper we focus on three abdominal vessels that almost always abut the pancreas tissue and as such useful landmarks to identify the relative location of the pancreas. The splenic and portal veins extend from the hila of the spleen and liver, respectively, travel through the abdominal cavity and join at a position close to the head of the pancreas known as the portal confluence. A third vein, the superior mesenteric vein, anastomoses with the other two veins at the portal confluence. An automatic segmentation framework for obtaining the splenic vein, portal confluence and superior mesenteric vein is proposed using 17 contrast enhanced computed-tomography datasets. The proposed method uses outputs from the multi-organ multi-atlas label fusion and Frangi vesselness filter to obtain automatic seed points for vessel tracking and generation of statistical models of the desired vessels. The approach shows ability to identify the vessels and improve localization of the pancreas within the abdomen.

  18. AN EFFICIENT SEGMENTATION ALGORITHM FOR ENTITY INTERACTION

    Directory of Open Access Journals (Sweden)

    Eugene Ch'ng

    2009-04-01

    Full Text Available The inventorying of biological diversity and studies in biocomplexity require the management of large electronic datasets of organisms. While species inventory has adopted structured electronic databases for some time, the computer modelling of the functional interactions between biological entities at all levels of life is still in the stage of development. One of the challenges for this type of modelling is the biotic interactions that occur between large datasets of entities represented as computer algorithms. In real-time simulation that models the biotic interactions of large population datasets, the use of computational processing time could be extensive. One way of increasing the efficiency of such simulation is to partition the landscape so that entities need only traverse its local space for entities that falls within the interaction proximity. This article presents an efficient segmentation algorithm for biotic interactions for research related to the modelling and simulation of biological systems.

  19. Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts.

    Science.gov (United States)

    Zeng, Ye-Zhan; Zhao, Yu-Qian; Tang, Ping; Liao, Miao; Liang, Yi-Xiong; Liao, Sheng-Hui; Zou, Bei-Ji

    2017-10-01

    Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Use of Gabor filters and deep networks in the segmentation of retinal vessel morphology

    Science.gov (United States)

    Leopold, Henry A.; Orchard, Jeff; Zelek, John; Lakshminarayanan, Vasudevan

    2017-02-01

    The segmentation of retinal morphology has numerous applications in assessing ophthalmologic and cardiovascular disease pathologies. The early detection of many such conditions is often the most effective method for reducing patient risk. Computer aided segmentation of the vasculature has proven to be a challenge, mainly due to inconsistencies such as noise, variations in hue and brightness that can greatly reduce the quality of fundus images. Accurate fundus and/or retinal vessel maps give rise to longitudinal studies able to utilize multimodal image registration and disease/condition status measurements, as well as applications in surgery preparation and biometrics. This paper further investigates the use of a Convolutional Neural Network as a multi-channel classifier of retinal vessels using the Digital Retinal Images for Vessel Extraction database, a standardized set of fundus images used to gauge the effectiveness of classification algorithms. The CNN has a feed-forward architecture and varies from other published architectures in its combination of: max-pooling, zero-padding, ReLU layers, batch normalization, two dense layers and finally a Softmax activation function. Notably, the use of Adam to optimize training the CNN on retinal fundus images has not been found in prior review. This work builds on prior work of the authors, exploring the use of Gabor filters to boost the accuracy of the system to 0.9478 during post processing. The mean of a series of Gabor filters with varying frequencies and sigma values are applied to the output of the network and used to determine whether a pixel represents a vessel or non-vessel.

  1. Research of the multimodal brain-tumor segmentation algorithm

    Science.gov (United States)

    Lu, Yisu; Chen, Wufan

    2015-12-01

    It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance.

  2. Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm

    OpenAIRE

    Khamael Abbas; Mustafa Rydh

    2012-01-01

    In this paper, a adopted approach to fully automatic satellite image segmentation, called JSEG, "JPEG image segmentation" is presented. First colors in the image are quantized to represent differentiate regions in the image. Then image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for “good” segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the “J-image”...

  3. KM_GrabCut: a fast interactive image segmentation algorithm

    Science.gov (United States)

    Li, Jianbo; Yao, Yiping; Tang, Wenjie

    2015-03-01

    Image segmentation is critical for image processing. Among several algorithms, GrabCut is well known by its little user interaction and desirable segmentation result. However, it needs to take a lot of time to adjust the Gaussian Mixture Model (GMM) and to cut the weighted graph with Max-Flow/Min-Cut Algorithm iteratively. To solve this problem, we first build a common algorithmic framework which can be shared by the class of GrabCut-like segmentation algorithms, and then propose KM_GrabCut algorithm based on this framework. The KM_GrabCut first uses K-means clustering algorithm to cluster pixels in foreground and background respectively, and then constructs a GMM based on each clustering result and cuts the corresponding weighted graph only once. Experimental results demonstrate that KM_GrabCut outperforms GrabCut with higher performance, comparable segmentation result and user interaction.

  4. An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

    Directory of Open Access Journals (Sweden)

    Ningning Zhou

    2014-01-01

    Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

  5. Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

    Directory of Open Access Journals (Sweden)

    Oludayo O. Olugbara

    2015-01-01

    Full Text Available Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

  6. Computerized detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): improvement of vessel segmentation

    Science.gov (United States)

    Zhou, Chuan; Chan, Heang-Ping; Kuriakose, Jean W.; Chughtai, Aamer; Hadjiiski, Lubomir M.; Wei, Jun; Patel, Smita; Kazerooni, Ella A.

    2011-03-01

    Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately when the vessel is occluded by PEs and/or surrounded by lymphoid tissues or lung diseases. In this study, we developed a method that combines MHES with level set refinement (MHES-LSR) to improve vessel segmentation accuracy. The level set was designed to propagate the initial object contours to the regions with relatively high gray-level, high gradient, and high compactness as measured by the smoothness of the curvature along vessel boundaries. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOI) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 31.7+/-10.9% using the MHES method to 7.7+/-4.7% using the MHES-LSR method. The correlation between the computer-segmented vessel volume and the reference standard was improved from 0.954 to 0.986. The accuracy of vessel segmentation was improved significantly (p<0.05). The MHES-LSR method may have the potential to improve PE detection.

  7. An algorithm for calculi segmentation on ureteroscopic images.

    Science.gov (United States)

    Rosa, Benoît; Mozer, Pierre; Szewczyk, Jérôme

    2011-03-01

    The purpose of the study is to develop an algorithm for the segmentation of renal calculi on ureteroscopic images. In fact, renal calculi are common source of urological obstruction, and laser lithotripsy during ureteroscopy is a possible therapy. A laser-based system to sweep the calculus surface and vaporize it was developed to automate a very tedious manual task. The distal tip of the ureteroscope is directed using image guidance, and this operation is not possible without an efficient segmentation of renal calculi on the ureteroscopic images. We proposed and developed a region growing algorithm to segment renal calculi on ureteroscopic images. Using real video images to compute ground truth and compare our segmentation with a reference segmentation, we computed statistics on different image metrics, such as Precision, Recall, and Yasnoff Measure, for comparison with ground truth. The algorithm and its parameters were established for the most likely clinical scenarii. The segmentation results are encouraging: the developed algorithm was able to correctly detect more than 90% of the surface of the calculi, according to an expert observer. Implementation of an algorithm for the segmentation of calculi on ureteroscopic images is feasible. The next step is the integration of our algorithm in the command scheme of a motorized system to build a complete operating prototype.

  8. Yarn image segmentation using the region growing algorithm

    Science.gov (United States)

    Fabijańska, Anna

    2011-11-01

    This paper is about the development of the image segmentation algorithm for the industrial measurement system. Specifically, the problem of segmentation of textile yarn images is considered. The algorithm developed for yarn hairiness analyzer is introduced. It aims at extracting single fibers protruding from the yarn core. The algorithm is a region growing-based approach where the growth of the region is guided and constrained by the coherence enhancing diffusion filter. Results of the proposed method are presented and compared with the results provided by the traditional clustering approaches and recent, well-established segmentation methods. The comparison proves that the proposed segmentation algorithm provides high quality results and significantly outperforms other methods in number of fibers extracted from the background.

  9. Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods

    Directory of Open Access Journals (Sweden)

    Saadia Zahid

    2015-01-01

    Full Text Available Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs with artificial neural networks (ANNs. Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.

  10. Multilevel Image Segmentation Based on an Improved Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2016-01-01

    Full Text Available Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.

  11. Software implementation and hardware acceleration of retinal vessel segmentation for diabetic retinopathy screening tests.

    Science.gov (United States)

    Cavinato, L; Fidone, I; Bacis, M; Del Sozzo, E; Durelli, G C; Santambrogio, M D

    2017-07-01

    Screening tests are an effective tool for the diagnosis and prevention of several diseases. Unfortunately, in order to produce an early diagnosis, the huge number of collected samples has to be processed faster than before. In particular this issue concerns image processing procedures, as they require a high computational complexity, which is not satisfied by modern software architectures. To this end, Field Programmable Gate Arrays (FPGAs) can be used to accelerate partially or entirely the computation. In this work, we demonstrate that the use of FPGAs is suitable for biomedical application, by proposing a case of study concerning the implementation of a vessels segmentation algorithm. The experimental results, computed on DRIVE and STARE databases, show remarkable improvements in terms of both execution time and power efficiency (6X and 5.7X respectively) compared to the software implementation. On the other hand, the proposed hardware approach outperforms literature works (3X speedup) without affecting the overall accuracy and sensitivity measures.

  12. Segmentation of thermographic images of hands using a genetic algorithm

    Science.gov (United States)

    Ghosh, Payel; Mitchell, Melanie; Gold, Judith

    2010-01-01

    This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.

  13. Roi Detection and Vessel Segmentation in Retinal Image

    Science.gov (United States)

    Sabaz, F.; Atila, U.

    2017-11-01

    Diabetes disrupts work by affecting the structure of the eye and afterwards leads to loss of vision. Depending on the stage of disease that called diabetic retinopathy, there are sudden loss of vision and blurred vision problems. Automated detection of vessels in retinal images is a useful study to diagnose eye diseases, disease classification and other clinical trials. The shape and structure of the vessels give information about the severity of the disease and the stage of the disease. Automatic and fast detection of vessels allows for a quick diagnosis of the disease and the treatment process to start shortly. ROI detection and vessel extraction methods for retinal image are mentioned in this study. It is shown that the Frangi filter used in image processing can be successfully used in detection and extraction of vessels.

  14. An algorithm to automate yeast segmentation and tracking.

    Directory of Open Access Journals (Sweden)

    Andreas Doncic

    Full Text Available Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.

  15. An algorithm to automate yeast segmentation and tracking.

    Science.gov (United States)

    Doncic, Andreas; Eser, Umut; Atay, Oguzhan; Skotheim, Jan M

    2013-01-01

    Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.

  16. Segmentation algorithms for ear image data towards biomechanical studies.

    Science.gov (United States)

    Ferreira, Ana; Gentil, Fernanda; Tavares, João Manuel R S

    2014-01-01

    In recent years, the segmentation, i.e. the identification, of ear structures in video-otoscopy, computerised tomography (CT) and magnetic resonance (MR) image data, has gained significant importance in the medical imaging area, particularly those in CT and MR imaging. Segmentation is the fundamental step of any automated technique for supporting the medical diagnosis and, in particular, in biomechanics studies, for building realistic geometric models of ear structures. In this paper, a review of the algorithms used in ear segmentation is presented. The review includes an introduction to the usually biomechanical modelling approaches and also to the common imaging modalities. Afterwards, several segmentation algorithms for ear image data are described, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples. Finally, the conclusions are presented as well as a discussion about possible trends for future research concerning the ear segmentation.

  17. A Novel Plant Root Foraging Algorithm for Image Segmentation Problems

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO. The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem.

  18. A Vessel Active Contour Model for Vascular Segmentation

    Directory of Open Access Journals (Sweden)

    Yun Tian

    2014-01-01

    Full Text Available This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.

  19. Optimization of multiresolution segmentation by using a genetic algorithm

    Science.gov (United States)

    Nikfar, Maryam; Zoej, Mohammad Javad Valadan; Mohammadzadeh, Ali; Mokhtarzade, Mehdi; Navabi, Afshin

    2012-01-01

    Most traditional pixel-based analyses are based on the digital number of each pixel. Whereas images can provide more details such as color, size, shape, and texture, object-oriented processing is more advantageous. Multiresolution segmentation, which was proposed by Baatz and Schäpe, is one of the most powerful segmentation algorithms. On the other hand, meaningful segmentation is the most important issue in object-oriented processing. Currently, meaningful segmentation, which is recommended by Baatz's multiresolution segmentation approach, is a trial-and-error task that is very tedious and time consuming. Therefore, a genetic algorithm (GA) is used for finding optimal parameters of Baatz's multiresolution segmentation approach for three building groups' meaningful segmentation. The optimal parameters are found by GA and its generality has been evaluated on a simulated image as well as some IKONOS and GeoEye image patches. The evaluations show the efficiency of GA for finding optimal multiresolution segmentation parameters for meaningful segmentation of the simulated image and the three groups of building images.

  20. AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

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    V.G. Biju

    2015-11-01

    Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

  1. Magnetic resonance imaging of water ascent in embolized xylem vessels of grapevine stem segments

    Science.gov (United States)

    Mingtao Wang; Melvin T. Tyree; Roderick E. Wasylishen

    2013-01-01

    Temporal and spatial information about water refilling of embolized xylem vessels and the rate of water ascent in these vessels is critical for understanding embolism repair in intact living vascular plants. High-resolution 1H magnetic resonance imaging (MRI) experiments have been performed on embolized grapevine stem segments while they were...

  2. FCM Clustering Algorithms for Segmentation of Brain MR Images

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    Yogita K. Dubey

    2016-01-01

    Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

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

    OpenAIRE

    Vishwambhar Pathak; Praveen Dhyani; Prabhat Mahanti

    2013-01-01

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

  4. Based on the CSI regional segmentation indoor localization algorithm

    Science.gov (United States)

    Zeng, Xi; Lin, Wei; Lan, Jingwei

    2017-08-01

    To solve the problem of high cost and low accuracy, the method of Channel State Information (CSI) regional segmentation are proposed in the indoor positioning. Because Channel State Information (CSI) stability, and effective against multipath effect, we used the Channel State Information (CSI) to segment location area. The method Acquisition CSI the influence of different link to pinpoint the location of the area. Then the method can improve the accuracy of positioning, and reduce the cost of the fingerprint localization algorithm.

  5. Objectness Supervised Merging Algorithm for Color Image Segmentation

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    Haifeng Sima

    2016-01-01

    Full Text Available Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

  6. A segmentation algorithm of intracranial hemorrhage CT image

    Science.gov (United States)

    Wang, Haibo; Chen, Zhiguo; Wang, Jianzhi

    2011-10-01

    To develop a computer aided detection (CAD) system that improves diagnostic accuracy of intracranial hemorrhage on cerebral CT. A method for CT image segmentation of brain is proposed, with which, several regions that are suspicious of hemorrhage can be segmented rapidly and effectively. Extracting intracranial area algorithm is introduced firstly to extract intracranial area. Secondly, FCM is employed twice, we named it with TFCM. FCM is first employed to identify areas of intracranial hemorrhage. Finally, FCM is employed to segment the lesions. Experimental results on real medical images demonstrate the efficiency and effectiveness.

  7. Automatic blood vessels segmentation based on different retinal maps from OCTA scans.

    Science.gov (United States)

    Eladawi, Nabila; Elmogy, Mohammed; Helmy, Omar; Aboelfetouh, Ahmed; Riad, Alaa; Sandhu, Harpal; Schaal, Shlomit; El-Baz, Ayman

    2017-10-01

    The retinal vascular network reflects the health of the retina, which is a useful diagnostic indicator of systemic vascular. Therefore, the segmentation of retinal blood vessels is a powerful method for diagnosing vascular diseases. This paper presents an automatic segmentation system for retinal blood vessels from Optical Coherence Tomography Angiography (OCTA) images. The system segments blood vessels from the superficial and deep retinal maps for normal and diabetic cases. Initially, we reduced the noise and improved the contrast of the OCTA images by using the Generalized Gauss-Markov random field (GGMRF) model. Secondly, we proposed a joint Markov-Gibbs random field (MGRF) model to segment the retinal blood vessels from other background tissues. It integrates both appearance and spatial models in addition to the prior probability model of OCTA images. The higher order MGRF (HO-MGRF) model in addition to the 1 st -order intensity model are used to consider the spatial information in order to overcome the low contrast between vessels and other tissues. Finally, we refined the segmentation by extracting connected regions using a 2D connectivity filter. The proposed segmentation system was trained and tested on 47 data sets, which are 23 normal data sets and 24 data sets for diabetic patients. To evaluate the accuracy and robustness of the proposed segmentation framework, we used three different metrics, which are Dice similarity coefficient (DSC), absolute vessels volume difference (VVD), and area under the curve (AUC). The results on OCTA data sets (DSC=95.04±3.75%, VVD=8.51±1.49%, and AUC=95.20±1.52%) show the promise of the proposed segmentation approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. White matter lesion segmentation using robust parameter estimation algorithms

    Science.gov (United States)

    Yang, Faguo; Zhu, Litao; Jiang, Tianzi

    2003-05-01

    White matter lesions are common brain abnormalities. In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from brain MRI images. The intensities of each tissue is assumed to be Gaussian distributed, whose parameters (mean vector and covariance matrix) are estimated using a tissue distribution model. And then a measure is defined to indicate in how much content a voxel belongs to the lesions. Experimental results demonstrate that our algorithm works well.

  9. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

    Science.gov (United States)

    Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

    2017-07-01

    Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. a Review of Point Clouds Segmentation and Classification Algorithms

    Science.gov (United States)

    Grilli, E.; Menna, F.; Remondino, F.

    2017-02-01

    Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

  11. Video Segmentation Using Fast Marching and Region Growing Algorithms

    Directory of Open Access Journals (Sweden)

    Eftychis Sifakis

    2002-04-01

    Full Text Available The algorithm presented in this paper is comprised of three main stages: (1 classification of the image sequence and, in the case of a moving camera, parametric motion estimation, (2 change detection having as reference a fixed frame, an appropriately selected frame or a displaced frame, and (3 object localization using local colour features. The image sequence classification is based on statistical tests on the frame difference. The change detection module uses a two-label fast marching algorithm. Finally, the object localization uses a region growing algorithm based on the colour similarity. Video object segmentation results are shown using the COST 211 data set.

  12. Evaluation of a segmentation algorithm designed for an FPGA implementation

    Science.gov (United States)

    Schwenk, Kurt; Schönermark, Maria; Huber, Felix

    2013-10-01

    The present work has to be seen in the context of real-time on-board image evaluation of optical satellite data. With on board image evaluation more useful data can be acquired, the time to get requested information can be decreased and new real-time applications are possible. Because of the relative high processing power in comparison to the low power consumption, Field Programmable Gate Array (FPGA) technology has been chosen as an adequate hardware platform for image processing tasks. One fundamental part for image evaluation is image segmentation. It is a basic tool to extract spatial image information which is very important for many applications such as object detection. Therefore a special segmentation algorithm using the advantages of FPGA technology has been developed. The aim of this work is the evaluation of this algorithm. Segmentation evaluation is a difficult task. The most common way for evaluating the performance of a segmentation method is still subjective evaluation, in which human experts determine the quality of a segmentation. This way is not in compliance with our needs. The evaluation process has to provide a reasonable quality assessment, should be objective, easy to interpret and simple to execute. To reach these requirements a so called Segmentation Accuracy Equality norm (SA EQ) was created, which compares the difference of two segmentation results. It can be shown that this norm is capable as a first quality measurement. Due to its objectivity and simplicity the algorithm has been tested on a specially chosen synthetic test model. In this work the most important results of the quality assessment will be presented.

  13. Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation.

    Science.gov (United States)

    Wang, Lei; Zhang, Huimao; He, Kan; Chang, Yan; Yang, Xiaodong

    2015-01-01

    Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and 'vesselness values' from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

  14. Robust vessel detection and segmentation in ultrasound images by a data-driven approach

    Science.gov (United States)

    Guo, Ping; Wang, Qiang; Wang, Xiaotao; Hao, Zhihui; Xu, Kuanhong; Ren, Haibing; Kim, Jung Bae; Hwang, Youngkyoo

    2014-03-01

    This paper presents a learning based vessel detection and segmentation method in real-patient ultrasound (US) liver images. We aim at detecting multiple shaped vessels robustly and automatically, including vessels with weak and ambiguous boundaries. Firstly, vessel candidate regions are detected by a data-driven approach. Multi-channel vessel enhancement maps with complement performances are generated and aggregated under a Conditional Random Field (CRF) framework. Vessel candidates are obtained by thresholding the saliency map. Secondly, regional features are extracted and the probability of each region being a vessel is modeled by random forest regression. Finally, a fast levelset method is developed to refine vessel boundaries. Experiments have been carried out on an US liver dataset with 98 patients. The dataset contains both normal and abnormal liver images. The proposed method in this paper is compared with a traditional Hessian based method, and the average precision is promoted by 56 percents and 7.8 percents for vessel detection and classification, respectively. This improvement shows that our method is more robust to noise, therefore has a better performance than the Hessian based method for the detection of vessels with weak and ambiguous boundaries.

  15. Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

    Science.gov (United States)

    Mann, Kulwinder S.; Kaur, Sukhpreet

    2017-06-01

    There are various eye diseases in the patients suffering from the diabetes which includes Diabetic Retinopathy, Glaucoma, Hypertension etc. These all are the most common sight threatening eye diseases due to the changes in the blood vessel structure. The proposed method using supervised methods concluded that the segmentation of the retinal blood vessels can be performed accurately using neural networks training. It uses features which include Gray level features; Moment Invariant based features, Gabor filtering, Intensity feature, Vesselness feature for feature vector computation. Then the feature vector is calculated using only the prominent features.

  16. Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

    Science.gov (United States)

    Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

    2017-06-12

    Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

  17. Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

    Directory of Open Access Journals (Sweden)

    Jiayin Liu

    2017-06-01

    Full Text Available Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC, which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF, which is estimated by Kernel Density Estimation (KDE with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

  18. AN EFFICIENT TECHNIQUE FOR RETINAL VESSEL SEGMENTATION AND DENOISING USING MODIFIED ISODATA AND CLAHE

    Directory of Open Access Journals (Sweden)

    Khan Bahadar Khan

    2016-11-01

    Full Text Available Retinal damage caused due to complications of diabetes is known as Diabetic Retinopathy (DR. In this case, the vision is obscured due to the damage of retinal tinny blood vessels of the retina. These tinny blood vessels may cause leakage which affect the vision and can lead to complete blindness. Identification of these new retinal vessels and their structure is essential for analysis of DR. Automatic blood vessels segmentation plays a significant role to assist subsequent automatic methodologies that aid to such analysis. In literature most of the people have used computationally hungry a strong preprocessing steps followed by a simple thresholding and post processing, But in our proposed technique we utilize an arrangement of  light pre-processing which consists of Contrast Limited Adaptive Histogram Equalization (CLAHE for contrast enhancement, a difference image of green channel from its Gaussian blur filtered image to remove local noise or geometrical object, Modified Iterative Self Organizing Data Analysis Technique (MISODATA for segmentation of vessel and non-vessel pixels based on global and local thresholding, and a strong  post processing using region properties (area, eccentricity to eliminate the unwanted region/segment, non-vessel pixels and noise that never been used to reject misclassified foreground pixels. The strategy is tested on the publically accessible DRIVE (Digital Retinal Images for Vessel Extraction and STARE (STructured Analysis of the REtina databases. The performance of proposed technique is assessed comprehensively and the acquired accuracy, robustness, low complexity and high efficiency and very less computational time that make the method an efficient tool for automatic retinal image analysis. Proposed technique perform well as compared to the existing strategies on the online available databases in term of accuracy, sensitivity, specificity, false positive rate, true positive rate and area under receiver

  19. Modified cuckoo search algorithm in microscopic image segmentation of hippocampus.

    Science.gov (United States)

    Chakraborty, Shouvik; Chatterjee, Sankhadeep; Dey, Nilanjan; Ashour, Amira S; Ashour, Ahmed S; Shi, Fuqian; Mali, Kalyani

    2017-10-01

    Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR. © 2017 Wiley Periodicals, Inc.

  20. Automatic detection and segmentation of vascular structures in dermoscopy images using a novel vesselness measure based on pixel redness and tubularness

    Science.gov (United States)

    Kharazmi, Pegah; Lui, Harvey; Stoecker, William V.; Lee, Tim

    2015-03-01

    Vascular structures are one of the most important features in the diagnosis and assessment of skin disorders. The presence and clinical appearance of vascular structures in skin lesions is a discriminating factor among different skin diseases. In this paper, we address the problem of segmentation of vascular patterns in dermoscopy images. Our proposed method is composed of three parts. First, based on biological properties of human skin, we decompose the skin to melanin and hemoglobin component using independent component analysis of skin color images. The relative quantities and pure color densities of each component were then estimated. Subsequently, we obtain three reference vectors of the mean RGB values for normal skin, pigmented skin and blood vessels from the hemoglobin component by averaging over 100000 pixels of each group outlined by an expert. Based on the Euclidean distance thresholding, we generate a mask image that extracts the red regions of the skin. Finally, Frangi measure was applied to the extracted red areas to segment the tubular structures. Finally, Otsu's thresholding was applied to segment the vascular structures and get a binary vessel mask image. The algorithm was implemented on a set of 50 dermoscopy images. In order to evaluate the performance of our method, we have artificially extended some of the existing vessels in our dermoscopy data set and evaluated the performance of the algorithm to segment the newly added vessel pixels. A sensitivity of 95% and specificity of 87% were achieved.

  1. Accurate colon residue detection algorithm with partial volume segmentation

    Science.gov (United States)

    Li, Xiang; Liang, Zhengrong; Zhang, PengPeng; Kutcher, Gerald J.

    2004-05-01

    Colon cancer is the second leading cause of cancer-related death in the United States. Earlier detection and removal of polyps can dramatically reduce the chance of developing malignant tumor. Due to some limitations of optical colonoscopy used in clinic, many researchers have developed virtual colonoscopy as an alternative technique, in which accurate colon segmentation is crucial. However, partial volume effect and existence of residue make it very challenging. The electronic colon cleaning technique proposed by Chen et al is a very attractive method, which is also kind of hard segmentation method. As mentioned in their paper, some artifacts were produced, which might affect the accurate colon reconstruction. In our paper, instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing a maximum a posterior probability (MAP) image-segmentation framework. A Markov random field (MRF) model was developed to reflect the spatial information for the tissue mixtures. The spatial information based on hard segmentation was used to determine which tissue types are in the specific voxel. Parameters of each tissue class were estimated by the expectation-maximization (EM) algorithm during the MAP tissue-mixture segmentation. Real CT experimental results demonstrated that the partial volume effects between four tissue types have been precisely detected. Meanwhile, the residue has been electronically removed and very smooth and clean interface along the colon wall has been obtained.

  2. Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting

    NARCIS (Netherlands)

    Gao, Shan; van't Klooster, Ronald; Kitslaar, Pieter H.; Coolen, Bram F.; van den Berg, Alexandra M.; Smits, Loek P.; Shahzad, Rahil; Shamonin, Denis P.; de Koning, Patrick J. H.; Nederveen, Aart J.; van der Geest, Rob J.

    2017-01-01

    Purpose: The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI.

  3. Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

    Directory of Open Access Journals (Sweden)

    Mandlenkosi Victor Gwetu

    2014-12-01

    Full Text Available Although computerized retinal image blood vessel segmentation has been extensively researched, there is still room for improvement in the quality of the segmented images. Since retinal image analysis is still widely used in the diagnosis of diabetic retinopathy, efficient and accurate image characterization techniques are required. Previous work has mainly focused on improving segmentation accuracy rates with little regard to the false positives that are produced by illumination variation. This research work presents a hybrid approach towards the segmentation of retinal blood vessels. New approaches towards the reduction of background illumination variation are proposed using normalized Gabor filtering. These are the base-offset encoding and a modified version of an existing zero-integral kernel technique. The valley emphasis automatic thresholding scheme is used to segment the Gabor response images. Experiments are conducted on the DRIVE and STARE retinal image data sets. Accuracy rates of up to 94% are achieved through the zero-integral and base offset methods. This is comparable with results from literature, where the same data sets are segmented using other classification techniques. The median-offset method is found to most effectively reduce background illumination variation.

  4. Multimodal MEMPRAGE, FLAIR, and R2* Segmentation to Resolve Dura and Vessels from Cortical Gray Matter

    Directory of Open Access Journals (Sweden)

    Roberto Viviani

    2017-05-01

    Full Text Available While widely in use in automated segmentation approaches for the detection of group differences or of changes associated with continuous predictors in gray matter volume, T1-weighted images are known to represent dura and cortical vessels with signal intensities similar to those of gray matter. By considering multiple signal sources at once, multimodal segmentation approaches may be able to resolve these different tissue classes and address this potential confound. We explored here the simultaneous use of FLAIR and apparent transverse relaxation rates (a signal related to T2* relaxation maps and having similar contrast with T1-weighted images. Relative to T1-weighted images alone, multimodal segmentation had marked positive effects on 1. the separation of gray matter from dura, 2. the exclusion of vessels from the gray matter compartment, and 3. the contrast with extracerebral connective tissue. While obtainable together with the T1-weighted images without increasing scanning times, apparent transverse relaxation rates were less effective than added FLAIR images in providing the above mentioned advantages. FLAIR images also improved the detection of cortical matter in areas prone to susceptibility artifacts in standard MPRAGE T1-weighted images, while the addition of transverse relaxation maps exacerbated the effect of these artifacts on segmentation. Our results confirm that standard MPRAGE segmentation may overestimate gray matter volume by wrongly assigning vessels and dura to this compartment and show that multimodal approaches may greatly improve the specificity of cortical segmentation. Since multimodal segmentation is easily implemented, these benefits are immediately available to studies focusing on translational applications of structural imaging.

  5. Retinal vessel segmentation in colour fundus images using Extreme Learning Machine.

    Science.gov (United States)

    Zhu, Chengzhang; Zou, Beiji; Zhao, Rongchang; Cui, Jinkai; Duan, Xuanchu; Chen, Zailiang; Liang, Yixiong

    2017-01-01

    Attributes of the retinal vessel play important role in systemic conditions and ophthalmic diagnosis. In this paper, a supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel. Firstly, a set of 39-D discriminative feature vectors, consisting of local features, morphological features, phase congruency, Hessian and divergence of vector fields, is extracted for each pixel of the fundus image. Then a matrix is constructed for pixel of the training set based on the feature vector and the manual labels, and acts as the input of the ELM classifier. The output of classifier is the binary retinal vascular segmentation. Finally, an optimization processing is implemented to remove the region less than 30 pixels which is isolated from the retinal vascilar. The experimental results testing on the public Digital Retinal Images for Vessel Extraction (DRIVE) database demonstrate that the proposed method is much faster than the other methods in segmenting the retinal vessels. Meanwhile the average accuracy, sensitivity, and specificity are 0.9607, 0.7140 and 0.9868, respectively. Moreover the proposed method exhibits high speed and robustness on a new Retinal Images for Screening (RIS) database. Therefore it has potential applications for real-time computer-aided diagnosis and disease screening. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2015-01-01

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

  7. Device for Investigation of Mechanical Tension of Isolated Smooth Muscle Vessels and Airway Segments of Animals

    Science.gov (United States)

    Aleinik, A.; Karpovich, N.; Turgunova, N.; Nosarev, A.

    2016-11-01

    For the purpose of testing and the search for new drug compounds, designed to heal many human diseases, it is necessary to investigate the deformation of experimental tissue samples under influence of these drugs. For this task a precision force sensor for measuring the mechanical tension, produced by isolated ring segments of blood vessels and airways was created. The hardware and software systems for the study of changes in contractile responses of the airway smooth muscles and blood vessels of experimental animals was developed.

  8. Automated image segmentation and registration of vessel wall MRI for quantitative assessment of carotid artery vessel wall dimensions and plaque composition

    NARCIS (Netherlands)

    Klooster, Ronald van 't

    2014-01-01

    The main goal of this thesis was to develop methods for automated segmentation, registration and classification of the carotid artery vessel wall and plaque components using multi-sequence MR vessel wall images to assess atherosclerosis. First, a general introduction into atherosclerosis and

  9. Crowdsourcing the creation of image segmentation algorithms for connectomics

    Directory of Open Access Journals (Sweden)

    Ignacio eArganda-Carreras

    2015-11-01

    Full Text Available To stimulate progress in automating the reconstruction of neural circuits,we organized the first international challenge on 2D segmentationof electron microscopic (EM images of the brain. Participants submittedboundary maps predicted for a test set of images, and were scoredbased on their agreement with ground truth from human experts. Thewinning team had no prior experience with EM images, and employeda convolutional network. This ``deep learning'' approach has sincebecome accepted as a standard for segmentation of EM images. The challengehas continued to accept submissions, and the best so far has resultedfrom cooperation between two teams. The challenge has probably saturated,as algorithms cannot progress beyond limits set by ambiguities inherentin 2D scoring. Retrospective evaluation of the challenge scoring systemreveals that it was not sufficiently robust to variations in the widthsof neurite borders. We propose a solution to this problem, which shouldbe useful for a future 3D segmentation challenge.

  10. Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis.

    Science.gov (United States)

    Lin, Muqing; Chen, Jeon-Hor; Nie, Ke; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

    2009-10-01

    To develop a computer-based algorithm for detecting blood vessels that appear in breast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), and to evaluate the improvement in reducing the number of vascular pixels that are labeled by computer-aided diagnosis (CAD) systems as being suspicious of malignancy. The analysis was performed in 34 cases. The algorithm applied a filter bank based on wavelet transform and the Hessian matrix to detect linear structures as blood vessels on a two-dimensional maximum intensity projection (MIP). The vessels running perpendicular to the MIP plane were then detected based on the connectivity of enhanced pixels above a threshold. The nonvessel enhancements were determined and excluded based on their morphological properties, including those showing scattered small segment enhancements or nodular or planar clusters. The detected vessels were first converted to a vasculature skeleton by thinning and subsequently compared to the vascular track manually drawn by a radiologist. When evaluating the performance of the algorithm in identifying vascular tissue, the correct-detection rate refers to pixels identified by both the algorithm and radiologist, while the incorrect-detection rate refers to pixels identified by only the algorithm, and the missed-detection rate refers to pixels identified only by the radiologist. From 34 analyzed cases the median correct-detection rate was 85.6% (mean 84.9% +/- 7.8%), the incorrect-detection rate was 13.1% (mean 15.1% +/- 7.8%), and the missed-detection rate was 19.2% (mean 21.3% +/- 12.8%). When detected vessels were excluded in the hot-spot color-coding of the CAD system, they could reduce the labeling of vascular vessels in 2.6%-68.6% of hot-spot pixels (mean 16.6% +/- 15.9%). The computer algorithm-based method can detect most large vessels and provide an effective means in reducing the labeling of vascular pixels as suspicious on a DCE-MRI CAD system. This algorithm may improve the

  11. The Galileo Ground Segment Integrity Algorithms: Design and Performance

    Directory of Open Access Journals (Sweden)

    Carlos Hernández Medel

    2008-01-01

    Full Text Available Galileo, the European Global Navigation Satellite System, will provide to its users highly accurate global positioning services and their associated integrity information. The element in charge of the computation of integrity messages within the Galileo Ground Mission Segment is the integrity processing facility (IPF, which is developed by GMV Aerospace and Defence. The main objective of this paper is twofold: to present the integrity algorithms implemented in the IPF and to show the achieved performance with the IPF software prototype, including aspects such as: implementation of the Galileo overbounding concept, impact of safety requirements on the algorithm design including the threat models for the so-called feared events, and finally the achieved performance with real GPS and simulated Galileo scenarios.

  12. Hybrid Segmentation of Vessels and Automated Flow Measures in In-Vivo Ultrasound Imaging

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Martins, Bo; Hansen, Kristoffer Lindskov

    2016-01-01

    method implements automated VFI flow measures such as peak systolic velocity (PSV) and volume flow. An evaluation of the performance of the segmentation algorithm relative to expert manual segmentation of 60 frames randomly chosen from 6 ultrasound sequences (10 frame randomly chosen from each sequence...... expert segmentations. The flowrig results also demonstrated that the PSVs measured from VFI had a mean relative error of 14.5% in comparison with the actual PSVs. The error for the PSVs measured from spectral Doppler was 29.5%, indicating that VFI is 15% more precise than spectral Doppler in PSV...

  13. A SURVEY OF RETINA BASED DISEASE IDENTIFICATION USING BLOOD VESSEL SEGMENTATION

    Directory of Open Access Journals (Sweden)

    P Kuppusamy

    2016-11-01

    Full Text Available The colour retinal photography is one of the most essential features to identify the confirmation of various eye diseases. The iris is primary attribute to authenticate the human. This research work presents the survey and comparison of various blood vessel related feature identification, segmentation, extraction and enhancement methods. Additionally, this study is observed the various databases performance for storing the images and testing in minimal time. This paper is also provides the better performance techniques based on the survey.

  14. Robust Coordinated Control Algorithm for Multiple Marine Vessels with External Disturbances

    Directory of Open Access Journals (Sweden)

    Weixue Liu

    2013-01-01

    Full Text Available The problem of coordinated control for multiple marine vessels in the presence of external disturbances is considered in this paper. A robust coordinated control algorithm is proposed for multiple marine vessels. The proposed robust coordinated control algorithm is divided into two parts. The first part develops an extended state observer to estimate the disturbances of marine vessels. The second part presents a robust coordinated control algorithm based on the output of the extended state observer. Furthermore, the robust coordinated control algorithm is designed using the dynamic surface control method. In light of the leader-follower strategy, the trajectory for each vessel is defined according to the desired trajectory of the assigned leader and the relative distance with respect to the leader. The effectiveness of the proposed coordination algorithm is demonstrated by the simulation results.

  15. Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method

    Directory of Open Access Journals (Sweden)

    Guannan Chen

    2017-01-01

    Full Text Available As a nonintrusive method, the retina imaging provides us with a better way for the diagnosis of ophthalmologic diseases. Extracting the vessel profile automatically from the retina image is an important step in analyzing retina images. A novel hybrid active contour model is proposed to segment the fundus image automatically in this paper. It combines the signed pressure force function introduced by the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS model with the local intensity property introduced by the Local Binary fitting (LBF model to overcome the difficulty of the low contrast in segmentation process. It is more robust to the initial condition than the traditional methods and is easily implemented compared to the supervised vessel extraction methods. Proposed segmentation method was evaluated on two public datasets, DRIVE (Digital Retinal Images for Vessel Extraction and STARE (Structured Analysis of the Retina (the average accuracy of 0.9390 with 0.7358 sensitivity and 0.9680 specificity on DRIVE datasets and average accuracy of 0.9409 with 0.7449 sensitivity and 0.9690 specificity on STARE datasets. The experimental results show that our method is effective and our method is also robust to some kinds of pathology images compared with the traditional level set methods.

  16. VESSEL CENTERLINE TRACKING AND BOUNDARY SEGMENTATION IN CORONARY MRA WITH MINIMAL MANUAL INTERACTION.

    Science.gov (United States)

    Soleimanifard, Sahar; Schär, Michael; Hays, Allison G; Weiss, Robert G; Stuber, Matthias; Prince, Jerry L

    2012-01-01

    Magnetic resonance angiography (MRA) provides a noninvasive means to detect the presence, location and severity of atherosclerosis throughout the vascular system. In such studies, and especially those in the coronary arteries, the vessel luminal area is typically measured at multiple cross-sectional locations along the course of the artery. The advent of fast volumetric imaging techniques covering proximal to mid segments of coronary arteries necessitates automatic analysis tools requiring minimal manual interactions to robustly measure cross-sectional area along the three-dimensional track of the arteries in under-sampled and non-isotropic datasets. In this work, we present a modular approach based on level set methods to track the vessel centerline, segment the vessel boundaries, and measure transversal area using two user-selected endpoints in each coronary of interest. Arterial area and vessel length are measured using our method and compared to the standard Soap-Bubble reformatting and analysis tool in in-vivo non-contrast enhanced coronary MRA images.

  17. The implement of Talmud property allocation algorithm based on graphic point-segment way

    Science.gov (United States)

    Cen, Haifeng

    2017-04-01

    Under the guidance of the Talmud allocation scheme's theory, the paper analyzes the algorithm implemented process via the perspective of graphic point-segment way, and designs the point-segment way's Talmud property allocation algorithm. Then it uses Java language to implement the core of allocation algorithm, by using Android programming to build a visual interface.

  18. A scale space based algorithm for automated segmentation of single shot tagged MRI of shearing deformation

    NARCIS (Netherlands)

    Sprengers, Andre M. J.; Caan, Matthan W. A.; Moerman, Kevin M.; Nederveen, Aart J.; Lamerichs, Rolf M.; Stoker, Jaap

    2013-01-01

    This study proposes a scale space based algorithm for automated segmentation of single-shot tagged images of modest SNR. Furthermore the algorithm was designed for analysis of discontinuous or shearing types of motion, i.e. segmentation of broken tag patterns. The proposed algorithm utilises

  19. Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

    Science.gov (United States)

    Scorza, Davide; Moccia, Sara; De Luca, Giuseppe; Plaino, Lisa; Cardinale, Francesco; Mattos, Leonardo S.; Kabongo, Luis; De Momi, Elena

    2017-03-01

    Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

  20. Optimal Viewing Angle Determination for Multiple Vessel Segments in Coronary Angiographic Image

    Science.gov (United States)

    Wang, Xuehu; Yang, Jian; Chen, Yang; Ai, Danni; Hu, Yining; Wang, Yongtian

    2014-06-01

    Angiographic image is the perspective projection of the whole body from a 3D space to a 2D imaging plane, in which X-ray is used. As such, topological vasculature information has been lost. In 2D angiograms, foreshortening and overlapping are commonly observed in tubular-like structures. Hence, an optimum viewing angle should be determined to observe an interesting vessel segment (IVS) or an interesting vessel bifurcation (IVB) with minimized foreshortening and overlapping from a limited number of angiographic images. In this study, a novel integrated optimization method is proposed to calculate the optimum viewing angle. In the proposed method, the irregular shape and inter-branch distance of vasculatures are considered. Furthermore, three optimized conditions, including projection foreshortening rate, projection stenosis rate, and projection overlapping rate, are designed and integrated to determine the optimum viewing angle in a single vessel segment. The three conditions, including projection foreshortening, projection stenosis, and projection adjacent spacing rates, are also designed to optimize the viewing angle of bifurcations. To evaluate the performance of the proposed method, we simulated an angiographic image based on X-ray propagating principle by integrating 3D coronary artery tree models and the respective CT volume data. Experimental results demonstrate that the proposed method is very effective and robust; hence, this method can be used to determine the optimum viewing angle of IVS or IVB with irregular stenosis. The proposed method can also help physicians observe the branching structure or stenosis clearly in clinical practice.

  1. Models and Algorithms for Container Vessel Stowage Optimization

    DEFF Research Database (Denmark)

    Delgado-Ortegon, Alberto

    planning that includes features of stowage planning that have not been considered in previous work. For slot planning, a fast and accurate representative model to optimally stow vessel sections is introduced. The second SP approach serves as the optimization component of a commercial decision support tool...... used for interactive planning of container vessels. Expert's know-how formulated as user preferences is integrated into the heuristic optimization component and used to tackle complex constraints and optimize combinatorial objectives. According to our experimental evaluation, stowage plans computed...... by our heuristic are competitive enough with respect to those made by experts under the same conditions. The CCP evaluates how the stowage characteristics of containers with different features affect important performance measures used in liner shipping companies, e.g., vessel intake and cargo revenue...

  2. A Wavelet Relational Fuzzy C-Means Algorithm for 2D Gel Image Segmentation

    Directory of Open Access Journals (Sweden)

    Shaheera Rashwan

    2013-01-01

    Full Text Available One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM and the Wavelet Fuzzy C-Means (WFCM to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases the quality of the segmentation.

  3. Linear segmentation algorithm for detecting layer boundary with lidar.

    Science.gov (United States)

    Mao, Feiyue; Gong, Wei; Logan, Timothy

    2013-11-04

    The automatic detection of aerosol- and cloud-layer boundary (base and top) is important in atmospheric lidar data processing, because the boundary information is not only useful for environment and climate studies, but can also be used as input for further data processing. Previous methods have demonstrated limitations in defining the base and top, window-size setting, and have neglected the in-layer attenuation. To overcome these limitations, we present a new layer detection scheme for up-looking lidars based on linear segmentation with a reasonable threshold setting, boundary selecting, and false positive removing strategies. Preliminary results from both real and simulated data show that this algorithm cannot only detect the layer-base as accurate as the simple multi-scale method, but can also detect the layer-top more accurately than that of the simple multi-scale method. Our algorithm can be directly applied to uncalibrated data without requiring any additional measurements or window size selections.

  4. A segmentation algorithm based on image projection for complex text layout

    Science.gov (United States)

    Zhu, Wangsheng; Chen, Qin; Wei, Chuanyi; Li, Ziyang

    2017-10-01

    Segmentation algorithm is an important part of layout analysis, considering the efficiency advantage of the top-down approach and the particularity of the object, a breakdown of projection layout segmentation algorithm. Firstly, the algorithm will algorithm first partitions the text image, and divided into several columns, then for each column scanning projection, the text image is divided into several sub regions through multiple projection. The experimental results show that, this method inherits the projection itself and rapid calculation speed, but also can avoid the effect of arc image information page segmentation, and also can accurate segmentation of the text image layout is complex.

  5. Evolutionary algorithm for automatic detection of blood vessel shapes

    Science.gov (United States)

    Kutics, Andrea

    1996-04-01

    Automatic detection of blood vessel shapes locating in the skin has a great diagnostic importance. In this work, an evolutionary approach operating on morphological operator and operation structures is proposed for the determination of the shape and network of blood vessels located in upper skin layers. A population of individuals comprising morphological structures is generated. A two-dimensional queue like data representation of individuals is applied in order to provide an appropriate representation of the connectivity constraints originated in the two dimensional nature of the structuring elements. Two-dimensional crossover and mutation type manipulation operations are carried out on selected elements of the population. Unlike the usual techniques, in our approach no constraints are used for background and smoothness as no matched filter or linear operator is applied. Also no a priori knowledge of the vessel shape is necessary due to the evolutionary method. Unlike the usual imaging techniques, that mainly use angiograms as input, in this work infrared filtered images taken by CCD camera are applied to investigate the blood vessels of broad skin areas. The method is implemented parallel on a lattice network of transputers resulting in a significantly decreased processing time compared to the usual techniques.

  6. Robust iris segmentation through parameterization of the Chan-Vese algorithm

    CSIR Research Space (South Africa)

    Mabuza-Hocquet, G

    2015-06-01

    Full Text Available -Vese algorithm with pre-defined initial contour and curve evolution parameters for accurate segmentation. Preprocessing techniques to enhance and detect iris features for extraction. Labeling features of strongest pixel connectivity and using Harris algorithm...

  7. Segmentation of elastic fibres in images of vessel wall sections stained with Weigert's resorcin-fuchsin.

    Science.gov (United States)

    Hernández-Morera, Pablo; Travieso-González, Carlos M; Castaño-González, Irene; Mompeó-Corredera, Blanca; Ortega-Santana, Francisco

    2017-04-01

    The elastic fibres are an essential component of the extracellular matrix in blood vessel walls that allows a long-range of deformability and passive recoil without energy input. The quantitative determination of elastic fibres will provide information on the state of the vascular wall and to determine the role and behaviour of this key structural element in different physiological and pathological vascular processes. We present a segmentation method to identify and quantify elastic fibres based on a local threshold technique and some morphological characteristics measured on the segmented objects that facilitate the discrimination between elastic fibres and other image components. The morphological characteristics analysed are the thickness and the length of an object. The segmentation method was evaluated using an image database of vein sections stained with Weigert's resorcin-fuchsin. The performance results are based on a ground truth generated manually resulting in values of sensitivity greater than 80% with the exception in two samples, and specificity values above 90% for all samples. Medical specialists carried out a visual evaluation where the observations indicate a general agreement on the segmentation results' visual quality, and the consistency between the methodology proposed and the subjective observation of the doctors for the evaluation of pathological changes in vessel wall. The proposed methodology provides more objective measurements than the qualitative methods traditionally used in the histological analysis, with a significant potential for this method to be used as a diagnostic aid for many other vascular pathological conditions and in similar tissues such as skin and mucous membranes. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm.

    Science.gov (United States)

    Song, Hong; Kang, Wei; Zhang, Qian; Wang, Shuliang

    2015-01-01

    Organ segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures. In this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods. Our method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification.

  9. Application of multi-scale segmentation algorithms for high resolution remote sensing image

    Science.gov (United States)

    Zhou, Tingting; Gu, Lingjia; Ren, Ruizhi

    2017-09-01

    In recent decades, with the rapid development of remote sensing technology, high resolution remote sensing images have been widely used in various fields due to their characteristics, such as rich spectral information and complex texture information. As a key step in the feature extraction, multi-scale image segmentation algorithm has been a hotspot currently. The traditional image segmentation is based on pixels, which only takes the spectral information of pixel into account, and ignores the texture, spatial information and contextual relation of the objects in the image. The experimental high resolution remote sensing images are from GF-2 and the features of the experimental data are obvious, the edges are clear. By using the statistical region merging (SRM) algorithm, the fractal net evolution approach (FNEA) algorithm and the unsupervised multi-scale segmentation of color images (UMSC) algorithm, this paper analyzes the segmentation effects of three multi-scale segmentation algorithms on the optimal scale and on the same segmentation scale respectively. The experimental results under the optimal scale and the same segmentation scale show that the SRM algorithm outperforms the UMSC algorithm, and UMSC algorithm outperforms the FENA algorithm in multi-scale segmentation.

  10. A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

    Science.gov (United States)

    Liu, Likun

    2018-01-01

    In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

  11. Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm

    Directory of Open Access Journals (Sweden)

    Fernando Cervantes-Sanchez

    2016-01-01

    Full Text Available This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA in X-ray angiograms. Since the single-scale Gabor filters (SSG are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with Az=0.9502 over a training set of 40 images and Az=0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.

  12. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter.

    Science.gov (United States)

    Singh, Nagendra Pratap; Srivastava, Rajeev

    2016-06-01

    Retinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension, diabetes, glaucoma, etc. In this paper, a novel matched filter approach with the Gumbel probability distribution function as its kernel is introduced to improve the performance of retinal blood vessel segmentation. Before applying the proposed matched filter, the input retinal images are pre-processed. During pre-processing stage principal component analysis (PCA) based gray scale conversion followed by contrast limited adaptive histogram equalization (CLAHE) are applied for better enhancement of retinal image. After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new matched filter. The post-processing steps after applying the proposed matched filter include the entropy based optimal thresholding and length filtering to obtain the segmented image. For evaluating the performance of proposed approach, the quantitative performance measures, an average accuracy, average true positive rate (ATPR), and average false positive rate (AFPR) are calculated. The respective values of the quantitative performance measures are 0.9522, 0.7594, 0.0292 for DRIVE data set and 0.9270, 0.7939, 0.0624 for STARE data set. To justify the effectiveness of proposed approach, receiver operating characteristic (ROC) curve is plotted and the average area under the curve (AUC) is calculated. The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively. The obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian distribution function and Cauchy PDF based matched filter approaches. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. An exact algorithm for generating homogenous two-segment cutting patterns

    Science.gov (United States)

    Cui, Y.

    2007-04-01

    An exact algorithm is proposed for generating homogenous two-segment patterns for the constrained two-dimensional guillotine-cutting problems of rectangular items. It is a bottom-up approach combined with branch-and-bound techniques. The stock plate is divided into two segments. Each segment consists of strips of the same length and direction. Only homogenous strips are considered, each of which contains items of the same type. The strip directions of the two segments may be either the same or perpendicular to each other. The algorithm uses a tree-search procedure. It starts from an initial lower bound, implicitly generates all possible segments through the assembly of strips, and constructs possible patterns through the combination of two segments. Tighter bounds are established to discard non-promising segments. The computational results indicate that the algorithm is efficient both in computation time and in material utilization, and is able to deal with relatively large-scale problems.

  14. A Novel Face Segmentation Algorithm from a Video Sequence for Real-Time Face Recognition

    Directory of Open Access Journals (Sweden)

    Sudhaker Samuel RD

    2007-01-01

    Full Text Available The first step in an automatic face recognition system is to localize the face region in a cluttered background and carefully segment the face from each frame of a video sequence. In this paper, we propose a fast and efficient algorithm for segmenting a face suitable for recognition from a video sequence. The cluttered background is first subtracted from each frame, in the foreground regions, a coarse face region is found using skin colour. Then using a dynamic template matching approach the face is efficiently segmented. The proposed algorithm is fast and suitable for real-time video sequence. The algorithm is invariant to large scale and pose variation. The segmented face is then handed over to a recognition algorithm based on principal component analysis and linear discriminant analysis. The online face detection, segmentation, and recognition algorithms take an average of 0.06 second on a 3.2 GHz P4 machine.

  15. An Algorithm for Morphological Segmentation of Esperanto Words

    National Research Council Canada - National Science Library

    Guinard, Theresa

    2016-01-01

    ... segmentation is usually more semantically probable than the others. This paper presents a modified n-gram Markov model that finds the most probable segmentation of any Esperanto word, where the model’s states represent morpheme part-of-speech and semantic classes. The overall segmentation accuracy was over 98% for a set of presegmented dictionary w...

  16. Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation

    Directory of Open Access Journals (Sweden)

    R. V. V. Krishna

    2016-10-01

    Full Text Available This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD which is a modification of Weber Local Descriptor (WLD is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.

  17. An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding

    OpenAIRE

    Wang, Fujun; Li, Junlan; Liu, Shiwei; Zhao, Xingyu; Zhang, Dawei; Tian, Yanling

    2014-01-01

    In order to improve the precision and efficiency of microelectronic bonding, this paper presents an improved adaptive genetic algorithm (IAGA) for the image segmentation and vision alignment of the solder joints in the microelectronic chips. The maximum between-cluster variance (OTSU) threshold segmentation method was adopted for the image segmentation of microchips, and the IAGA was introduced to the threshold segmentation considering the features of the images. The performance of the image ...

  18. Edged watershed segmentation: a semi-interactive algorithm for segmentation of low-resolution maps from electron cryomicroscopy.

    Science.gov (United States)

    Baker, Lindsay A; Rubinstein, John L

    2011-10-01

    Electron cryomicroscopy (cryo-EM) allows for the structural analysis of large protein complexes that may be difficult to study by other means. Frequently, maps of complexes from cryo-EM are obtained at resolutions between 10 and 25Å. To aid in the interpretation of these medium- to low-resolution maps, they may be subdivided into three-dimensional segments representing subunits or subcomplexes. This division is often accomplished using a manual segmentation approach. While extremely useful, manual segmentation is subjective. We have developed a novel semi-interactive segmentation algorithm that can incorporate prior knowledge of subunit composition or structure without biasing the boundaries between subunits or subcomplexes. This algorithm has been characterized with experimental and simulated cryo-EM density maps at resolutions between 10 and 25Å. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. Improved dynamic-programming-based algorithms for segmentation of masses in mammograms.

    Science.gov (United States)

    Rojas Domínguez, Alfonso; Nandi, Asoke K

    2007-11-01

    In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID2PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID2PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions.

  20. Extended-Maxima Transform Watershed Segmentation Algorithm for Touching Corn Kernels

    Directory of Open Access Journals (Sweden)

    Yibo Qin

    2013-01-01

    Full Text Available Touching corn kernels are usually oversegmented by the traditional watershed algorithm. This paper proposes a modified watershed segmentation algorithm based on the extended-maxima transform. Firstly, a distance-transformed image is processed by the extended-maxima transform in the range of the optimized threshold value. Secondly, the binary image obtained by the preceding process is run through the watershed segmentation algorithm, and watershed ridge lines are superimposed on the original image, so that touching corn kernels are separated into segments. Fifty images which all contain 400 corn kernels were tested. Experimental results showed that the effect of segmentation is satisfactory by the improved algorithm, and the accuracy of segmentation is as high as 99.87%.

  1. Optimal design of high pressure hydrogen storage vessel using an adaptive genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Ping [Institute of Applied Mechanics, Zhejiang University, Hangzhou 310027 (China); Zheng, Jinyang; Chen, Honggang; Liu, Pengfei [Institute of Chemical Machinery and Process Equipment, Zhejiang University, Hangzhou 310027 (China)

    2010-04-15

    The weight minimum optimization of composite hydrogen storage vessel under the burst pressure constraint is considered. An adaptive genetic algorithm is proposed to perform the optimal design of composite vessels. The proposed optimization algorithm considers the adaptive probabilities of crossover and mutation which change with the fitness values of individuals and proposes a penalty function to deal with the burst pressure constraint. The winding thickness and angles of composite layers are chosen as the design variables. Effects of the population size and the number of generations on the optimal results are explored. The results using the adaptive genetic algorithm are also compared with those using the simple genetic algorithm and the Monte Carlo optimization method. (author)

  2. Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage.

    Science.gov (United States)

    Scherer, Moritz; Cordes, Jonas; Younsi, Alexander; Sahin, Yasemin-Aylin; Götz, Michael; Möhlenbruch, Markus; Stock, Christian; Bösel, Julian; Unterberg, Andreas; Maier-Hein, Klaus; Orakcioglu, Berk

    2016-11-01

    ABC/2 is still widely accepted for volume estimations in spontaneous intracerebral hemorrhage (ICH) despite known limitations, which potentially accounts for controversial outcome-study results. The aim of this study was to establish and validate an automatic segmentation algorithm, allowing for quick and accurate quantification of ICH. A segmentation algorithm implementing first- and second-order statistics, texture, and threshold features was trained on manual segmentations with a random-forest methodology. Quantitative data of the algorithm, manual segmentations, and ABC/2 were evaluated for agreement in a study sample (n=28) and validated in an independent sample not used for algorithm training (n=30). ABC/2 volumes were significantly larger compared with either manual or algorithm values, whereas no significant differences were found between the latter (P<0.0001; Friedman+Dunn's multiple comparison). Algorithm agreement with the manual reference was strong (concordance correlation coefficient 0.95 [lower 95% confidence interval 0.91]) and superior to ABC/2 (concordance correlation coefficient 0.77 [95% confidence interval 0.64]). Validation confirmed agreement in an independent sample (algorithm concordance correlation coefficient 0.99 [95% confidence interval 0.98], ABC/2 concordance correlation coefficient 0.82 [95% confidence interval 0.72]). The algorithm was closer to respective manual segmentations than ABC/2 in 52/58 cases (89.7%). An automatic segmentation algorithm for volumetric analysis of spontaneous ICH was developed and validated in this study. Algorithm measurements showed strong agreement with manual segmentations, whereas ABC/2 exhibited its limitations, yielding inaccurate overestimations of ICH volume. The refined, yet time-efficient, quantification of ICH by the algorithm may facilitate evaluation of clot volume as an outcome predictor and trigger for surgical interventions in the clinical setting. © 2016 American Heart Association, Inc.

  3. A scale space based algorithm for automated segmentation of single shot tagged MRI of shearing deformation.

    Science.gov (United States)

    Sprengers, Andre M J; Caan, Matthan W A; Moerman, Kevin M; Nederveen, Aart J; Lamerichs, Rolf M; Stoker, Jaap

    2013-04-01

    This study proposes a scale space based algorithm for automated segmentation of single-shot tagged images of modest SNR. Furthermore the algorithm was designed for analysis of discontinuous or shearing types of motion, i.e. segmentation of broken tag patterns. The proposed algorithm utilises non-linear scale space for automatic segmentation of single-shot tagged images. The algorithm's ability to automatically segment tagged shearing motion was evaluated in a numerical simulation and in vivo. A typical shearing deformation was simulated in a Shepp-Logan phantom allowing for quantitative evaluation of the algorithm's success rate as a function of both SNR and the amount of deformation. For a qualitative in vivo evaluation tagged images showing deformations in the calf muscles and eye movement in a healthy volunteer were acquired. Both the numerical simulation and the in vivo tagged data demonstrated the algorithm's ability for automated segmentation of single-shot tagged MR provided that SNR of the images is above 10 and the amount of deformation does not exceed the tag spacing. The latter constraint can be met by adjusting the tag delay or the tag spacing. The scale space based algorithm for automatic segmentation of single-shot tagged MR enables the application of tagged MR to complex (shearing) deformation and the processing of datasets with relatively low SNR.

  4. FCM Clustering Algorithms for Segmentation of Brain MR Images

    OpenAIRE

    Yogita K. Dubey; Mushrif, Milind M.

    2016-01-01

    The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentati...

  5. Segmentation of Mushroom and Cap width Measurement using Modified K-Means Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Eser Sert

    2014-01-01

    Full Text Available Mushroom is one of the commonly consumed foods. Image processing is one of the effective way for examination of visual features and detecting the size of a mushroom. We developed software for segmentation of a mushroom in a picture and also to measure the cap width of the mushroom. K-Means clustering method is used for the process. K-Means is one of the most successful clustering methods. In our study we customized the algorithm to get the best result and tested the algorithm. In the system, at first mushroom picture is filtered, histograms are balanced and after that segmentation is performed. Results provided that customized algorithm performed better segmentation than classical K-Means algorithm. Tests performed on the designed software showed that segmentation on complex background pictures is performed with high accuracy, and 20 mushrooms caps are measured with 2.281 % relative error.

  6. Study on Control Algorithm for Continuous Segments Trajectory Interpolation

    Institute of Scientific and Technical Information of China (English)

    SHI Chuan; YE Peiqing; LV Qiang

    2006-01-01

    In CNC machining, the complexity of the part contour causes a series of problems including the repeated start-stop of the motor, low machining efficiency, and poor machining quality. To relieve those problems, a new interpolation algorithm was put forward to realize the interpolation control of continuous sections trajectory. The relevant error analysis of the algorithm was also studied. The feasibility of the algorithm was proved by machining experiment using a laser machine to carve the interpolation trajectory in the CNC system GT100. This algorithm effectively improved the machining efficiency and the contour quality.

  7. PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting

    Directory of Open Access Journals (Sweden)

    Kaikuo Xu

    2013-01-01

    Full Text Available Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm, which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length and MML (minimum message length methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

  8. A semi-supervised segmentation algorithm as applied to k-means ...

    African Journals Online (AJOL)

    ... study the newly proposed semi-supervised segmentation algorithm outperforms both an unsupervised and a supervised segmentation technique, when compared by using the Gini coecient as performance measure of the resulting predictive models. Key words: Banking, clustering, multivariate statistics, data mining ...

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

    Science.gov (United States)

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

    2015-12-01

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

  10. PREPAID TELECOM CUSTOMERS SEGMENTATION USING THE K-MEAN ALGORITHM

    Directory of Open Access Journals (Sweden)

    Marar Liviu Ioan

    2012-07-01

    Full Text Available The scope of relationship marketing is to retain customers and win their loyalty. This can be achieved if the companies’ products and services are developed and sold considering customers’ demands. Fulfilling customers’ demands, taken as the starting point of relationship marketing, can be obtained by acknowledging that the customers’ needs and wishes are heterogeneous. The segmentation of the customers’ base allows operators to overcome this because it illustrates the whole heterogeneous market as the sum of smaller homogeneous markets. The concept of segmentation relies on the high probability of persons grouped into segments based on common demands and behaviours to have a similar response to marketing strategies. This article focuses on the segmentation of a telecom customer base according to specific and noticeable criteria of a certain service. Although the segmentation concept is widely approached in professional literature, articles on the segmentation of a telecom customer base are very scarce, due to the strategic nature of this information. Market segmentation is carried out based on how customers spent their money on credit recharging, on making calls, on sending SMS and on Internet navigation. The method used for customer segmentation is the K-mean cluster analysis. To assess the internal cohesion of the clusters we employed the average sum of squares error indicator, and to determine the differences among the clusters we used the ANOVA and the post-hoc Tukey tests. The analyses revealed seven customer segments with different features and behaviours. The results enable the telecom company to conceive marketing strategies and planning which lead to better understanding of its customers’ needs and ultimately to a more efficient relationship with the subscribers and enhanced customer satisfaction. At the same time, the results enable the description and characterization of expenditure patterns

  11. An Algorithm for Morphological Segmentation of Esperanto Words

    Directory of Open Access Journals (Sweden)

    Guinard Theresa

    2016-04-01

    Full Text Available Morphological analysis (finding the component morphemes of a word and tagging morphemes with part-of-speech information is a useful preprocessing step in many natural language processing applications, especially for synthetic languages. Compound words from the constructed language Esperanto are formed by straightforward agglutination, but for many words, there is more than one possible sequence of component morphemes. However, one segmentation is usually more semantically probable than the others. This paper presents a modified n-gram Markov model that finds the most probable segmentation of any Esperanto word, where the model’s states represent morpheme part-of-speech and semantic classes. The overall segmentation accuracy was over 98% for a set of presegmented dictionary words.

  12. Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

    Science.gov (United States)

    Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

    2011-10-01

    Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

  13. Open-source algorithm for automatic choroid segmentation of OCT volume reconstructions

    Science.gov (United States)

    Mazzaferri, Javier; Beaton, Luke; Hounye, Gisèle; Sayah, Diane N.; Costantino, Santiago

    2017-02-01

    The use of optical coherence tomography (OCT) to study ocular diseases associated with choroidal physiology is sharply limited by the lack of available automated segmentation tools. Current research largely relies on hand-traced, single B-Scan segmentations because commercially available programs require high quality images, and the existing implementations are closed, scarce and not freely available. We developed and implemented a robust algorithm for segmenting and quantifying the choroidal layer from 3-dimensional OCT reconstructions. Here, we describe the algorithm, validate and benchmark the results, and provide an open-source implementation under the General Public License for any researcher to use (https://www.mathworks.com/matlabcentral/fileexchange/61275-choroidsegmentation).

  14. Image understanding algorithms for segmentation evaluation and region-of-interest identification using Bayesian networks

    Science.gov (United States)

    Jaber, Mustafa; Saber, Eli

    2011-06-01

    A two-fold image understanding algorithm based on Bayesian networks is introduced. The methodology has modules for image segmentation evaluation and region of interest (ROI) identification. The former uses a set of segmentation maps (SMs) of a target image to identify the optimal one. These SMs could be generated from the same segmentation algorithm at different thresholds or from different segmentation techniques. Global and regional low-level image features are extracted from the optimal SM and used along with the original image to identify the ROI. The proposed algorithm was tested on a set of 4000 color images that are publicly available and compared favorably to the state-of-the-art techniques. Applications of the proposed framework include image compression, image summarization, mobile phone imagery, digital photo cropping, and image thumb-nailing.

  15. HARDWARE REALIZATION OF CANNY EDGE DETECTION ALGORITHM FOR UNDERWATER IMAGE SEGMENTATION USING FIELD PROGRAMMABLE GATE ARRAYS

    Directory of Open Access Journals (Sweden)

    ALEX RAJ S. M.

    2017-09-01

    Full Text Available Underwater images raise new challenges in the field of digital image processing technology in recent years because of its widespread applications. There are many tangled matters to be considered in processing of images collected from water medium due to the adverse effects imposed by the environment itself. Image segmentation is preferred as basal stage of many digital image processing techniques which distinguish multiple segments in an image and reveal the hidden crucial information required for a peculiar application. There are so many general purpose algorithms and techniques that have been developed for image segmentation. Discontinuity based segmentation are most promising approach for image segmentation, in which Canny Edge detection based segmentation is more preferred for its high level of noise immunity and ability to tackle underwater environment. Since dealing with real time underwater image segmentation algorithm, which is computationally complex enough, an efficient hardware implementation is to be considered. The FPGA based realization of the referred segmentation algorithm is presented in this paper.

  16. An improved optimum-path forest clustering algorithm for remote sensing image segmentation

    Science.gov (United States)

    Chen, Siya; Sun, Tieli; Yang, Fengqin; Sun, Hongguang; Guan, Yu

    2018-03-01

    Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.

  17. COUPLING REGULAR TESSELLATION WITH RJMCMC ALGORITHM TO SEGMENT SAR IMAGE WITH UNKNOWN NUMBER OF CLASSES

    Directory of Open Access Journals (Sweden)

    Y. Wang

    2016-06-01

    Full Text Available This paper presents a Synthetic Aperture Radar (SAR image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC' algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a RJMCMC algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.

  18. Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver.

    Science.gov (United States)

    Marcan, Marija; Pavliha, Denis; Music, Maja Marolt; Fuckan, Igor; Magjarevic, Ratko; Miklavcic, Damijan

    2014-09-01

    Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input. We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data. Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm. The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation-based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.

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

    Science.gov (United States)

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

    2011-07-01

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

  20. An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2014-01-01

    Full Text Available A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA. An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.

  1. Contour detection and completion for inpainting and segmentation based on topological gradient and fast marching algorithms.

    Science.gov (United States)

    Auroux, Didier; Cohen, Laurent D; Masmoudi, Mohamed

    2011-01-01

    We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segmentation) require continuous contours. For this purpose, we consider the fast marching algorithm in order to find minimal paths in the topological gradient image. This coupled algorithm quickly provides accurate and connected contours. We present then two numerical applications, to image inpainting and segmentation, of this hybrid algorithm.

  2. Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation

    Science.gov (United States)

    Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

    2015-01-01

    Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it. PMID:26221133

  3. Multispectral IKONOS image segmentation based on texture marker-controlled watershed algorithm

    Science.gov (United States)

    Xiao, Pengfeng; Feng, Xuezhi; Zhao, Shuhe; She, Jianfeng

    2007-11-01

    Segmentation has already been recognized as a valuable and complementary approach that performs a region-based rather than a point-based evaluation of high-resolution remotely sensed data. An approach to segmentation of multispectral IKONOS image based on texture marker-controlled watershed transform is presented. Primarily the texture and edge features are extracted from the response of log Gabor filtering. The texture features are obtained from the amplitude response, and phase congruency is introduced to detect invariant edge features. Then a method for multispectral IKONOS image segmentation based on band feature combination is demonstrated. After that an algorithm to combining texture with edge features is presented and used to implement the marker-controlled watershed segmentation. Finally empirical discrepancy is calculated to evaluate the segmentation results. It shows that the precision of right segmentation rate is up to 75% to 85%.

  4. An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Zoran N. Milivojevic

    2011-09-01

    Full Text Available The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures.

  5. Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm.

    Science.gov (United States)

    Lorenzo-Valdés, Maria; Sanchez-Ortiz, Gerardo I; Elkington, Andrew G; Mohiaddin, Raad H; Rueckert, Daniel

    2004-09-01

    In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate a priori information into the classification process. The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 healthy volunteers. It provides space and time-varying probability maps for the left and right ventricles, the myocardium, and background structures such as the liver, stomach, lungs and skin. In addition to using the probabilistic cardiac atlas as a priori information, the segmentation algorithm incorporates spatial and temporal contextual information by using 4D Markov Random Fields. After the classification, the largest connected component of each structure is extracted using a global connectivity filter which improves the results significantly, especially for the myocardium. Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. We used the 'leave one out' test where the image set to be segmented was not used in the construction of its corresponding atlas. Results show that the procedure can successfully segment the left ventricle (LV) (r = 0.96), myocardium (r = 0.92) and right ventricle (r = 0.92). In addition, 4D images from 10 patients with hypertrophic cardiomyopathy were also manually and automatically segmented yielding a good correlation in the volumes of the LV (r = 0.93) and myocardium (0.94) when the atlas constructed with volunteers is blurred.

  6. Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

    Science.gov (United States)

    Janaki, Sathya D.; Geetha, K.

    2017-06-01

    Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.

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

    Science.gov (United States)

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

    2013-03-01

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

  8. THE ROLE OF ECG IN LOCALIZING THE CULPRIT VESSEL OCCLUSION IN ACUTE ST SEGMENT ELEVATION MYOCARDICAL INFARCTION WITH ANGIOGRAPHIC CORRELATION

    OpenAIRE

    Markandeya Rao; Ravindra Kumar; Nanditha

    2015-01-01

    BACKGROUND & OBJECTIVES The Electrocardiogram remains a crucial tool in the identification and management of acute myocardial infarction. A detailed analysis of patterns of ST-segment elevation may influence decisions regarding the perfusion therapy. This study was undertaken to identify the culprit vessel from ECG in patients with acute ST elevation myocardial infarction and correlate with coronary angiogram. MATERIALS & METHODS This is a prospective study, condu...

  9. A novel white blood cells segmentation algorithm based on adaptive neutrosophic similarity score.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2018-12-01

    White blood cells (WBCs) play a crucial role in the diagnosis of many diseases according to their numbers or morphology. The recent digital pathology equipments investigate and analyze the blood smear images automatically. The previous automated segmentation algorithms worked on healthy and non-healthy WBCs separately. Also, such algorithms had employed certain color components which leak adaptively with different datasets. In this paper, a novel segmentation algorithm for WBCs in the blood smear images is proposed using multi-scale similarity measure based on the neutrosophic domain. We employ neutrosophic similarity score to measure the similarity between different color components of the blood smear image. Since we utilize different color components from different color spaces, we modify the neutrosphic score algorithm to be adaptive. Two different segmentation frameworks are proposed: one for the segmentation of nucleus, and the other for the cytoplasm of WBCs. Moreover, our proposed algorithm is applied to both healthy and non-healthy WBCs. in some cases, the single blood smear image gather between healthy and non-healthy WBCs which is considered in our proposed algorithm. Also, our segmentation algorithm is performed without any external morphological binary enhancement methods which may effect on the original shape of the WBC. Different public datasets with different resolutions were used in our experiments. We evaluate the system performance based on both qualitative and quantitative measurements. The quantitative results indicates high precision rates of the segmentation performance measurement A1 = 96.5% and A2 = 97.2% of the proposed method. The average segmentation performance results for different WBCs types reach to 97.6%. In this paper, a method based on adaptive neutrosphic sets similarity score is proposed in order to detect WBCs from a blood smear microscopic image and segment its components (nucleus and the cytoplasm). The proposed

  10. Evaluation of an improved technique for lumen path definition and lumen segmentation of atherosclerotic vessels in CT angiography

    Energy Technology Data Exchange (ETDEWEB)

    Velsen, Evert F.S. van; Niessen, Wiro J.; Meijering, Erik; Stokking, Rik [University Medical Center Rotterdam, Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam (Netherlands); Weert, Thomas T. de; Monye, Cecile de; Lugt, Aad van der [University Medical Center Rotterdam, Department of Radiology, Erasmus MC, Rotterdam (Netherlands)

    2007-07-15

    Vessel image analysis is crucial when considering therapeutical options for (cardio-) vascular diseases. Our method, VAMPIRE (Vascular Analysis using Multiscale Paths Inferred from Ridges and Edges), involves two parts: a user defines a start- and endpoint upon which a lumen path is automatically defined, and which is used for initialization; the automatic segmentation of the vessel lumen on computed tomographic angiography (CTA) images. Both parts are based on the detection of vessel-like structures by analyzing intensity, edge, and ridge information. A multi-observer evaluation study was performed to compare VAMPIRE with a conventional method on the CTA data of 15 patients with carotid artery stenosis. In addition to the start- and endpoint, the two radiologists required on average 2.5 (SD: 1.9) additional points to define a lumen path when using the conventional method, and 0.1 (SD: 0.3) when using VAMPIRE. The segmentation results were quantitatively evaluated using Similarity Indices, which were slightly lower between VAMPIRE and the two radiologists (respectively 0.90 and 0.88) compared with the Similarity Index between the radiologists (0.92). The evaluation shows that the improved definition of a lumen path requires minimal user interaction, and that using this path as initialization leads to good automatic lumen segmentation results. (orig.)

  11. A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions.

    Science.gov (United States)

    Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

    2016-12-07

    The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

  12. A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions

    Science.gov (United States)

    Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

    2016-01-01

    The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application. PMID:27924935

  13. Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm.

    Science.gov (United States)

    Jackman, Patrick; Sun, Da-Wen; Allen, Paul

    2009-10-01

    An algorithm for automatic segmentation of beef longissimus dorsi (LD) muscle and marbling has been developed. The algorithm used simple thresholding to remove the background and then used clustering and thresholding with contrast enhancement via a customised greyscale to remove marbling. It was possible to attain lean muscle free of obvious marbling or background pixels where specular reflection could be effectively mitigated. Features of the automatically derived LD muscle and marbling images were compared to corresponding features of LD muscle and marbling images derived with a segmentation method requiring manual completion. Very strong correlations (up to r=1) were found between the colour features of both sets of LD muscle images. Strong correlations (up to r=0.96) were found between the features of both sets of marbling images. The automatic segmentation method has shown its good ability to approximate colour and marbling features. The algorithm has adaptable parameters and can be retailored to suit different image acquisition environments.

  14. Performance of an open-source heart sound segmentation algorithm on eight independent databases.

    Science.gov (United States)

    Liu, Chengyu; Springer, David; Clifford, Gari D

    2017-08-01

    Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality. Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the [Formula: see text] measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated. The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average [Formula: see text] score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The [Formula: see text] score was shown to increases with an increases in the tolerance window size, as expected. The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for

  15. SEGMENTATION OF HYPERSPECTRAL IMAGE USING JSEG BASED ON UNSUPERVISED CLUSTERING ALGORITHMS

    OpenAIRE

    V. Saravana Kumar; E.R. Naganathan

    2016-01-01

    Hyperspectral image analysis is a complicated and challenging task due to the inherent nature of the image. The main aim of this work is to segment the object in hyperspectral scene using image processing technique. This paper address a novel approach entitled as Segmentation of hyperspectral image using JSEG based on unsupervised cluster methods. In the preprocessing part, single band is picked out from the hyperspectral image and then converts into false color image. The JSEG algorithm is s...

  16. A Time-Consistent Video Segmentation Algorithm Designed for Real-Time Implementation

    Directory of Open Access Journals (Sweden)

    M. El Hassani

    2008-01-01

    Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved change-detection mask. This mask is designed using both illumination differences between frames and region segmentation of the previous frame. By considering both pixel and region levels, we obtain a particularly efficient algorithm at a low computational cost, allowing its implementation in real-time on the TriMedia processor for CIF image sequences.

  17. An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes

    Science.gov (United States)

    2002-01-01

    Experts algo- rithm first collects statistics about the frequency and boundary entropy of ngrams , then passes a window over the series and has two...a simple matter to cut the series at locations with high vote counts. Here are the steps of the algorithm: Build an ngram trie of depth n+1. Nodes at...level i+1 of the trie represent ngrams of length i. The children of a node are the extensions of the ngram represented by the node. For example, a b c

  18. A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

    Science.gov (United States)

    Strîmbu, Victor F.; Strîmbu, Bogdan M.

    2015-06-01

    This work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures. The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m2. More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent.

  19. Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    K. Parvathi

    2009-01-01

    Full Text Available The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

  20. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

    Science.gov (United States)

    Hatipoglu, Nuh; Bilgin, Gokhan

    2017-10-01

    In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

  1. Performance evaluation of a contextual news story segmentation algorithm

    Science.gov (United States)

    Janvier, Bruno; Bruno, Eric; Marchand-Maillet, Stephane; Pun, Thierry

    2006-01-01

    The problem of semantic video structuring is vital for automated management of large video collections. The goal is to automatically extract from the raw data the inner structure of a video collection; so that a whole new range of applications to browse and search video collections can be derived out of this high-level segmentation. To reach this goal, we exploit techniques that consider the full spectrum of video content; it is fundamental to properly integrate technologies from the fields of computer vision, audio analysis, natural language processing and machine learning. In this paper, a multimodal feature vector providing a rich description of the audio, visual and text modalities is first constructed. Boosted Random Fields are then used to learn two types of relationships: between features and labels and between labels associated with various modalities for improved consistency of the results. The parameters of this enhanced model are found iteratively by using two successive stages of Boosting. We experimented using the TRECvid corpus and show results that validate the approach over existing studies.

  2. A fully automated algorithm of baseline correction based on wavelet feature points and segment interpolation

    Science.gov (United States)

    Qian, Fang; Wu, Yihui; Hao, Peng

    2017-11-01

    Baseline correction is a very important part of pre-processing. Baseline in the spectrum signal can induce uneven amplitude shifts across different wavenumbers and lead to bad results. Therefore, these amplitude shifts should be compensated before further analysis. Many algorithms are used to remove baseline, however fully automated baseline correction is convenient in practical application. A fully automated algorithm based on wavelet feature points and segment interpolation (AWFPSI) is proposed. This algorithm finds feature points through continuous wavelet transformation and estimates baseline through segment interpolation. AWFPSI is compared with three commonly introduced fully automated and semi-automated algorithms, using simulated spectrum signal, visible spectrum signal and Raman spectrum signal. The results show that AWFPSI gives better accuracy and has the advantage of easy use.

  3. Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe Algorithm

    Directory of Open Access Journals (Sweden)

    Xiaoqin Zhou

    2017-05-01

    Full Text Available Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in three-dimensional space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D (Red-Green-Blue and Depth algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

  4. Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    Science.gov (United States)

    A., Javadpour; A., Mohammadi

    2016-01-01

    Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

  5. An evolutionary algorithm for the segmentation of muscles and bones of the lower limb.

    Science.gov (United States)

    Lpez, Marco A.; Braidot, A.; Sattler, Anbal; Schira, Claudia; Uriburu, E.

    2016-04-01

    In the field of medical image segmentation, muscles segmentation is a problem that has not been fully resolved yet. This is due to the fact that the basic assumption of image segmentation, which asserts that a visual distinction should ex- ist between the different structures to be identified, is infringed. As the tissue composition of two different muscles is the same, it becomes extremely difficult to distinguish one another if they are near. We have developed an evolutionary algorithm which selects the set and the sequence of morphological operators that better segments muscles and bones from an MRI image. The achieved results shows that the developed algorithm presents average sensitivity values close to 75% in the segmentation of the different processed muscles and bones. It also presents average specificity values close to 93% for the same structures. Furthermore, the algorithm can identify muscles that are closely located through the path from their origin point to their insertions, with very low error values (below 7%) .

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

    Science.gov (United States)

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

    2017-11-01

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

  7. A Decision-Tree-Based Algorithm for Speech/Music Classification and Segmentation

    Directory of Open Access Journals (Sweden)

    Yizhar Lavner

    2009-01-01

    Full Text Available We present an efficient algorithm for segmentation of audio signals into speech or music. The central motivation to our study is consumer audio applications, where various real-time enhancements are often applied. The algorithm consists of a learning phase and a classification phase. In the learning phase, predefined training data is used for computing various time-domain and frequency-domain features, for speech and music signals separately, and estimating the optimal speech/music thresholds, based on the probability density functions of the features. An automatic procedure is employed to select the best features for separation. In the test phase, initial classification is performed for each segment of the audio signal, using a three-stage sieve-like approach, applying both Bayesian and rule-based methods. To avoid erroneous rapid alternations in the classification, a smoothing technique is applied, averaging the decision on each segment with past segment decisions. Extensive evaluation of the algorithm, on a database of more than 12 hours of speech and more than 22 hours of music showed correct identification rates of 99.4% and 97.8%, respectively, and quick adjustment to alternating speech/music sections. In addition to its accuracy and robustness, the algorithm can be easily adapted to different audio types, and is suitable for real-time operation.

  8. Surgical wound segmentation based on adaptive threshold edge detection and genetic algorithm

    Science.gov (United States)

    Shih, Hsueh-Fu; Ho, Te-Wei; Hsu, Jui-Tse; Chang, Chun-Che; Lai, Feipei; Wu, Jin-Ming

    2017-02-01

    Postsurgical wound care has a great impact on patients' prognosis. It often takes few days, even few weeks, for the wound to stabilize, which incurs a great cost of health care and nursing resources. To assess the wound condition and diagnosis, it is important to segment out the wound region for further analysis. However, the scenario of this strategy often consists of complicated background and noise. In this study, we propose a wound segmentation algorithm based on Canny edge detector and genetic algorithm with an unsupervised evaluation function. The results were evaluated by the 112 clinical images, and 94.3% of images were correctly segmented. The judgment was based on the evaluation of experimented medical doctors. This capability to extract complete wound regions, makes it possible to conduct further image analysis such as intelligent recovery evaluation and automatic infection requirements.

  9. An algorithm for automating the registration of USDA segment ground data to LANDSAT MSS data

    Science.gov (United States)

    Graham, M. H. (Principal Investigator)

    1981-01-01

    The algorithm is referred to as the Automatic Segment Matching Algorithm (ASMA). The ASMA uses control points or the annotation record of a P-format LANDSAT compter compatible tape as the initial registration to relate latitude and longitude to LANDSAT rows and columns. It searches a given area of LANDSAT data with a 2x2 sliding window and computes gradient values for bands 5 and 7 to match the segment boundaries. The gradient values are held in memory during the shifting (or matching) process. The reconstructed segment array, containing ones (1's) for boundaries and zeros elsewhere are computer compared to the LANDSAT array and the best match computed. Initial testing of the ASMA indicates that it has good potential for replacing the manual technique.

  10. Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

    Science.gov (United States)

    Muhammad, Azam Sheikh; Lavesson, Niklas; Davidsson, Paul; Nilsson, Mikael

    Traffic Sign Recognition is a widely studied problem and its dynamic nature calls for the application of a broad range of preprocessing, segmentation, and recognition techniques but few databases are available for evaluation. We have produced a database consisting of 1,300 images captured by a video camera. On this database we have conducted a systematic experimental study. We used four different preprocessing techniques and designed a generic speed sign segmentation algorithm. Then we selected a range of contemporary speed sign classification algorithms using shape based segmented binary images for training and evaluated their results using four metrics, including accuracy and processing speed. The results indicate that Naive Bayes and Random Forest seem particularly well suited for this recognition task. Moreover, we show that two specific preprocessing techniques appear to provide a better basis for concept learning than the others.

  11. Analysis of the Command and Control Segment (CCS) attitude estimation algorithm

    Science.gov (United States)

    Stockwell, Catherine

    1993-01-01

    This paper categorizes the qualitative behavior of the Command and Control Segment (CCS) differential correction algorithm as applied to attitude estimation using simultaneous spin axis sun angle and Earth cord length measurements. The categories of interest are the domains of convergence, divergence, and their boundaries. Three series of plots are discussed that show the dependence of the estimation algorithm on the vehicle radius, the sun/Earth angle, and the spacecraft attitude. Common qualitative dynamics to all three series are tabulated and discussed. Out-of-limits conditions for the estimation algorithm are identified and discussed.

  12. Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi

    NARCIS (Netherlands)

    Lassen, B.C.; Rikxoort, E.M. van; Schmidt, M.; Kerkstra, S.; Ginneken, B. van; Kuhnigk, J.

    2013-01-01

    Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. In this work an automated segmentation approach is presented that performs a markerbased watershed transformation on {CT} scans to subdivide the

  13. Research of fiber optical faceplate defects segmentation based on improved watershed algorithm

    Science.gov (United States)

    Yang, Bingqian; Wang, Mingquan; Zhang, Junsheng; Gao, Jinkai

    2017-08-01

    In this paper, an improved adaptive watershed segmentation method is proposed based on the characteristics of the optical fiber faceplate. Firstly, median filtering and morphological contrast enhancement are performed on the defect images, and then the gradient of the image is obtained by multi-scale morphology. In the improved watershed algorithm, the local minimum is first removed which the depth is lower than H. Then, the local minimum of the depth larger than H as the seed point are extended. Finally, the gradient image is modified by the forced minimum method. The modified gradient image is used to make the watershed Segmentation to get the final segmentation result .The experimental results show that the method can effectively suppress the over-segmentation, and the defects can be extracted well.

  14. High-speed MRF-based segmentation algorithm using pixonal images

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Hassanpour, H.; Naimi, H. M.

    2013-01-01

    Segmentation is one of the most complicated procedures in the image processing that has important role in the image analysis. In this paper, an improved pixon-based method for image segmentation is proposed. In proposed algorithm, complex partial differential equations (PDEs) is used as a kernel...... function to make pixonal image. Using this kernel function causes noise on images to reduce and an image not to be over-segment when the pixon-based method is used. Utilising the PDE-based method leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance...... and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually, we segment the image with the use of a Markov random field. The experimental results indicate that the proposed pixon-based approach has a reduced computational load...

  15. GPU-based acceleration of an automatic white matter segmentation algorithm using CUDA.

    Science.gov (United States)

    Labra, Nicole; Figueroa, Miguel; Guevara, Pamela; Duclap, Delphine; Hoeunou, Josselin; Poupon, Cyril; Mangin, Jean-Francois

    2013-01-01

    This paper presents a parallel implementation of an algorithm for automatic segmentation of white matter fibers from tractography data. We execute the algorithm in parallel using a high-end video card with a Graphics Processing Unit (GPU) as a computation accelerator, using the CUDA language. By exploiting the parallelism and the properties of the memory hierarchy available on the GPU, we obtain a speedup in execution time of 33.6 with respect to an optimized sequential version of the algorithm written in C, and of 240 with respect to the original Python/C++ implementation. The execution time is reduced from more than two hours to only 35 seconds for a subject dataset of 800,000 fibers, thus enabling applications that use interactive segmentation and visualization of small to medium-sized tractography datasets.

  16. Contour Detection and Completion for Inpainting and Segmentation Based on Topological Gradient and Fast Marching Algorithms

    Directory of Open Access Journals (Sweden)

    Didier Auroux

    2011-01-01

    Full Text Available We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segmentation require continuous contours. For this purpose, we consider the fast marching algorithm in order to find minimal paths in the topological gradient image. This coupled algorithm quickly provides accurate and connected contours. We present then two numerical applications, to image inpainting and segmentation, of this hybrid algorithm.

  17. Skeletonization algorithm-based blood vessel quantification using in vivo 3D photoacoustic imaging

    Science.gov (United States)

    Meiburger, K. M.; Nam, S. Y.; Chung, E.; Suggs, L. J.; Emelianov, S. Y.; Molinari, F.

    2016-11-01

    Blood vessels are the only system to provide nutrients and oxygen to every part of the body. Many diseases can have significant effects on blood vessel formation, so that the vascular network can be a cue to assess malicious tumor and ischemic tissues. Various imaging techniques can visualize blood vessel structure, but their applications are often constrained by either expensive costs, contrast agents, ionizing radiations, or a combination of the above. Photoacoustic imaging combines the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging, and image contrast depends on optical absorption. This enables the detection of light absorbing chromophores such as hemoglobin with a greater penetration depth compared to purely optical techniques. We present here a skeletonization algorithm for vessel architectural analysis using non-invasive photoacoustic 3D images acquired without the administration of any exogenous contrast agents. 3D photoacoustic images were acquired on rats (n  =  4) in two different time points: before and after a burn surgery. A skeletonization technique based on the application of a vesselness filter and medial axis extraction is proposed to extract the vessel structure from the image data and six vascular parameters (number of vascular trees (NT), vascular density (VD), number of branches (NB), 2D distance metric (DM), inflection count metric (ICM), and sum of angles metric (SOAM)) were calculated from the skeleton. The parameters were compared (1) in locations with and without the burn wound on the same day and (2) in the same anatomic location before (control) and after the burn surgery. Four out of the six descriptors were statistically different (VD, NB, DM, ICM, p  <  0.05) when comparing two anatomic locations on the same day and when considering the same anatomic location at two separate times (i.e. before and after burn surgery). The study demonstrates an

  18. Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

    Science.gov (United States)

    Rajab, Maher I

    2011-11-01

    Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

  19. Electrocardiography as a predictor of left main or three-vessel disease in patients with non-ST segment elevation acute coronary syndrome

    Directory of Open Access Journals (Sweden)

    Ashraf Hussien

    2011-06-01

    Conclusion: ST-segment elevation in lead aVR ⩾0.5 mm and QRS duration ⩾90 ms are good electrocardiographic predictors of left main or three vessel disease in patients with non-ST segment elevation acute coronary syndrome.

  20. A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Barari, Amin

    2011-01-01

    Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...... for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over-segmentation...

  1. Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

    Directory of Open Access Journals (Sweden)

    Wuli Wang

    2017-01-01

    Full Text Available Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.

  2. SEGMENTATION OF HYPERSPECTRAL IMAGE USING JSEG BASED ON UNSUPERVISED CLUSTERING ALGORITHMS

    Directory of Open Access Journals (Sweden)

    V. Saravana Kumar

    2016-11-01

    Full Text Available Hyperspectral image analysis is a complicated and challenging task due to the inherent nature of the image. The main aim of this work is to segment the object in hyperspectral scene using image processing technique. This paper address a novel approach entitled as Segmentation of hyperspectral image using JSEG based on unsupervised cluster methods. In the preprocessing part, single band is picked out from the hyperspectral image and then converts into false color image. The JSEG algorithm is segregate the false color image properly without manual parameter adjustment. The segmentation has carried in two major stages. To begin with, colors in the image are quantized to represent several classes which can be used to differentiate regions in the image. Besides, hit rate regions with cognate color regions merging algorithm is used. In region merging part, K-means, Fuzzy C-Means (FCM and Fast K-Means weighted option (FWKM algorithm are used to segregate the image in accordance with the color for each cluster and its neighborhoods. Experiment results of above clustering method could be analyzed in terms of mean, standard deviation, number of cluster, number of pixels, time taken, number of objects occur in the resultant image. FWKM algorithm results yields good performance than its counterparts.

  3. Réalisation d'algorithmes de segmentation : Application sur des images du cancer du sein

    OpenAIRE

    Saïdi, Fatima Zahra

    2014-01-01

    Le traitement des images médicales est une discipline nouvelle, riche et variée, mais dans laquelle les nombreuses méthodes existantes sont délicates à appliquer aux probl èmes réels. Les travaux de ce mémoire s'articulent autour de deux axes : l'utilisation de di érents algorithmes de segmentation d'images microscopiques acquises à partir des biopsies du sein qui représentent des tumeurs bénignes et malignes, à savoir la segmentation par morphologie mathématique ( la Ligne de ...

  4. THE ROLE OF ECG IN LOCALIZING THE CULPRIT VESSEL OCCLUSION IN ACUTE ST SEGMENT ELEVATION MYOCARDICAL INFARCTION WITH ANGIOGRAPHIC CORRELATION

    Directory of Open Access Journals (Sweden)

    Markandeya Rao

    2015-12-01

    Full Text Available BACKGROUND & OBJECTIVES The Electrocardiogram remains a crucial tool in the identification and management of acute myocardial infarction. A detailed analysis of patterns of ST-segment elevation may influence decisions regarding the perfusion therapy. This study was undertaken to identify the culprit vessel from ECG in patients with acute ST elevation myocardial infarction and correlate with coronary angiogram. MATERIALS & METHODS This is a prospective study, conducted on 126 patients in Osmania General Hospital, Hyderabad. Patients with ST segment elevation from ECG was evaluated to identify culprit vessel and later correlated with coronary angiogram. RESULTS Amongst 126 patients in this study, 70 patients had anterior wall and 56 patients had inferior wall myocardial infarction. ST> 1mm in V4R, ST  V3/ST  LIII Lead II was the most sensitive and ratio of STV3/STLIII >1.2 was the most specific criteria. ST in inferior leads > 1mm had maximum sensitivity in localizing occlusion in proximal D1 occlusion proximal to S1 as well. Absence of ST i in inferior leads is the most sensitive criteria in occlusion distal to S1 as well as in distal D1 in AWMI. CONCLUSION The admission ECG in patients with ST elevation AMI is valuable not only for determining early reperfusion treatment, but also provides important information to guide clinical decision-making.

  5. Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images.

    Science.gov (United States)

    Zhao, Yitian; Rada, Lavdie; Chen, Ke; Harding, Simon P; Zheng, Yalin

    2015-09-01

    Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L(2) Lebesgue measure of the γ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., H(1) Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.

  6. An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

    Directory of Open Access Journals (Sweden)

    Mengling Zhao

    2015-01-01

    Full Text Available As a computational intelligence method, artificial immune network (AIN algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN. The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC, and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

  7. Code Synchronization Algorithm Based on Segment Correlation in Spread Spectrum Communication

    Directory of Open Access Journals (Sweden)

    Aohan Li

    2015-10-01

    Full Text Available Spread Spectrum (SPSP Communication is the theoretical basis of Direct Sequence Spread Spectrum (DSSS transceiver technology. Spreading code, modulation, demodulation, carrier synchronization and code synchronization in SPSP communications are the core parts of DSSS transceivers. This paper focuses on the code synchronization problem in SPSP communications. A novel code synchronization algorithm based on segment correlation is proposed. The proposed algorithm can effectively deal with the informational misjudgment caused by the unreasonable data acquisition times. This misjudgment may lead to an inability of DSSS receivers to restore transmitted signals. Simulation results show the feasibility of a DSSS transceiver design based on the proposed code synchronization algorithm. Finally, the communication functions of the DSSS transceiver based on the proposed code synchronization algorithm are implemented on Field Programmable Gate Array (FPGA.

  8. A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

    Science.gov (United States)

    Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

    2015-01-01

    Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

  9. Simulation on Vessel Intelligent Collision Avoidance Based on Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Li Weifeng

    2016-10-01

    Full Text Available TAs the rapid development of the ship equipments and navigation technology, vessel intelligent collision avoidance theory was researched world widely. Meantime, more and more ship intelligent collision avoidance products are put into use. It not only makes the ship much safer, but also lighten the officers work intensity and improve the ship’s economy. The paper based on the International Regulation for Preventing Collision at sea and ship domain theories, with the ship proceeding distance when collision avoidance as the objective function, through the artificial fish swarm algorithm to optimize the collision avoidance path, and finally simulates overtaking situation, crossing situation and head-on situation three classic meeting situation of ships on the sea by VC++ computer language. Calculation and simulation results are basically consistent with the actual situation which certifies that its validity.

  10. Application of Micro-segmentation Algorithms to the Healthcare Market:A Case Study

    Energy Technology Data Exchange (ETDEWEB)

    Sukumar, Sreenivas R [ORNL; Aline, Frank [ORNL

    2013-01-01

    We draw inspiration from the recent success of loyalty programs and targeted personalized market campaigns of retail companies such as Kroger, Netflix, etc. to understand beneficiary behaviors in the healthcare system. Our posit is that we can emulate the financial success the companies have achieved by better understanding and predicting customer behaviors and translating such success to healthcare operations. Towards that goal, we survey current practices in market micro-segmentation research and analyze health insurance claims data using those algorithms. We present results and insights from micro-segmentation of the beneficiaries using different techniques and discuss how the interpretation can assist with matching the cost-effective insurance payment models to the beneficiary micro-segments.

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

    Science.gov (United States)

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

    2007-01-01

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

  12. An algorithm for automatic segmentation and classification of magnetic resonance brain images.

    Science.gov (United States)

    Erickson, B J; Avula, R T

    1998-05-01

    In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images were acquired in 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images.

  13. An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues

    Science.gov (United States)

    Ribeiro, Angela; Ranz, Juan; Burgos-Artizzu, Xavier P.; Pajares, Gonzalo; Sanchez del Arco, Maria J.; Navarrete, Luis

    2011-01-01

    Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically quantifying the residue coverage. In other words, the objective is to achieve a segmentation that would permit the discrimination of the texture of the residue so that the output of the segmentation process is a binary image in which residue zones are isolated from the rest. The RGB images used come from a sample of images in which sections of terrain were photographed with a conventional camera positioned in zenith orientation atop a tripod. The images were taken outdoors under uncontrolled lighting conditions. Up to 92% similarity was achieved between the images obtained by the segmentation process proposed in this paper and the templates made by an elaborate manual tracing process. In addition to the proposed segmentation procedure and the fine tuning procedure that was developed, a global quantification of the soil coverage by residues for the sampled area was achieved that differed by only 0.85% from the quantification obtained using template images. Moreover, the proposed method does not depend on the type of residue present in the image. The study was conducted at the experimental farm “El Encín” in Alcalá de Henares (Madrid, Spain). PMID:22163966

  14. A Gaussian process and derivative spectral-based algorithm for red blood cell segmentation

    Science.gov (United States)

    Xue, Yingying; Wang, Jianbiao; Zhou, Mei; Hou, Xiyue; Li, Qingli; Liu, Hongying; Wang, Yiting

    2017-07-01

    As an imaging technology used in remote sensing, hyperspectral imaging can provide more information than traditional optical imaging of blood cells. In this paper, an AOTF based microscopic hyperspectral imaging system is used to capture hyperspectral images of blood cells. In order to achieve the segmentation of red blood cells, Gaussian process using squared exponential kernel function is applied first after the data preprocessing to make the preliminary segmentation. The derivative spectrum with spectral angle mapping algorithm is then applied to the original image to segment the boundary of cells, and using the boundary to cut out cells obtained from the Gaussian process to separated adjacent cells. Then the morphological processing method including closing, erosion and dilation is applied so as to keep adjacent cells apart, and by applying median filtering to remove noise points and filling holes inside the cell, the final segmentation result can be obtained. The experimental results show that this method appears better segmentation effect on human red blood cells.

  15. A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm

    DEFF Research Database (Denmark)

    Nadernejad, E; Barari, Amin

    2011-01-01

    Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...... for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over...

  16. 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.

  17. Low QRS Voltage on Presenting Electrocardiogram Predicts Multi-vessel Disease in Anterior ST-segment Elevation Myocardial Infarction.

    Science.gov (United States)

    Kobayashi, Akihiro; Misumida, Naoki; Aoi, Shunsuke; Kanei, Yumiko

    Low QRS voltage was reported to predict adverse outcomes in acute myocardial infarction in the pre-thrombolytic era. However, the association between low voltage and angiographic findings has not been fully addressed. We performed a retrospective analysis of patients with anterior ST-segment elevation myocardial infarction (STEMI). Low QRS voltage was defined as either peak to peak QRS complex voltage voltage. Patients with low voltage had a higher rate of multi-vessel disease (MVD) (76% vs. 52%, p=0.01). Patients with low voltage were more likely to undergo coronary artery bypass grafting (CABG) during admission (11% vs. 2%, p=0.028). Low voltage was an independent predictor for MVD (OR 2.50; 95% CI 1.12 to 6.03; p=0.032). Low QRS voltage was associated with MVD and in-hospital CABG in anterior STEMI. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. CT liver volumetry using geodesic active contour segmentation with a level-set algorithm

    Science.gov (United States)

    Suzuki, Kenji; Epstein, Mark L.; Kohlbrenner, Ryan; Obajuluwa, Ademola; Xu, Jianwu; Hori, Masatoshi; Baron, Richard

    2010-03-01

    Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(Fliver volumes.

  19. Computer-aided detection of pulmonary embolism: Influence on radiologists' detection performance with respect to vessel segments

    Energy Technology Data Exchange (ETDEWEB)

    Das, Marco; Muehlenbruch, Georg; Helm, Anita; Guenther, Rolf W.; Wildberger, Joachim E. [RWTH Aachen University, Department of Diagnostic Radiology, Aachen (Germany); Bakai, Annemarie [Siemens Medical Solutions, CAD Applications, Malvern, PA (United States); Salganicoff, Marcos; Liang, Jianming; Wolf, Matthias [Siemens Medical Solutions, CT Division, Forchheim (Germany); Stanzel, Sven [RWTH Aachen University, Institute of Medical Statistics, Aachen (Germany)

    2008-07-15

    The purpose was to assess the sensitivity of a CAD software prototype for the detection of pulmonary embolism in MDCT chest examinations with regard to vessel level and to assess the influence on radiologists' detection performance. Forty-three patients with suspected PE were included in this retrospective study. MDCT chest examinations with a standard PE protocol were acquired at a 16-slice MDCT. All patient data were read by three radiologists (R1, R2, R3), and all thrombi were marked. A CAD prototype software was applied to all datasets, and each finding of the software was analyzed with regard to vessel level. The standard of reference was assessed in a consensus read. Sensitivity for the radiologists and CAD software was assessed. Thirty-three patients were positive for PE, with a total of 215 thrombi. The mean overall sensitivity for the CAD software alone was 83% (specificity, 80%). Radiologist sensitivity was 77% = R3, 82% = R2, and R1 = 87%. With the aid of the CAD software, sensitivities increased to 98% (R1), 93% (R2), and 92% (R3) (p<0.0001). CAD performance at the lobar level was 87%, at the segmental 90% and at the subsegmental 77%. With the use of CAD for PE, the detection performance of radiologists can be improved. (orig.)

  20. Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT.

    Science.gov (United States)

    Bae, JangPyo; Kim, Namkug; Lee, Sang Min; Seo, Joon Beom; Kim, Hee Chan

    2014-04-01

    To develop and validate a semiautomatic segmentation method for thoracic cavity volumetry and mediastinum fat quantification of patients with chronic obstructive pulmonary disease. The thoracic cavity region was separated by segmenting multiorgans, namely, the rib, lung, heart, and diaphragm. To encompass various lung disease-induced variations, the inner thoracic wall and diaphragm were modeled by using a three-dimensional surface-fitting method. To improve the accuracy of the diaphragm surface model, the heart and its surrounding tissue were segmented by a two-stage level set method using a shape prior. To assess the accuracy of the proposed algorithm, the algorithm results of 50 patients were compared to the manual segmentation results of two experts with more than 5 years of experience (these manual results were confirmed by an expert thoracic radiologist). The proposed method was also compared to three state-of-the-art segmentation methods. The metrics used to evaluate segmentation accuracy were volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), false negative ratio on VOR (FNRV), average symmetric absolute surface distance (ASASD), average symmetric squared surface distance (ASSSD), and maximum symmetric surface distance (MSSD). In terms of thoracic cavity volumetry, the mean ± SD VOR, FPRV, and FNRV of the proposed method were (98.17 ± 0.84)%, (0.49 ± 0.23)%, and (1.34 ± 0.83)%, respectively. The ASASD, ASSSD, and MSSD for the thoracic wall were 0.28 ± 0.12, 1.28 ± 0.53, and 23.91 ± 7.64 mm, respectively. The ASASD, ASSSD, and MSSD for the diaphragm surface were 1.73 ± 0.91, 3.92 ± 1.68, and 27.80 ± 10.63 mm, respectively. The proposed method performed significantly better than the other three methods in terms of VOR, ASASD, and ASSSD. The proposed semiautomatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart), performed with high accuracy and may be useful

  1. Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT

    Energy Technology Data Exchange (ETDEWEB)

    Bae, JangPyo [Interdisciplinary Program, Bioengineering Major, Graduate School, Seoul National University, Seoul 110-744, South Korea and Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Kim, Namkug, E-mail: namkugkim@gmail.com; Lee, Sang Min; Seo, Joon Beom [Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Kim, Hee Chan [Department of Biomedical Engineering, College of Medicine and Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 110-744 (Korea, Republic of)

    2014-04-15

    Purpose: To develop and validate a semiautomatic segmentation method for thoracic cavity volumetry and mediastinum fat quantification of patients with chronic obstructive pulmonary disease. Methods: The thoracic cavity region was separated by segmenting multiorgans, namely, the rib, lung, heart, and diaphragm. To encompass various lung disease-induced variations, the inner thoracic wall and diaphragm were modeled by using a three-dimensional surface-fitting method. To improve the accuracy of the diaphragm surface model, the heart and its surrounding tissue were segmented by a two-stage level set method using a shape prior. To assess the accuracy of the proposed algorithm, the algorithm results of 50 patients were compared to the manual segmentation results of two experts with more than 5 years of experience (these manual results were confirmed by an expert thoracic radiologist). The proposed method was also compared to three state-of-the-art segmentation methods. The metrics used to evaluate segmentation accuracy were volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), false negative ratio on VOR (FNRV), average symmetric absolute surface distance (ASASD), average symmetric squared surface distance (ASSSD), and maximum symmetric surface distance (MSSD). Results: In terms of thoracic cavity volumetry, the mean ± SD VOR, FPRV, and FNRV of the proposed method were (98.17 ± 0.84)%, (0.49 ± 0.23)%, and (1.34 ± 0.83)%, respectively. The ASASD, ASSSD, and MSSD for the thoracic wall were 0.28 ± 0.12, 1.28 ± 0.53, and 23.91 ± 7.64 mm, respectively. The ASASD, ASSSD, and MSSD for the diaphragm surface were 1.73 ± 0.91, 3.92 ± 1.68, and 27.80 ± 10.63 mm, respectively. The proposed method performed significantly better than the other three methods in terms of VOR, ASASD, and ASSSD. Conclusions: The proposed semiautomatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart

  2. Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers.

    Science.gov (United States)

    Milankovic, Ivan L; Mijailovic, Nikola V; Filipovic, Nenad D; Peulic, Aleksandar S

    2017-01-01

    Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

  3. A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images.

    Science.gov (United States)

    Arslan, Salim; Ozyurek, Emel; Gunduz-Demir, Cigdem

    2014-06-01

    Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. © 2014 International Society for Advancement of Cytometry.

  4. Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Approach for tuning algorithm parameters

    Science.gov (United States)

    Mueller, Jenna L.; Harmany, Zachary T.; Mito, Jeffrey K.; Kennedy, Stephanie A.; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G.; Willett, Rebecca M.; Brown, J. Quincy; Ramanujam, Nimmi

    2013-02-01

    The combination of fluorescent contrast agents with microscopy is a powerful technique to obtain real time images of tissue histology without the need for fixing, sectioning, and staining. The potential of this technology lies in the identification of robust methods for image segmentation and quantitation, particularly in heterogeneous tissues. Our solution is to apply sparse decomposition (SD) to monochrome images of fluorescently-stained microanatomy to segment and quantify distinct tissue types. The clinical utility of our approach is demonstrated by imaging excised margins in a cohort of mice after surgical resection of a sarcoma. Representative images of excised margins were used to optimize the formulation of SD and tune parameters associated with the algorithm. Our results demonstrate that SD is a robust solution that can advance vital fluorescence microscopy as a clinically significant technology.

  5. Optimized adaptation algorithm for HEVC/H.265 dynamic adaptive streaming over HTTP using variable segment duration

    Science.gov (United States)

    Irondi, Iheanyi; Wang, Qi; Grecos, Christos

    2016-04-01

    Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation algorithm is then proposed to accurately estimate the bandwidth and precisely determine the time to download the next

  6. A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

    Science.gov (United States)

    Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

  7. An Algorithm to Guide Recipient Vessel Selection in Cases of Free Functional Muscle Transfer for Facial Reanimation

    Directory of Open Access Journals (Sweden)

    Francis P Henry

    2014-11-01

    Full Text Available BackgroundThe aim of this study was to review the recipient vessels used in our cases of facial reanimation with free functional muscle transfer and to identify patient variables that may predict when the facial vessels are absent. From this we present a protocol for vessel selection in cases when the facial artery and/or vein are absent.MethodsPatients were identified from November 2006 to October 2013. Data was collected on patient demographics, facial palsy aetiology, history of previous facial surgery/trauma and flap/recipient vessels used. A standard operative approach was adopted and performed by a single surgeon.ResultsEighty-seven eligible patients were identified for inclusion amongst which 98 hemifaces were operated upon. The facial artery and vein were the most commonly used recipient vessels (90% and 83% of patients, respectively. Commonly used alternative vessels were the transverse facial vein and superficial temporal artery. Those with congenital facial palsy were significantly more likely to lack a suitable facial vein (P=0.03 and those with a history of previous facial surgery or trauma were significantly more likely to have an absent facial artery and vein (P<0.05.ConclusionsOur algorithm can help to guide vessel selection cases of facial reanimation with free functional muscle transfer. Amongst patients with congenital facial palsy or in those with a previous history of facial surgery or trauma, the facial vessels are more likely to be absent and so the surgeon should then look towards the transverse facial vein and superficial temporal artery as alternative recipient structures.

  8. An Algorithm for Obtaining the Distribution of 1-Meter Lightning Channel Segment Altitudes for Application in Lightning NOx Production Estimation

    Science.gov (United States)

    Peterson, Harold; Koshak, William J.

    2009-01-01

    An algorithm has been developed to estimate the altitude distribution of one-meter lightning channel segments. The algorithm is required as part of a broader objective that involves improving the lightning NOx emission inventories of both regional air quality and global chemistry/climate models. The algorithm was tested and applied to VHF signals detected by the North Alabama Lightning Mapping Array (NALMA). The accuracy of the algorithm was characterized by comparing algorithm output to the plots of individual discharges whose lengths were computed by hand; VHF source amplitude thresholding and smoothing were applied to optimize results. Several thousands of lightning flashes within 120 km of the NALMA network centroid were gathered from all four seasons, and were analyzed by the algorithm. The mean, standard deviation, and median statistics were obtained for all the flashes, the ground flashes, and the cloud flashes. One-meter channel segment altitude distributions were also obtained for the different seasons.

  9. Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET.

    Science.gov (United States)

    Zaidi, Habib; Ruest, Torsten; Schoenahl, Frederic; Montandon, Marie-Louise

    2006-10-01

    Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification

  10. The Cascaded Enhanced k-Means and Fuzzy c-Means Clustering Algorithms for Automated Segmentation of Malaria Parasites

    Directory of Open Access Journals (Sweden)

    Abdul Nasir Aimi Salihah

    2018-01-01

    Full Text Available Malaria continues to be one of the leading causes of death in the world, despite the massive efforts put forth by World Health Organization (WHO in eradicating it, worldwide. Efficient control and proper treatment of this disease requires early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes a malaria parasite segmentation approach via cascaded clustering algorithms to automate the malaria diagnosis process. The comparisons among the cascaded clustering algorithms have been made by considering the accuracy, sensitivity and specificity of the segmented malaria images. Based on the qualitative and quantitative findings, the results show that by using the final centres that have been generated by enhanced k-means (EKM clustering as the initial centres for fuzzy c-means (FCM clustering, has led to the production of good segmented malaria image. The proposed cascaded EKM and FCM clustering has successfully segmented 100 malaria images of Plasmodium Vivax species with average segmentation accuracy, sensitivity and specificity values of 99.22%, 88.84% and 99.56%, respectively. Therefore, the EKM algorithm has given the best performance compared to k-means (KM and moving k-means (MKM algorithms when all the three clustering algorithms are cascaded with FCM algorithm.

  11. Local pixel value collection algorithm for spot segmentation in two-dimensional gel electrophoresis research.

    Science.gov (United States)

    Peer, Peter; Corzo, Luis Galo

    2007-01-01

    Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. A comparison of results is done with a method called pixel value collection (PVC). Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC). The results show that LPVC achieves similar segmentation results as PVC, but is much faster than PVC.

  12. Local Pixel Value Collection Algorithm for Spot Segmentation in Two-Dimensional Gel Electrophoresis Research

    Directory of Open Access Journals (Sweden)

    Peter Peer

    2007-09-01

    Full Text Available Two-dimensional gel-electrophoresis (2-DE images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis database. A comparison of results is done with a method called pixel value collection (PVC. Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC. The results show that LPVC achieves similar segmentation results as PVC, but is much faster than PVC.

  13. SHADOW DETECTION FROM VERY HIGH RESOLUTON SATELLITE IMAGE USING GRABCUT SEGMENTATION AND RATIO-BAND ALGORITHMS

    Directory of Open Access Journals (Sweden)

    N. M. S. M. Kadhim

    2015-03-01

    Full Text Available Very-High-Resolution (VHR satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour, the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates

  14. Usage of Clustering Algorithm to Segment Image into Simply Connected Domains

    Directory of Open Access Journals (Sweden)

    S. V. Belim

    2015-01-01

    Full Text Available The article suggests a method of image segmentation into simply connected domains based on color. Pixels from an original image are represented as points in five-dimensional space which includes three color and two spatial coordinates. The points are normalized in order to eliminate distinguished characteristics. The set of points is compared with a weighted complete graph. The points of five-dimensional space are vertexes in the graph. Euclidian distance between the points is used as weights of the edges in the graph. To solve the task of clustering, a minimum spanning tree of the graph is built. For clustering, the tree is separated into sub-trees by removing some edges. Each sub-tree is a simply connected domain on the original image. In order to improve algorithm speed and reduce memory usage a greedy algorithm is used to build this minimum spanning tree for the graph. Edges to be removed are searched on the graph representing the length of an added edge versus a sequence number of its adding to the tree in the greedy algorithm. The desired edges are detected as maximums on the graphic. This search is based on assumption that transition to an adjacent cluster leads to connection of longer edge in comparison with edges within a cluster. Segmentation into clusters is iterative. At each step the bigger clusters are divided into smaller ones. It means that hierarchy of clusters can be built. A computer experiment was carried out using different images.The suggested method has no disadvantages of the most common method of k-means and allows dividing domains with different colors but the same intensity. Therewith there is no need to specify a number of clusters. Instead, it is necessary to choose a segmentation depth then a number of clusters will be automatically defined. The suggested method has no disadvantages of detection of image edges either. It is sufficient to find one point of image edge to separate two domains.A distinctive feature of

  15. Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm.

    Science.gov (United States)

    Kim, Jung Jin; Nam, Jimin; Jang, In Gwun

    2018-02-01

    Automated segmentation with high accuracy and speed is a prerequisite for FEA-based quantitative assessment with a large population. However, hip joint segmentation has remained challenging due to a narrow articular cartilage and thin cortical bone with a marked interindividual variance. To overcome this challenge, this paper proposes a fully automated segmentation method for a hip joint that uses the complementary characteristics between the thresholding technique and the watershed algorithm. Using the golden section method and load path algorithm, the proposed method first determines the patient-specific optimal threshold value that enables reliably separating a femur from a pelvis while removing cortical and trabecular bone in the femur at the minimum. This provides regional information on the femur. The watershed algorithm is then used to obtain boundary information on the femur. The proximal femur can be extracted by merging the complementary information on a target image. For eight CT images, compared with the manual segmentation and other segmentation methods, the proposed method offers a high accuracy in terms of the dice overlap coefficient (97.24 ± 0.44%) and average surface distance (0.36 ± 0.07 mm) within a fast timeframe in terms of processing time per slice (1.25 ± 0.27 s). The proposed method also delivers structural behavior which is close to that of the manual segmentation with a small mean of average relative errors of the risk factor (4.99%). The segmentation results show that, without the aid of a prerequisite dataset and users' manual intervention, the proposed method can segment a hip joint as fast as the simplified Kang (SK)-based automated segmentation, while maintaining the segmentation accuracy at a similar level of the snake-based semi-automated segmentation. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Practical Constraint K-Segment Principal Curve Algorithms for Generating Railway GPS Digital Map

    Directory of Open Access Journals (Sweden)

    Dewang Chen

    2013-01-01

    Full Text Available In order to obtain a decent trade-off between the low-cost, low-accuracy Global Positioning System (GPS receivers and the requirements of high-precision digital maps for modern railways, using the concept of constraint K-segment principal curves (CKPCS and the expert knowledge on railways, we propose three practical CKPCS generation algorithms with reduced computational complexity, and thereafter more suitable for engineering applications. The three algorithms are named ALLopt, MPMopt, and DCopt, in which ALLopt exploits global optimization and MPMopt and DCopt apply local optimization with different initial solutions. We compare the three practical algorithms according to their performance on average projection error, stability, and the fitness for simple and complex simulated trajectories with noise data. It is found that ALLopt only works well for simple curves and small data sets. The other two algorithms can work better for complex curves and large data sets. Moreover, MPMopt runs faster than DCopt, but DCopt can work better for some curves with cross points. The three algorithms are also applied in generating GPS digital maps for two railway GPS data sets measured in Qinghai-Tibet Railway (QTR. Similar results like the ones in synthetic data are obtained. Because the trajectory of a railway is relatively simple and straight, we conclude that MPMopt works best according to the comprehensive considerations on the speed of computation and the quality of generated CKPCS. MPMopt can be used to obtain some key points to represent a large amount of GPS data. Hence, it can greatly reduce the data storage requirements and increase the positioning speed for real-time digital map applications.

  17. Automatic segmentation of thermal images of diabetic-at-risk feet using the snakes algorithm

    Science.gov (United States)

    Etehadtavakol, Mahnaz; Ng, E. Y. K.; Kaabouch, Naima

    2017-11-01

    Diabetes is a disease with multi-systemic problems. It is a leading cause of death, medical costs, and loss of productivity. Foot ulcers are one generally known problem of uncontrolled diabetes that can lead to amputation signs of foot ulcers are not always obvious. Sometimes, symptoms won't even show up until ulcer is infected. Hence, identification of pre-ulceration of the plantar surface of the foot in diabetics is beneficial. Thermography has the potential to identify regions of the plantar with no evidence of ulcer but yet risk. Thermography is a technique that is safe, easy, non-invasive, with no contact, and repeatable. In this study, 59 thermographic images of the plantar foot of patients with diabetic neuropathy are implemented using the snakes algorithm to separate two feet from background automatically and separating the right foot from the left on each image. The snakes algorithm both separates the right and left foot into segmented different clusters according to their temperatures. The hottest regions will have the highest risk of ulceration for each foot. This algorithm also worked perfectly for all the current images.

  18. A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans.

    Science.gov (United States)

    Girish, G N; Anima, V A; Kothari, Abhishek R; Sudeep, P V; Roychowdhury, Sohini; Rajan, Jeny

    2018-01-01

    Retinal cysts are formed by accumulation of fluid in the retina caused by leakages from inflammation or vitreous fractures. Analysis of the retinal cystic spaces holds significance in detection and treatment of several ocular diseases like age-related macular degeneration, diabetic macular edema etc. Thus, segmentation of intra-retinal cysts and quantification of cystic spaces are vital for retinal pathology and severity detection. In the recent years, automated segmentation of intra-retinal cysts using optical coherence tomography B-scans has gained significant importance in the field of retinal image analysis. The objective of this paper is to compare different intra-retinal cyst segmentation algorithms for comparative analysis and benchmarking purposes. In this work, we employ a modular approach for standardizing the different segmentation algorithms. Further, we analyze the variations in automated cyst segmentation performances and method scalability across image acquisition systems by using the publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge). Several key automated methods are comparatively analyzed using quantitative and qualitative experiments. Our analysis demonstrates the significance of variations in signal-to-noise ratio (SNR), retinal layer morphology and post-processing steps on the automated cyst segmentation processes. This benchmarking study provides insights towards the scalability of automated processes across vendor-specific imaging modalities to provide guidance for retinal pathology diagnostics and treatment processes. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

    Energy Technology Data Exchange (ETDEWEB)

    Polan, D [University of Michigan, Ann Arbor, MI (United States); Brady, S; Kaufman, R [St. Jude Children’s Research Hospital, Memphis, TN (United States)

    2016-06-15

    Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT

  20. Technical aspects of the process of segmentation and packaging of the reactor vessel of Jose Cabrera NPP; Aspectos tecnicos del proceso de segmentacion y embalaje de la vasija del reactor de la central nuclear Jose Cabrera

    Energy Technology Data Exchange (ETDEWEB)

    Valdivieso, J. M.; Garcia Castro, R.

    2015-07-01

    Westinghouse is carrying out the segmentation of the reactor pressure vessel (RPV) within the framework of the Dismantling and Decommissioning Project of the Jose Cabrera NPP. The final concept is based on the comprehensive Westinghouse experience in the field of LWR pressure vessel and internals segmentation, and particularly in previous reactor internals segmentation project for Jose Cabrera NPP. This article shows the development of all the activities included: cutting method selection, preparatory works, cutting activities, waste characterization and packaging activities. (Author)

  1. A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara

    2013-01-01

    In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...... segmentation could be avoided. Indeed, the bilateral filtering, as a preprocessing step, eliminates the unnecessary details of the image and results in a few numbers of pixons, faster performance and more robustness against unwanted environmental noises. Then, the obtained pixonal image is segmented using...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques....

  2. Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

    Directory of Open Access Journals (Sweden)

    Margarita Gamarra

    2017-08-01

    Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

  3. A Fast Semiautomatic Algorithm for Centerline-Based Vocal Tract Segmentation

    Directory of Open Access Journals (Sweden)

    Anton A. Poznyakovskiy

    2015-01-01

    Full Text Available Vocal tract morphology is an important factor in voice production. Its analysis has potential implications for educational matters as well as medical issues like voice therapy. The knowledge of the complex adjustments in the spatial geometry of the vocal tract during phonation is still limited. For a major part, this is due to difficulties in acquiring geometry data of the vocal tract in the process of voice production. In this study, a centerline-based segmentation method using active contours was introduced to extract the geometry data of the vocal tract obtained with MRI during sustained vowel phonation. The applied semiautomatic algorithm was found to be time- and interaction-efficient and allowed performing various three-dimensional measurements on the resulting model. The method is suitable for an improved detailed analysis of the vocal tract morphology during speech or singing which might give some insights into the underlying mechanical processes.

  4. Genetic algorithms as a useful tool for trabecular and cortical bone segmentation.

    Science.gov (United States)

    Janc, K; Tarasiuk, J; Bonnet, A S; Lipinski, P

    2013-07-01

    The aim of this study was to find a semi-automatic method of bone segmentation on the basis of computed tomography (CT) scan series in order to recreate corresponding 3D objects. So, it was crucial for the segmentation to be smooth between adjacent scans. The concept of graphics pipeline computing was used, i.e. simple graphics filters such as threshold or gradient were processed in a manner that the output of one filter became the input of the second one resulting in so called pipeline. The input of the entire stream was the CT scan and the output corresponded to the binary mask showing where a given tissue is located in the input image. In this approach the main task consists in finding the suitable sequence, types and parameters of graphics filters building the pipeline. Because of the high number of desired parameters (in our case 96), it was decided to use a slightly modified genetic algorithm. To determine fitness value, the mask obtained from the parameters found through genetic algorithms (GA) was compared with those manually prepared. The numerical value corresponding to such a comparison has been defined by Dice's coefficient. Preparation of reference masks for a few scans among the several hundreds of them was the only action done manually by a human expert. Using this method, very good results both for trabecular and cortical bones were obtained. It has to be emphasized that as no real border exists between these two bone types, the manually prepared reference masks were quite conventional and therefore charged with errors. As GA is a non-deterministic method, the present work also contains a statistical analysis of the relations existing between various GA parameters and fitness function. Finally the best sets of the GA parameters are proposed. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  5. Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Chengquan Zhou

    2018-02-01

    Full Text Available To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram–Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT, and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.

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

    Science.gov (United States)

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

    2016-07-07

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

  7. A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

    Directory of Open Access Journals (Sweden)

    Elizabeth M Sweeney

    Full Text Available Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS lesion segmentation in structural magnetic resonance imaging (MRI. We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w, T2-weighted (T2-w and fluid-attenuated inversion recovery (FLAIR MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

  8. SU-E-J-142: Performance Study of Automatic Image-Segmentation Algorithms in Motion Tracking Via MR-IGRT

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Y; Olsen, J.; Parikh, P.; Noel, C; Wooten, H; Du, D; Mutic, S; Hu, Y [Washington University, St. Louis, MO (United States); Kawrakow, I; Dempsey, J [Washington University, St. Louis, MO (United States); ViewRay Co., Oakwood Village, OH (United States)

    2014-06-01

    Purpose: Evaluate commonly used segmentation algorithms on a commercially available real-time MR image guided radiotherapy (MR-IGRT) system (ViewRay), compare the strengths and weaknesses of each method, with the purpose of improving motion tracking for more accurate radiotherapy. Methods: MR motion images of bladder, kidney, duodenum, and liver tumor were acquired for three patients using a commercial on-board MR imaging system and an imaging protocol used during MR-IGRT. A series of 40 frames were selected for each case to cover at least 3 respiratory cycles. Thresholding, Canny edge detection, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE), along with the ViewRay treatment planning and delivery system (TPDS) were included in the comparisons. To evaluate the segmentation results, an expert manual contouring of the organs or tumor from a physician was used as a ground-truth. Metrics value of sensitivity, specificity, Jaccard similarity, and Dice coefficient were computed for comparison. Results: In the segmentation of single image frame, all methods successfully segmented the bladder and kidney, but only FKM, KHM and TPDS were able to segment the liver tumor and the duodenum. For segmenting motion image series, the TPDS method had the highest sensitivity, Jarccard, and Dice coefficients in segmenting bladder and kidney, while FKM and KHM had a slightly higher specificity. A similar pattern was observed when segmenting the liver tumor and the duodenum. The Canny method is not suitable for consistently segmenting motion frames in an automated process, while thresholding and RD-LSE cannot consistently segment a liver tumor and the duodenum. Conclusion: The study compared six different segmentation methods and showed the effectiveness of the ViewRay TPDS algorithm in segmenting motion images during MR-IGRT. Future studies include a selection of conformal segmentation methods based on image/organ-specific information

  9. Segmentation of Lung Structures in CT

    DEFF Research Database (Denmark)

    Lo, Pechin Chien Pau

    This thesis proposes and evaluates new algorithms for segmenting various lung structures in computed tomography (CT) images, namely the lungs, airway trees and vessel trees. The main objective of these algorithms is to facilitate a better platform for studying Chronic Obstructive Pulmonary Disease....... Two approaches for extracting the airway tree using the voxel classification appearance model are proposed: a vessel guided approach and a locally optimal paths approach. The vessel guided approach exploits the fact that all airways are accompanied by arteries of similar orientation...... in combination with a multiscale vessel enhancement filter for the extraction of vessel trees in CT. It was shown that the locally optimal path approach is capable of extracting a better connected vessel tree and extract more of the small peripheral vessels in comparison to applying a threshold on the output...

  10. Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms.

    Science.gov (United States)

    Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly A

    2013-02-15

    High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. Copyright © 2012 Elsevier B.V. All rights reserved.

  11. A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using a DNA Genetic Algorithm for Magnetic Resonance Image Segmentation

    Directory of Open Access Journals (Sweden)

    Wenke Zang

    2017-10-01

    Full Text Available MRI segmentation is critically important for clinical study and diagnosis. Existing methods based on soft clustering have several drawbacks, including low accuracy in the presence of image noise and artifacts, and high computational cost. In this paper, we introduce a new formulation of the MRI segmentation problem as a kernel-based intuitionistic fuzzy C-means (KIFCM clustering problem and propose a new DNA-based genetic algorithm to obtain the optimal KIFCM clustering. While this algorithm searches the solution space for the optimal model parameters, it also obtains the optimal clustering, therefore the optimal MRI segmentation. We perform empirical study by comparing our method with six state-of-the-art soft clustering methods using a set of UCI (University of California, Irvine datasets and a set of synthetic and clinic MRI datasets. The preliminary results show that our method outperforms other methods in both the clustering metrics and the computational efficiency.

  12. DIALIGN-T: An improved algorithm for segment-based multiple sequence alignment

    Directory of Open Access Journals (Sweden)

    Kaufmann Michael

    2005-03-01

    Full Text Available Abstract Background We present a complete re-implementation of the segment-based approach to multiple protein alignment that contains a number of improvements compared to the previous version 2.2 of DIALIGN. This previous version is superior to Needleman-Wunsch-based multi-alignment programs on locally related sequence sets. However, it is often outperformed by these methods on data sets with global but weak similarity at the primary-sequence level. Results In the present paper, we discuss strengths and weaknesses of DIALIGN in view of the underlying objective function. Based on these results, we propose several heuristics to improve the segment-based alignment approach. For pairwise alignment, we implemented a fragment-chaining algorithm that favours chains of low-scoring local alignments over isolated high-scoring fragments. For multiple alignment, we use an improved greedy procedure that is less sensitive to spurious local sequence similarities. To evaluate our method on globally related protein families, we used the well-known database BAliBASE. For benchmarking tests on locally related sequences, we created a new reference database called IRMBASE which consists of simulated conserved motifs implanted into non-related random sequences. Conclusion On BAliBASE, our new program performs significantly better than the previous version of DIALIGN and is comparable to the standard global aligner CLUSTAL W, though it is outperformed by some newly developed programs that focus on global alignment. On the locally related test sets in IRMBASE, our method outperforms all other programs that we evaluated.

  13. Vessel discoloration detection in malarial retinopathy

    Science.gov (United States)

    Agurto, C.; Nemeth, S.; Barriga, S.; Soliz, P.; MacCormick, I.; Taylor, T.; Harding, S.; Lewallen, S.; Joshi, V.

    2016-03-01

    Cerebral malaria (CM) is a life-threatening clinical syndrome associated with malarial infection. It affects approximately 200 million people, mostly sub-Saharan African children under five years of age. Malarial retinopathy (MR) is a condition in which lesions such as whitening and vessel discoloration that are highly specific to CM appear in the retina. Other unrelated diseases can present with symptoms similar to CM, therefore the exact nature of the clinical symptoms must be ascertained in order to avoid misdiagnosis, which can lead to inappropriate treatment and, potentially, death. In this paper we outline the first system to detect the presence of discolored vessels associated with MR as a means to improve the CM diagnosis. We modified and improved our previous vessel segmentation algorithm by incorporating the `a' channel of the CIELab color space and noise reduction. We then divided the segmented vasculature into vessel segments and extracted features at the wall and in the centerline of the segment. Finally, we used a regression classifier to sort the segments into discolored and not-discolored vessel classes. By counting the abnormal vessel segments in each image, we were able to divide the analyzed images into two groups: normal and presence of vessel discoloration due to MR. We achieved an accuracy of 85% with sensitivity of 94% and specificity of 67%. In clinical practice, this algorithm would be combined with other MR retinal pathology detection algorithms. Therefore, a high specificity can be achieved. By choosing a different operating point in the ROC curve, our system achieved sensitivity of 67% with specificity of 100%.

  14. Optimization of automated segmentation of monkeypox virus-induced lung lesions from normal lung CT images using hard C-means algorithm

    Science.gov (United States)

    Castro, Marcelo A.; Thomasson, David; Avila, Nilo A.; Hufton, Jennifer; Senseney, Justin; Johnson, Reed F.; Dyall, Julie

    2013-03-01

    Monkeypox virus is an emerging zoonotic pathogen that results in up to 10% mortality in humans. Knowledge of clinical manifestations and temporal progression of monkeypox disease is limited to data collected from rare outbreaks in remote regions of Central and West Africa. Clinical observations show that monkeypox infection resembles variola infection. Given the limited capability to study monkeypox disease in humans, characterization of the disease in animal models is required. A previous work focused on the identification of inflammatory patterns using PET/CT image modality in two non-human primates previously inoculated with the virus. In this work we extended techniques used in computer-aided detection of lung tumors to identify inflammatory lesions from monkeypox virus infection and their progression using CT images. Accurate estimation of partial volumes of lung lesions via segmentation is difficult because of poor discrimination between blood vessels, diseased regions, and outer structures. We used hard C-means algorithm in conjunction with landmark based registration to estimate the extent of monkeypox virus induced disease before inoculation and after disease progression. Automated estimation is in close agreement with manual segmentation.

  15. Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

    Directory of Open Access Journals (Sweden)

    Viswanath Satish

    2012-02-01

    of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis.

  16. Improving Reliability of pQCT-Derived Muscle Area and Density Measures Using a Watershed Algorithm for Muscle and Fat Segmentation

    OpenAIRE

    Wong, Andy Kin On; Hummel, Kayla; Moore, Cameron; Beattie, Karen A.; Shaker, Sami; Craven, B. Catharine; Adachi, Jonathan D.; Papaioannou, Alexandra; Giangregorio, Lora

    2014-01-01

    In peripheral quantitative computed tomography scans of the calf muscles, segmentation of muscles from subcutaneous fat is challenged by muscle fat infiltration. Threshold-based edge detection segmentation by manufacturer software fails when muscle boundaries are not smooth. This study compared the test-retest precision error for muscle-fat segmentation using the threshold-based edge detection method vs manual segmentation guided by the watershed algorithm. Three clinical populations were inv...

  17. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images.

    Science.gov (United States)

    Karim, Rashed; Bhagirath, Pranav; Claus, Piet; James Housden, R; Chen, Zhong; Karimaghaloo, Zahra; Sohn, Hyon-Mok; Lara Rodríguez, Laura; Vera, Sergio; Albà, Xènia; Hennemuth, Anja; Peitgen, Heinz-Otto; Arbel, Tal; Gonzàlez Ballester, Miguel A; Frangi, Alejandro F; Götte, Marco; Razavi, Reza; Schaeffter, Tobias; Rhode, Kawal

    2016-05-01

    Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  18. An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model.

    Science.gov (United States)

    Jahromi, Mahdi Kazemian; Kafieh, Raheleh; Rabbani, Hossein; Dehnavi, Alireza Mehri; Peyman, Alireza; Hajizadeh, Fedra; Ommani, Mohammadreza

    2014-07-01

    Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time-consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on optical coherence tomography (OCT) images. For this purpose, we apply the GMM method in two consequent steps. In the first step, the GMM is applied on the original image to localize the first and the last boundaries. In the next step, gradient response of a contrast enhanced version of the image is fed into another GMM algorithm to obtain a more clear result around the second boundary. Finally, the first boundary is traced toward down to localize the exact location of the second boundary. We tested the performance of the algorithm on images taken from a Heidelberg OCT imaging system. To evaluate our approach, the automatic boundary results are compared with the boundaries that have been segmented manually by two corneal specialists. The quantitative results show that the proposed method segments the desired boundaries with a great accuracy. Unsigned mean errors between the results of the proposed method and the manual segmentation are 0.332, 0.421, and 0.795 for detection of epithelium, Bowman, and endothelium boundaries, respectively. Unsigned mean errors of the inter-observer between two corneal specialists have also a comparable unsigned value of 0.330, 0.398, and 0.534, respectively.

  19. Interleaved segment correction achieves higher improvement factors in using genetic algorithm to optimize light focusing through scattering media

    Science.gov (United States)

    Li, Runze; Peng, Tong; Liang, Yansheng; Yang, Yanlong; Yao, Baoli; Yu, Xianghua; Min, Junwei; Lei, Ming; Yan, Shaohui; Zhang, Chunmin; Ye, Tong

    2017-10-01

    Focusing and imaging through scattering media has been proved possible with high resolution wavefront shaping. A completely scrambled scattering field can be corrected by applying a correction phase mask on a phase only spatial light modulator (SLM) and thereby the focusing quality can be improved. The correction phase is often found by global searching algorithms, among which Genetic Algorithm (GA) stands out for its parallel optimization process and high performance in noisy environment. However, the convergence of GA slows down gradually with the progression of optimization, causing the improvement factor of optimization to reach a plateau eventually. In this report, we propose an interleaved segment correction (ISC) method that can significantly boost the improvement factor with the same number of iterations comparing with the conventional all segment correction method. In the ISC method, all the phase segments are divided into a number of interleaved groups; GA optimization procedures are performed individually and sequentially among each group of segments. The final correction phase mask is formed by applying correction phases of all interleaved groups together on the SLM. The ISC method has been proved significantly useful in practice because of its ability to achieve better improvement factors when noise is present in the system. We have also demonstrated that the imaging quality is improved as better correction phases are found and applied on the SLM. Additionally, the ISC method lowers the demand of dynamic ranges of detection devices. The proposed method holds potential in applications, such as high-resolution imaging in deep tissue.

  20. Real-time implementations of image segmentation algorithms on shared memory multicore architecture: a survey (Conference Presentation)

    Science.gov (United States)

    Akil, Mohamed

    2017-05-01

    The real-time processing is getting more and more important in many image processing applications. Image segmentation is one of the most fundamental tasks image analysis. As a consequence, many different approaches for image segmentation have been proposed. The watershed transform is a well-known image segmentation tool. The watershed transform is a very data intensive task. To achieve acceleration and obtain real-time processing of watershed algorithms, parallel architectures and programming models for multicore computing have been developed. This paper focuses on the survey of the approaches for parallel implementation of sequential watershed algorithms on multicore general purpose CPUs: homogeneous multicore processor with shared memory. To achieve an efficient parallel implementation, it's necessary to explore different strategies (parallelization/distribution/distributed scheduling) combined with different acceleration and optimization techniques to enhance parallelism. In this paper, we give a comparison of various parallelization of sequential watershed algorithms on shared memory multicore architecture. We analyze the performance measurements of each parallel implementation and the impact of the different sources of overhead on the performance of the parallel implementations. In this comparison study, we also discuss the advantages and disadvantages of the parallel programming models. Thus, we compare the OpenMP (an application programming interface for multi-Processing) with Ptheads (POSIX Threads) to illustrate the impact of each parallel programming model on the performance of the parallel implementations.

  1. A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance.

    Science.gov (United States)

    Zhang, Yue; Zou, Huanxin; Luo, Tiancheng; Qin, Xianxiang; Zhou, Shilin; Ji, Kefeng

    2016-10-13

    The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR) images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR) and Airborne Synthetic Aperture Radar (AirSAR) data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods.

  2. A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance

    Directory of Open Access Journals (Sweden)

    Yue Zhang

    2016-10-01

    Full Text Available The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR and Airborne Synthetic Aperture Radar (AirSAR data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods.

  3. Validation of Point Clouds Segmentation Algorithms Through Their Application to Several Case Studies for Indoor Building Modelling

    Science.gov (United States)

    Macher, H.; Landes, T.; Grussenmeyer, P.

    2016-06-01

    Laser scanners are widely used for the modelling of existing buildings and particularly in the creation process of as-built BIM (Building Information Modelling). However, the generation of as-built BIM from point clouds involves mainly manual steps and it is consequently time consuming and error-prone. Along the path to automation, a three steps segmentation approach has been developed. This approach is composed of two phases: a segmentation into sub-spaces namely floors and rooms and a plane segmentation combined with the identification of building elements. In order to assess and validate the developed approach, different case studies are considered. Indeed, it is essential to apply algorithms to several datasets and not to develop algorithms with a unique dataset which could influence the development with its particularities. Indoor point clouds of different types of buildings will be used as input for the developed algorithms, going from an individual house of almost one hundred square meters to larger buildings of several thousand square meters. Datasets provide various space configurations and present numerous different occluding objects as for example desks, computer equipments, home furnishings and even wine barrels. For each dataset, the results will be illustrated. The analysis of the results will provide an insight into the transferability of the developed approach for the indoor modelling of several types of buildings.

  4. An Efficient Correction Algorithm for Eliminating Image Misalignment Effects on Co-Phasing Measurement Accuracy for Segmented Active Optics Systems.

    Directory of Open Access Journals (Sweden)

    Dan Yue

    Full Text Available The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality.

  5. An Efficient Correction Algorithm for Eliminating Image Misalignment Effects on Co-Phasing Measurement Accuracy for Segmented Active Optics Systems

    Science.gov (United States)

    Yue, Dan; Xu, Shuyan; Nie, Haitao; Wang, Zongyang

    2016-01-01

    The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD) algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF) of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality. PMID:26934045

  6. Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms.

    Science.gov (United States)

    Wiesmann, Veit; Bergler, Matthias; Palmisano, Ralf; Prinzen, Martin; Franz, Daniela; Wittenberg, Thomas

    2017-03-18

    Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs.

  7. Quantification of the myocardial area at risk using coronary CT angiography and Voronoi algorithm-based myocardial segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Kurata, Akira; Kono, Atsushi; Coenen, Adriaan; Saru-Chelu, Raluca G.; Krestin, Gabriel P. [Erasmus University Medical Center, Department of Radiology, Rotterdam (Netherlands); Sakamoto, Tsuyoshi [AZE inc, Development Division, Chiyoda, Tokyo (Japan); Kido, Teruhito; Mochizuki, Teruhito [Ehime University Graduate School of Medicine, Department of Radiology, Toon, Ehime (Japan); Higashino, Hiroshi [Yotsuba Circulation Clinic, Department of Radiology, Matsuyama, Ehime (Japan); Abe, Mitsunori [Yotsuba Circulation Clinic, Department of Cardiology, Matsuyama, Ehime (Japan); Feyter, Pim J. de; Nieman, Koen [Erasmus University Medical Center, Department of Radiology, Rotterdam (Netherlands); Erasmus University Medical Center, Department of Cardiology, Rotterdam (Netherlands)

    2015-01-15

    The purpose of this study was to estimate the myocardial area at risk (MAAR) using coronary computed tomography angiography (CTA) and Voronoi algorithm-based myocardial segmentation in comparison with single-photon emission computed tomography (SPECT). Thirty-four patients with coronary artery disease underwent 128-slice coronary CTA, stress/rest thallium-201 SPECT, and coronary angiography (CAG). CTA-based MAAR was defined as the sum of all CAG stenosis (>50 %) related territories (the ratio of the left ventricular volume). Using automated quantification software (17-segment model, 5-point scale), SPECT-based MAAR was defined as the number of segments with a score above zero as compared to the total 17 segments by summed stress score (SSS), difference (SDS) score map, and comprehensive SPECT interpretation with either SSS or SDS best correlating CAG findings (SSS/SDS). Results were compared using Pearson's correlation coefficient. Forty-nine stenoses were observed in 102 major coronary territories. Mean value of CTA-based MAAR was 28.3 ± 14.0 %. SSS-based, SDS-based, and SSS/SDS-based MAAR was 30.1 ± 6.1 %, 20.1 ± 15.8 %, and 26.8 ± 15.7 %, respectively. CTA-based MAAR was significantly related to SPECT-based MAAR (r = 0.531 for SSS; r = 0.494 for SDS; r = 0.814 for SSS/SDS; P < 0.05 in each). CTA-based Voronoi algorithm myocardial segmentation reliably quantifies SPECT-based MAAR. (orig.)

  8. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Xiao Ling

    2016-08-01

    Full Text Available This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC, which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3, Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

  9. Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms

    OpenAIRE

    Wiesmann, Veit; Bergler, Matthias; Palmisano, Ralf; Prinzen, Martin; Franz, Daniela; Wittenberg, Thomas

    2017-01-01

    Background Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which ...

  10. Hemodynamic segmentation of brain perfusion images with delay and dispersion effects using an expectation-maximization algorithm.

    Directory of Open Access Journals (Sweden)

    Chia-Feng Lu

    Full Text Available Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified.

  11. WE-E-213CD-05: A Non-Rigid Image Registration Algorithm That Accommodates Organ Segmentation Error.

    Science.gov (United States)

    Zhang, C; Christensen, G E; Kurtek, S; Srivastava, A; Weiss, E; Murphy, M J; Williamson, J F

    2012-06-01

    To introduce a new deformable image registration algorithm based on surface matching that accommodates organ delineation error in daily Cone-beam CT images based on a priori knowledge of inter-observer segmentation uncertainty. The dataset includes four prostate cancer patients who underwent primary external beam radiotherapy and had tumors that were confined to the prostate. All imaging was performed without intravenous contrast. Organ surface segmentation errors in a multiple observer-contouring study on the pelvic organs in Fan-beam CT (FBCT) and Cone-beam CT (CBCT) were estimated from the training dataset. A novel deformable image registration algorithm is presented where the organ surface matching is penalized by this error. Portions of the organ surface that are delineated reliably are used to guide the registration whereas the portions that are highly uncertain are ignored. This approach reduces the impact of delineation errors in CBCT. An evaluation experiment compares three algorithms, namely intensity-only registration (INT), equally-weighted surface and image registration (EWSIR) and the proposed uncertainty- weighted surface and image registration. The surface dissimilarity was reduced from 0.172 to 0.134, 0.043 and 0.044 respectively after registration. The Jacobian of the transformation found by the proposed method was closer to one than that of EWSIR in the prostate. In prostate external-beam radiotherapy, slice-by-slice 2D manual contouring has variable spatial accuracy. For deformable image registration methods that match segmented surfaces, regions of high inaccuracy can misguide the registration. In contrast to the image registration methods where the FBCT and CBCT surfaces (or other features) are assumed to be exact, our method takes this uncertainty into account. Preliminary results show an improved registration performance suggesting a potential use in IGRT. This work was supported by National Cancer Institute Grant No. P01 CA 116602. © 2012

  12. An Improved Algorithm for the Piecewise-Smooth Mumford and Shah Model in Image Segmentation

    Directory of Open Access Journals (Sweden)

    2006-01-01

    Full Text Available An improved algorithm for the piecewise-smooth Mumford and Shah functional is presented. Compared to the previous work of Chan and Vese, and Choi et al, extensions of the key functions u ± are replaced by updating the level set function based on an artificial image that is composed of the diffused image and the original image. The low convergence problem of the classical algorithm is efficiently solved in the proposed approach. The resulting algorithm has also been demonstrated by several cases.

  13. An Improved Algorithm for the Piecewise-Smooth Mumford and Shah Model in Image Segmentation

    Directory of Open Access Journals (Sweden)

    Zhang Yingjie

    2006-01-01

    Full Text Available An improved algorithm for the piecewise-smooth Mumford and Shah functional is presented. Compared to the previous work of Chan and Vese, and Choi et al., extensions of the key functions are replaced by updating the level set function based on an artificial image that is composed of the diffused image and the original image. The low convergence problem of the classical algorithm is efficiently solved in the proposed approach. The resulting algorithm has also been demonstrated by several cases.

  14. An Improved Algorithm for the Piecewise-Smooth Mumford and Shah Model in Image Segmentation

    Directory of Open Access Journals (Sweden)

    Yingjie Zhang

    2006-04-01

    Full Text Available An improved algorithm for the piecewise-smooth Mumford and Shah functional is presented. Compared to the previous work of Chan and Vese, and Choi et al., extensions of the key functions u± are replaced by updating the level set function based on an artificial image that is composed of the diffused image and the original image. The low convergence problem of the classical algorithm is efficiently solved in the proposed approach. The resulting algorithm has also been demonstrated by several cases.

  15. Layer stacking: A novel algorithm for individual forest tree segmentation from LiDAR point clouds

    Science.gov (United States)

    Elias Ayrey; Shawn Fraver; John A. Kershaw; Laura S. Kenefic; Daniel Hayes; Aaron R. Weiskittel; Brian E. Roth

    2017-01-01

    As light detection and ranging (LiDAR) technology advances, it has become common for datasets to be acquired at a point density high enough to capture structural information from individual trees. To process these data, an automatic method of isolating individual trees from a LiDAR point cloud is required. Traditional methods for segmenting trees attempt to isolate...

  16. Comparison of segmentation algorithms for cow contour extraction from natural barn background in side view images

    NARCIS (Netherlands)

    Hertem, van T.; Alchanatis, V.; Antler, A.; Maltz, E.; Halachmi, I.; Schlageter Tello, A.A.; Lokhorst, C.; Viazzi, S.; Romanini, C.E.B.; Pluk, A.; Bahr, C.; Berckmans, D.

    2013-01-01

    Computer vision techniques are a means to extract individual animal information such as weight, activity and calving time in intensive farming. Automatic detection requires adequate image pre-processing such as segmentation to precisely distinguish the animal from its background. For some analyses

  17. Evaluation of Image Segmentation and Object Recognition Algorithms for Image Parsing

    Science.gov (United States)

    2013-09-01

    results for precision, recall, and F-measure indicate that the best approach to use for image segmentation is Sobel edge detection and to use Canny...or Sobel for object recognition. The process for this report would not work for a warfighter or analyst. It has poor performance. Additionally...1 2.1. Sobel Edge Detection

  18. THE SEGMENTATION OF A TEXT LINE FOR A HANDWRITTEN UNCONSTRAINED DOCUMENT USING THINING ALGORITHM

    NARCIS (Netherlands)

    Tsuruoka, S.; Adachi, Y.; Yoshikawa, T.

    2004-01-01

    For printed documents, the projection analysis of black pixels is widely used for the segmentation of a text line. However, for handwritten documents, we think that the projection analysis is not appropriate, as the separating border line of a text line is not a straight line on a paper with no

  19. The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer.

    Science.gov (United States)

    Bashir, Usman; Azad, Gurdip; Siddique, Muhammad Musib; Dhillon, Saana; Patel, Nikheel; Bassett, Paul; Landau, David; Goh, Vicky; Cook, Gary

    2017-12-01

    Measures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18F-FDG PET/CT images. Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC). 40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85-0.92) compared with FLAB (median ICC 0.83; IQR 0.77-0.86) and FH (median ICC 0.77; IQR 0.7-0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs. Compared with both FLAB

  20. New segmentation-based tone mapping algorithm for high dynamic range image

    Science.gov (United States)

    Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

    2017-07-01

    The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.

  1. Improving the reproducibility of MR-derived left ventricular volume and function measurements with a semi-automatic threshold-based segmentation algorithm

    NARCIS (Netherlands)

    Jaspers, Karolien; Freling, Hendrik G.; van Wijk, Kees; Romijn, Elisabeth I.; Greuter, Marcel J. W.; Willems, Tineke P.

    To validate a novel semi-automatic segmentation algorithm for MR-derived volume and function measurements by comparing it with the standard method of manual contour tracing. The new algorithms excludes papillary muscles and trabeculae from the blood pool, while the manual approach includes these

  2. Heparin pre-treatment in patients with ST-segment elevation myocardial infarction and the risk of intracoronary thrombus and total vessel occlusion. Insights from the TASTE trial.

    Science.gov (United States)

    Karlsson, Sofia; Andell, Pontus; Mohammad, Moman A; Koul, Sasha; Olivecrona, Göran K; James, Stefan K; Fröbert, Ole; Erlinge, David

    2017-08-01

    Pre-treatment with unfractionated heparin is common in ST-segment elevation myocardial infarction (STEMI) protocols, but the effect on intracoronary thrombus burden is unknown. We studied the effect of heparin pre-treatment on intracoronary thrombus burden and Thrombolysis in Myocardial Infarction (TIMI) flow prior to percutaneous coronary intervention in patients with STEMI. The Thrombus Aspiration in ST-Elevation Myocardial Infarction in Scandinavia (TASTE) trial angiographically assessed intracoronary thrombus burden and TIMI flow, prior to percutaneous coronary intervention, in patients with STEMI. In this observational sub-study, patients pre-treated with heparin were compared with patients not pre-treated with heparin. Primary end points were a visible intracoronary thrombus and total vessel occlusion prior to percutaneous coronary intervention. Secondary end points were in-hospital bleeding, in-hospital stroke and 30-day all-cause mortality. Heparin pre-treatment was administered in 2898 out of 7144 patients (41.0%). Patients pre-treated with heparin less often presented with an intracoronary thrombus (61.3% vs. 66.0%, ppre-treatment was independently associated with a reduced risk of intracoronary thrombus (odds ratio (OR) 0.73, 95% confidence interval (CI)=0.65-0.83) and total vessel occlusion (OR 0.64, 95% CI=0.56-0.73), prior to percutaneous coronary intervention. There were no significant differences in secondary end points of in-hospital bleeding (OR 0.84, 95% CI=0.55-1.27), in-hospital stroke (OR 1.17, 95% CI=0.48-2.82) or 30-day all-cause mortality (hazard ratio 0.88, 95% CI=0.60-1.30). Heparin pre-treatment was independently associated with a lower risk of intracoronary thrombus and total vessel occlusion before percutaneous coronary intervention in patients with STEMI, without evident safety concerns, in this large multi-centre observational study.

  3. Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm.

    Science.gov (United States)

    Abdullah, Muhammad; Fraz, Muhammad Moazam; Barman, Sarah A

    2016-01-01

    Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

  4. A Semiautomatic Segmentation Algorithm for Extracting the Complete Structure of Acini from Synchrotron Micro-CT Images

    Directory of Open Access Journals (Sweden)

    Luosha Xiao

    2013-01-01

    Full Text Available Pulmonary acinus is the largest airway unit provided with alveoli where blood/gas exchange takes place. Understanding the complete structure of acinus is necessary to measure the pathway of gas exchange and to simulate various mechanical phenomena in the lungs. The usual manual segmentation of a complete acinus structure from their experimentally obtained images is difficult and extremely time-consuming, which hampers the statistical analysis. In this study, we develop a semiautomatic segmentation algorithm for extracting the complete structure of acinus from synchrotron micro-CT images of the closed chest of mouse lungs. The algorithm uses a combination of conventional binary image processing techniques based on the multiscale and hierarchical nature of lung structures. Specifically, larger structures are removed, while smaller structures are isolated from the image by repeatedly applying erosion and dilation operators in order, adjusting the parameter referencing to previously obtained morphometric data. A cluster of isolated acini belonging to the same terminal bronchiole is obtained without floating voxels. The extracted acinar models above 98% agree well with those extracted manually. The run time is drastically shortened compared with manual methods. These findings suggest that our method may be useful for taking samples used in the statistical analysis of acinus.

  5. Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation

    Directory of Open Access Journals (Sweden)

    E.A. Zanaty

    2012-03-01

    Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

  6. A sport scene images segmentation method based on edge detection algorithm

    Science.gov (United States)

    Chen, Biqing

    2011-12-01

    This paper proposes a simple, fast sports scene image segmentation method; a lot of work so far has been looking for a way to reduce the different shades of emotions in smooth area. A novel method of pretreatment, proposed the elimination of different shades feelings. Internal filling mechanism is used to change the pixels enclosed by the interest as interest pixels. For some test has achieved harvest sports scene images has been confirmed.

  7. Analyzing the medical image by using clustering algorithms through segmentation process

    Science.gov (United States)

    Kumar, Papendra; Kumar, Suresh

    2012-01-01

    Basic aim of our study is to analyze the medical image. In computer vision, segmentationRefers to the process of partitioning a digital image into multiple regions. The goal ofSegmentation is to simplify and/or change the representation of an image into something thatIs more meaningful and easier to analyze. Image segmentation is typically used to locateObjects and boundaries (lines, curves, etc.) in images.There is a lot of scope of the analysis that we have done in our project; our analysis couldBe used for the purpose of monitoring the medical image. Medical imaging refers to theTechniques and processes used to create images of the human body (or parts thereof) forClinical purposes (medical procedures seeking to reveal, diagnose or examine disease) orMedical science (including the study of normal anatomy and function).As a discipline and in its widest sense, it is part of biological imaging and incorporatesRadiology (in the wider sense), radiological sciences, endoscopy, (medical) thermography, Medical photography and microscopy (e.g. for human pathological investigations).Measurement and recording techniques which are not primarily designed to produce images.

  8. On an algorithm for analysis of the radiation patterns of dual reflector and segmented reflector antennas

    Science.gov (United States)

    Christodoulou, C. G.; Botula, A. B.; Kauffman, J. F.

    1985-01-01

    An algorithm has been developed to calculate the radiation patterns of a large reflector antenna for space applications. The algorithm treats each panel in the reflector array as an analytic surface having n-sided polygonal boundaries. The radiation fields for individual panels are computed and stored separately, and then added to yield a uniform radiation pattern for the entire reflector. Numerical methods developed to reduce the overlap of the aperture fields of adjacent panels are described in detail. Calculations of the tangential field components of the aperture plane are carried out based on the principles of geometric optics (GO). Some radiation patterns calculated with the algorithm are compared with patterns produced by other methods, together with measured data for a classical Cassegrain antenna and the calculated patterns are illustrated.

  9. Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

    Directory of Open Access Journals (Sweden)

    Vinayak S Joshi

    Full Text Available The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44% correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42%.

  10. Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework.

    Science.gov (United States)

    Bergmeir, Christoph; García Silvente, Miguel; Benítez, José Manuel

    2012-09-01

    In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  11. Segmentation algorithm of colon based on multi-slice CT colonography

    Science.gov (United States)

    Hu, Yizhong; Ahamed, Mohammed Shabbir; Takahashi, Eiji; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Suzuki, Masahiro; Iinuma, Gen; Moriyama, Noriyuki

    2012-02-01

    CT colonography is a radiology test that looks at people's large intestines(colon). CT colonography can screen many options of colon cancer. This test is used to detect polyps or cancers of the colon. CT colonography is safe and reliable. It can be used if people are too sick to undergo other forms of colon cancer screening. In our research, we proposed a method for automatic segmentation of the colon from abdominal computed Tomography (CT) images. Our multistage detection method extracted colon and spited colon into different parts according to the colon anatomy information. We found that among the five segmented parts of the colon, sigmoid (20%) and rectum (50%) are more sensitive toward polyps and masses than the other three parts. Our research focused on detecting the colon by the individual diagnosis of sigmoid and rectum. We think it would make the rapid and easy diagnosis of colon in its earlier stage and help doctors for analysis of correct position of each part and detect the colon rectal cancer much easier.

  12. Influence of different contributions of scatter and attenuation on the threshold values in contrast-based algorithms for volume segmentation.

    Science.gov (United States)

    Matheoud, Roberta; Della Monica, Patrizia; Secco, Chiara; Loi, Gianfranco; Krengli, Marco; Inglese, Eugenio; Brambilla, Marco

    2011-01-01

    The aim of this work is to evaluate the role of different amount of attenuation and scatter on FDG-PET image volume segmentation using a contrast-oriented method based on the target-to-background (TB) ratio and target dimensions. A phantom study was designed employing 3 phantom sets, which provided a clinical range of attenuation and scatter conditions, equipped with 6 spheres of different volumes (0.5-26.5 ml). The phantoms were: (1) the Hoffman 3-dimensional brain phantom, (2) a modified International Electro technical Commission (IEC) phantom with an annular ring of water bags of 3 cm thickness fit over the IEC phantom, and (3) a modified IEC phantom with an annular ring of water bags of 9 cm. The phantoms cavities were filled with a solution of FDG at 5.4 kBq/ml activity concentration, and the spheres with activity concentration ratios of about 16, 8, and 4 times the background activity concentration. Images were acquired with a Biograph 16 HI-REZ PET/CT scanner. Thresholds (TS) were determined as a percentage of the maximum intensity in the cross section area of the spheres. To reduce statistical fluctuations a nominal maximum value is calculated as the mean from all voxel > 95%. To find the TS value that yielded an area A best matching the true value, the cross section were auto-contoured in the attenuation corrected slices varying TS in step of 1%, until the area so determined differed by less than 10 mm² versus its known physical value. Multiple regression methods were used to derive an adaptive thresholding algorithm and to test its dependence on different conditions of attenuation and scatter. The errors of scatter and attenuation correction increased with increasing amount of attenuation and scatter in the phantoms. Despite these increasing inaccuracies, PET threshold segmentation algorithms resulted not influenced by the different condition of attenuation and scatter. The test of the hypothesis of coincident regression lines for the three phantoms used

  13. Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

    OpenAIRE

    Margarita Gamarra; Eduardo Zurek; Homero San-Juan

    2017-01-01

    Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysi...

  14. Blood vessel modeling for interactive simulation of interventional neuroradiology procedures.

    Science.gov (United States)

    Kerrien, E; Yureidini, A; Dequidt, J; Duriez, C; Anxionnat, R; Cotin, S

    2017-01-01

    Endovascular interventions can benefit from interactive simulation in their training phase but also during pre-operative and intra-operative phases if simulation scenarios are based on patient data. A key feature in this context is the ability to extract, from patient images, models of blood vessels that impede neither the realism nor the performance of simulation. This paper addresses both the segmentation and reconstruction of the vasculature from 3D Rotational Angiography data, and adapted to simulation: An original tracking algorithm is proposed to segment the vessel tree while filtering points extracted at the vessel surface in the vicinity of each point on the centerline; then an automatic procedure is described to reconstruct each local unstructured point set as a skeleton-based implicit surface (blobby model). The output of successively applying both algorithms is a new model of vasculature as a tree of local implicit models. The segmentation algorithm is compared with Multiple Hypothesis Testing (MHT) algorithm (Friman et al., 2010) on patient data, showing its greater ability to track blood vessels. The reconstruction algorithm is evaluated on both synthetic and patient data and demonstrate its ability to fit points with a subvoxel precision. Various tests are also reported where our model is used to simulate catheter navigation in interventional neuroradiology. An excellent realism, and much lower computational costs are reported when compared to triangular mesh surface models. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Systematic review: comparative effectiveness of adjunctive devices in patients with ST-segment elevation myocardial infarction undergoing percutaneous coronary intervention of native vessels

    Directory of Open Access Journals (Sweden)

    Sobieraj Diana M

    2011-12-01

    Full Text Available Abstract Background During percutaneous coronary intervention (PCI, dislodgement of atherothrombotic material from coronary lesions can result in distal embolization, and may lead to increased major adverse cardiovascular events (MACE and mortality. We sought to systematically review the comparative effectiveness of adjunctive devices to remove thrombi or protect against distal embolization in patients with ST-segment elevation myocardial infarction (STEMI undergoing PCI of native vessels. Methods We conducted a systematic literature search of Medline, the Cochrane Database, and Web of Science (January 1996-March 2011, http://www.clinicaltrials.gov, abstracts from major cardiology meetings, TCTMD, and CardioSource Plus. Two investigators independently screened citations and extracted data from randomized controlled trials (RCTs that compared the use of adjunctive devices plus PCI to PCI alone, evaluated patients with STEMI, enrolled a population with 95% of target lesion(s in native vessels, and reported data on at least one pre-specified outcome. Quality was graded as good, fair or poor and the strength of evidence was rated as high, moderate, low or insufficient. Disagreement was resolved through consensus. Results 37 trials met inclusion criteria. At the maximal duration of follow-up, catheter aspiration devices plus PCI significantly decreased the risk of MACE by 27% compared to PCI alone. Catheter aspiration devices also significantly increased the achievement of ST-segment resolution by 49%, myocardial blush grade of 3 (MBG-3 by 39%, and thrombolysis in myocardial infarction (TIMI 3 flow by 8%, while reducing the risk of distal embolization by 44%, no reflow by 48% and coronary dissection by 70% versus standard PCI alone. In a majority of trials, the use of catheter aspiration devices increased procedural time upon qualitative assessment. Distal filter embolic protection devices significantly increased the risk of target revascularization

  16. Comb-like optical transmission spectra generated from one-dimensional two-segment-connected two-material waveguide networks optimized by genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yu [MOE Key Laboratory of Laser Life Science and Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631 (China); Yang, Xiangbo, E-mail: xbyang@scnu.edu.cn [MOE Key Laboratory of Laser Life Science and Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631 (China); School of Physical Education and Sports Science, South China Normal University, Guangzhou 510006 (China); Lu, Jian; Zhang, Guogang [MOE Key Laboratory of Laser Life Science and Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631 (China); Liu, Chengyi Timon [School of Physical Education and Sports Science, South China Normal University, Guangzhou 510006 (China)

    2014-03-01

    In this Letter, a one-dimensional (1D) two-segment-connected two-material waveguide network (TSCTMWN) is designed to produce comb-like frequency passbands, where each waveguide segment is composed of normal and anomalous dispersion materials and the length ratio of sub-waveguide segments is optimized by genetic algorithm (GA). It is found that 66 comb-like frequency passbands are created in the second frequency unit, maximal relative width difference of which is less than 2×10{sup −5}. It may be useful for the designing of dense wavelength division multiplexings (DWDMs) and multi-channel filters, etc., and provide new applications for GA.

  17. Improving reliability of pQCT-derived muscle area and density measures using a watershed algorithm for muscle and fat segmentation.

    Science.gov (United States)

    Wong, Andy Kin On; Hummel, Kayla; Moore, Cameron; Beattie, Karen A; Shaker, Sami; Craven, B Catharine; Adachi, Jonathan D; Papaioannou, Alexandra; Giangregorio, Lora

    2015-01-01

    In peripheral quantitative computed tomography scans of the calf muscles, segmentation of muscles from subcutaneous fat is challenged by muscle fat infiltration. Threshold-based edge detection segmentation by manufacturer software fails when muscle boundaries are not smooth. This study compared the test-retest precision error for muscle-fat segmentation using the threshold-based edge detection method vs manual segmentation guided by the watershed algorithm. Three clinical populations were investigated: younger adults, older adults, and adults with spinal cord injury (SCI). The watershed segmentation method yielded lower precision error (1.18%-2.01%) and higher (pmuscle density values (70.2±9.2 mg/cm3) compared with threshold-based edge detection segmentation (1.77%-4.06% error, 67.4±10.3 mg/cm3). This was particularly true for adults with SCI (precision error improved by 1.56% and 2.64% for muscle area and density, respectively). However, both methods still provided acceptable precision with error well under 5%. Bland-Altman analyses showed that the major discrepancies between the segmentation methods were found mostly among participants with SCI where more muscle fat infiltration was present. When examining a population where fatty infiltration into muscle is expected, the watershed algorithm is recommended for muscle density and area measurement to enable the detection of smaller change effect sizes. Copyright © 2015 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Trong-Ngoc Le

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-09-15

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

  20. Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images

    Science.gov (United States)

    Akita, K.; Kuga, H.

    1982-11-01

    We propose a computer method of recognizing blood vessel networks in color ocular fundus images which are used in the mass diagnosis of adult diseases such as hypertension and diabetes. A line detection algorithm is applied to extract the blood vessels, and the skeleton patterns of them are made to analyze and describe their structures. The recognition of line segments of arteries and/or veins in the vessel networks consists of three stages. First, a few segments which satisfy a certain constraint are picked up and discriminated as arteries or veins. This is the initial labeling. Then the remaining unknown ones are labeled by utilizing the physical level knowledge. We propose two schemes for this stage : a deterministic labeling and a probabilistic relaxation labeling. Finally the label of each line segment is checked so as to minimize the total number of labeling contradictions. Some experimental results are also presented.

  1. Development and Evaluation of an Algorithm for the Computer-Assisted Segmentation of the Human Hypothalamus on 7-Tesla Magnetic Resonance Images

    Science.gov (United States)

    Schmidt, Laura; Anwander, Alfred; Strauß, Maria; Trampel, Robert; Bazin, Pierre-Louis; Möller, Harald E.; Hegerl, Ulrich; Turner, Robert; Geyer, Stefan

    2013-01-01

    Post mortem studies have shown volume changes of the hypothalamus in psychiatric patients. With 7T magnetic resonance imaging this effect can now be investigated in vivo in detail. To benefit from the sub-millimeter resolution requires an improved segmentation procedure. The traditional anatomical landmarks of the hypothalamus were refined using 7T T1-weighted magnetic resonance images. A detailed segmentation algorithm (unilateral hypothalamus) was developed for colour-coded, histogram-matched images, and evaluated in a sample of 10 subjects. Test-retest and inter-rater reliabilities were estimated in terms of intraclass-correlation coefficients (ICC) and Dice's coefficient (DC). The computer-assisted segmentation algorithm ensured test-retest reliabilities of ICC≥.97 (DC≥96.8) and inter-rater reliabilities of ICC≥.94 (DC = 95.2). There were no significant volume differences between the segmentation runs, raters, and hemispheres. The estimated volumes of the hypothalamus lie within the range of previous histological and neuroimaging results. We present a computer-assisted algorithm for the manual segmentation of the human hypothalamus using T1-weighted 7T magnetic resonance imaging. Providing very high test-retest and inter-rater reliabilities, it outperforms former procedures established at 1.5T and 3T magnetic resonance images and thus can serve as a gold standard for future automated procedures. PMID:23935821

  2. Human Vision-Motivated Algorithm Allows Consistent Retinal Vessel Classification Based on Local Color Contrast for Advancing General Diagnostic Exams.

    Science.gov (United States)

    Ivanov, Iliya V; Leitritz, Martin A; Norrenberg, Lars A; Völker, Michael; Dynowski, Marek; Ueffing, Marius; Dietter, Johannes

    2016-02-01

    Abnormalities of blood vessel anatomy, morphology, and ratio can serve as important diagnostic markers for retinal diseases such as AMD or diabetic retinopathy. Large cohort studies demand automated and quantitative image analysis of vascular abnormalities. Therefore, we developed an analytical software tool to enable automated standardized classification of blood vessels supporting clinical reading. A dataset of 61 images was collected from a total of 33 women and 8 men with a median age of 38 years. The pupils were not dilated, and images were taken after dark adaption. In contrast to current methods in which classification is based on vessel profile intensity averages, and similar to human vision, local color contrast was chosen as a discriminator to allow artery vein discrimination and arterial-venous ratio (AVR) calculation without vessel tracking. With 83% ± 1 standard error of the mean for our dataset, we achieved best classification for weighted lightness information from a combination of the red, green, and blue channels. Tested on an independent dataset, our method reached 89% correct classification, which, when benchmarked against conventional ophthalmologic classification, shows significantly improved classification scores. Our study demonstrates that vessel classification based on local color contrast can cope with inter- or intraimage lightness variability and allows consistent AVR calculation. We offer an open-source implementation of this method upon request, which can be integrated into existing tool sets and applied to general diagnostic exams.

  3. Performance of automatic image segmentation algorithms for calculating total lesion glycolysis for early response monitoring in non-small cell lung cancer patients during concomitant chemoradiotherapy

    NARCIS (Netherlands)

    Grootjans, W.; Usmanij, E.A.; Oyen, W.J.G.; Heijden, E.H.F.M. van der; Visser, E.P.; Visvikis, D.; Hatt, M.; Bussink, J.; Geus-Oei, L.F. de

    2016-01-01

    BACKGROUND AND PURPOSE: This study evaluated the use of total lesion glycolysis (TLG) determined by different automatic segmentation algorithms, for early response monitoring in non-small cell lung cancer (NSCLC) patients during concomitant chemoradiotherapy. MATERIALS AND METHODS: Twenty-seven

  4. Algorithms

    Indian Academy of Sciences (India)

    positive numbers. The word 'algorithm' was most often associated with this algorithm till 1950. It may however be pOinted out that several non-trivial algorithms such as synthetic (polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used.

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

    Science.gov (United States)

    Lee, Sang-Hoon; Lee, Sanghoon

    2015-10-01

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

  6. Mix Model of FE Method and IPSO Algorithm for Dome Shape Optimization of Articulated Pressure Vessels Considering the Effect of Non-geodesic Trajectories

    Science.gov (United States)

    Paknahad, A.; Nourani, R.

    2014-04-01

    The main essential topic for the design of articulated pressure vessels is related to the determination of the optimal meridian profile. This article, aimed to present the new model for optimum design of dome contours for filament wound articulated pressure vessels based on non-geodesic trajectories. The current model is a mix of finite element analysis and inertia weight particle swarm optimization algorithm. Geometrical limitations, stability-ensuring winding conditions and the Tsai-Wu failure criterion have been used as optimization constraints. Classical lamination theory and non-geodesic trajectories are used to analyse the field stress equations and increase the structural performance. The geometry of dome contours is defined by the B-spline curves with twenty-one points. The results, when compared to the previously published results, indicate the efficiency of the presented model in achieving superior performance of dome shape for articulated pressure vessels. Also, it is shown that the design based on non-geodesic trajectories using this model gains better response than the design by geodesics type.

  7. Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation.

    Science.gov (United States)

    Carles, Montserrat; Fechter, Tobias; Nemer, Ursula; Nanko, Norbert; Mix, Michael; Nestle, Ursula; Schaefer, Andrea

    2015-12-21

    PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. Algorithm evaluation with phantoms resulted in: (i) no statistically significant diameter differences for different targets and movements (Δφ = 0.3 ± 1.6 mm); (ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC = 0.66 ± 0.04), Positive Predictive Value (PPV  = 0.81 ± 0.06) and Sensitivity (Sen. = 0.49 ± 0.05). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (%Vol) = 40 ± 30, DSC = 0.71 ± 0.07 and PPV = 0.90 ± 0.13). High accuracy in target tracking position (ΔME) was obtained for experimental and clinical data (ΔME(exp) = 0 ± 3 mm; ΔME(clin) 0.3 ± 1.4 mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume delineation, position tracking and its robustness on highly irregular target movements

  8. Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation

    Science.gov (United States)

    Carles, Montserrat; Fechter, Tobias; Nemer, Ursula; Nanko, Norbert; Mix, Michael; Nestle, Ursula; Schaefer, Andrea

    2015-12-01

    PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. Algorithm evaluation with phantoms resulted in: (i) no statistically significant diameter differences for different targets and movements (Δ φ =0.3+/- 1.6 mm); (ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC  =  0.66+/- 0.04 ), Positive Predictive Value (PPV  =  0.81+/- 0.06 ) and Sensitivity (Sen.  =  0.49+/- 0.05 ). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (%Vol)  =  40+/- 30 , DSC  =  0.71+/- 0.07 and PPV  =  0.90+/- 0.13 ). High accuracy in target tracking position (Δ ME) was obtained for experimental and clinical data (Δ ME{{}\\text{exp}}=0+/- 3 mm; Δ ME{{}\\text{clin}}=0.3+/- 1.4 mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume

  9. Algorithms

    Indian Academy of Sciences (India)

    In the description of algorithms and programming languages, what is the role of control abstraction? • What are the inherent limitations of the algorithmic processes? In future articles in this series, we will show that these constructs are powerful and can be used to encode any algorithm. In the next article, we will discuss ...

  10. Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

    Science.gov (United States)

    2013-01-01

    Background Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions The study concludes that currently no algorithm is deemed clearly better than

  11. [The abstraction of the heart rate signals based on clipped-error LMS algorithm with variable step size of segmenting type].

    Science.gov (United States)

    Zhao, Jiyin; Tian, Baofeng; Li, Jianpo; Sui, Junling

    2006-10-01

    The adaptive filter is an effective method to dealing with such kind of non-steady random signal of the heart rate. The algorithm of clipped- error LMS algorithm with variable step size of segmenting type was proposed. We adopted auto-regression predict filter estimating the noise of heart rate signals through Doppler ultrasound sensor. We used it as the reference signal of the adaptive noise cancelling system, its result is to remove some noise of main input, it made SNR raise by about 36 dB, it realized the abstraction of the heart rate signals.

  12. SU-C-BRA-01: Interactive Auto-Segmentation for Bowel in Online Adaptive MRI-Guided Radiation Therapy by Using a Multi-Region Labeling Algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Y; Chen, I; Kashani, R; Wan, H; Maughan, N; Muccigrosso, D; Parikh, P [Washington University School of Medicine, Saint Louis, MO (United States)

    2016-06-15

    Purpose: In MRI-guided online adaptive radiation therapy, re-contouring of bowel is time-consuming and can impact the overall time of patients on table. The study aims to auto-segment bowel on volumetric MR images by using an interactive multi-region labeling algorithm. Methods: 5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18–25 fractions each, total 118 fractions) on an MRI-guided radiation therapy system with a 0.35 Tesla magnet and three Co-60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two-dimensional multi-region labeling technique based on graph cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physician’s manual contouring by using metrics of Dice coefficient and Hausdorff distance. Results: Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto-contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively. Conclusion: The preliminary results demonstrated the potential of the proposed algorithm in auto-segmenting large and small bowel on low field MRI images in MRI-guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.

  13. Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm

    Directory of Open Access Journals (Sweden)

    Yachun Pang

    2012-01-01

    Full Text Available This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs clustering and gradient vector flow (GVF snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI. Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases, the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists’ delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists’ delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.

  14. Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method.

    Science.gov (United States)

    Guo, Xiaoxi; Huang, Shaohui; Fu, Xiaozhu; Wang, Boliang; Huang, Xiaoyang

    2015-06-19

    Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshold value for final segmentation. In this paper, an accelerated strategy based on a lookup table was presented first which can reduce the connectivity scene calculation time and achieve a speed-up factor of above 2. When the computing of the fuzzy connectedness relations is finished, a threshold is needed to generate the final result. Currently the threshold is preset by users. Since different thresholds may produce different outcomes, how to determine a proper threshold is crucial. According to our analysis of the hepatic vessel structure, a watershed-like method was used to find the optimal threshold. Meanwhile, by using Ostu algorithm to calculate the parameters for affinity relations and assigning the seed with the mean value, it is able to reduce the influence on the segmentation result caused by the location of the seed and enhance the robustness of fuzzy connectedness method. Experiments based on four different datasets demonstrate the efficiency of the lookup table strategy. These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels. Comparing to a refined region-growing algorithm that has been widely used for hepatic vessel segmentation, fuzzy connectedness method has advantages in detecting vascular edge and generating more than one vessel system through the weak connectivity of the vessel ends. An improved algorithm based on fuzzy connectedness method is proposed. This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation.

  15. A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation.

    Science.gov (United States)

    da Silva Senra Filho, Antonio Carlos

    2017-11-18

    Multiple sclerosis (MS) is a neurodegenerative disease with increasing importance in recent years, in which the T2 weighted with fluid attenuation inversion recovery (FLAIR) MRI imaging technique has been addressed for the hyperintense MS lesion assessment. Many automatic lesion segmentation approaches have been proposed in the literature in order to assist health professionals. In this study, a new hybrid lesion segmentation approach based on logistic classification (LC) and the iterative contrast enhancement (ICE) method is proposed (LC+ICE). T1 and FLAIR MRI images from 32 secondary progressive MS (SPMS) patients were used in the LC+ICE method, in which manual segmentation was used as the ground truth lesion segmentation. The DICE, Sensitivity, Specificity, Area under the ROC curve (AUC), and Volume Similarity measures showed that the LC+ICE method is able to provide a precise and robust lesion segmentation estimate, which was compared with two recent FLAIR lesion segmentation approaches. In addition, the proposed method also showed a stable segmentation among lesion loads, showing a wide applicability to different disease stages. The LC+ICE procedure is a suitable alternative to assist the manual FLAIR hyperintense MS lesion segmentation task.

  16. Algorithms

    Indian Academy of Sciences (India)

    , i is referred to as the loop-index, 'stat-body' is any sequence of ... while i ~ N do stat-body; i: = i+ 1; endwhile. The algorithm for sorting the numbers is described in Table 1 and the algorithmic steps on a list of 4 numbers shown in. Figure 1.

  17. Fracture Analysis of Vessels. Oak Ridge FAVOR, v06.1, Computer Code: Theory and Implementation of Algorithms, Methods, and Correlations

    Energy Technology Data Exchange (ETDEWEB)

    Williams, P. T. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Dickson, T. L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Yin, S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2007-12-01

    The current regulations to insure that nuclear reactor pressure vessels (RPVs) maintain their structural integrity when subjected to transients such as pressurized thermal shock (PTS) events were derived from computational models developed in the early-to-mid 1980s. Since that time, advancements and refinements in relevant technologies that impact RPV integrity assessment have led to an effort by the NRC to re-evaluate its PTS regulations. Updated computational methodologies have been developed through interactions between experts in the relevant disciplines of thermal hydraulics, probabilistic risk assessment, materials embrittlement, fracture mechanics, and inspection (flaw characterization). Contributors to the development of these methodologies include the NRC staff, their contractors, and representatives from the nuclear industry. These updated methodologies have been integrated into the Fracture Analysis of Vessels -- Oak Ridge (FAVOR, v06.1) computer code developed for the NRC by the Heavy Section Steel Technology (HSST) program at Oak Ridge National Laboratory (ORNL). The FAVOR, v04.1, code represents the baseline NRC-selected applications tool for re-assessing the current PTS regulations. This report is intended to document the technical bases for the assumptions, algorithms, methods, and correlations employed in the development of the FAVOR, v06.1, code.

  18. An Improved Algorithm Based on Minimum Spanning Tree for Multi-scale Segmentation of Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    LI Hui

    2015-07-01

    Full Text Available As the basis of object-oriented information extraction from remote sensing imagery,image segmentation using multiple image features,exploiting spatial context information, and by a multi-scale approach are currently the research focuses. Using an optimization approach of the graph theory, an improved multi-scale image segmentation method is proposed. In this method, the image is applied with a coherent enhancement anisotropic diffusion filter followed by a minimum spanning tree segmentation approach, and the resulting segments are merged with reference to a minimum heterogeneity criterion.The heterogeneity criterion is defined as a function of the spectral characteristics and shape parameters of segments. The purpose of the merging step is to realize the multi-scale image segmentation. Tested on two images, the proposed method was visually and quantitatively compared with the segmentation method employed in the eCognition software. The results show that the proposed method is effective and outperforms the latter on areas with subtle spectral differences.

  19. Vessel Operating Units (Vessels)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains data for vessels that are greater than five net tons and have a current US Coast Guard documentation number. Beginning in1979, the NMFS...

  20. Intracoronary Compared to Intravenous Abciximab in Patients with ST Segment Elevation Myocardial Infarction Treated with Primary Percutaneous Coronary Intervention Reduces Mortality, Target Vessel Revascularization and Reinfarction after 1 Year

    DEFF Research Database (Denmark)

    Iversen, Allan Zeeberg; Galatius, Soeren; Abildgaard, Ulrik

    2011-01-01

    Objectives: Administration of the glycoprotein IIb/IIIa inhibitor abciximab to patients with ST segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI) improves outcome. Data have suggested that an intracoronary (IC) bolus might be superior...... to the standard intravenous (IV) administration. We have previously reported reduced short-term mortality and need for target vessel revascularization (TVR) with the IC route. We now present long-term data from our randomized trial on IC versus IV abciximab in pPCI-treated STEMI patients. Methods: A total of 355...

  1. Template characterization and correlation algorithm created from segmentation for the iris biometric authentication based on analysis of textures implemented on a FPGA

    Science.gov (United States)

    Giacometto, F. J.; Vilardy, J. M.; Torres, C. O.; Mattos, L.

    2011-01-01

    Among the most used biometric signals to set personal security permissions, taker increasingly importance biometric iris recognition based on their textures and images of blood vessels due to the rich in these two unique characteristics that are unique to each individual. This paper presents an implementation of an algorithm characterization and correlation of templates created for biometric authentication based on iris texture analysis programmed on a FPGA (Field Programmable Gate Array), authentication is based on processes like characterization methods based on frequency analysis of the sample, and frequency correlation to obtain the expected results of authentication.

  2. Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm.

    Science.gov (United States)

    Precioso, Frederic; Barlaud, Michel; Blu, Thierry; Unser, Michael

    2005-07-01

    This paper deals with fast image and video segmentation using active contours. Region-based active contours using level sets are powerful techniques for video segmentation, but they suffer from large computational cost. A parametric active contour method based on B-Spline interpolation has been proposed in to highly reduce the computational cost, but this method is sensitive to noise. Here, we choose to relax the rigid interpolation constraint in order to robustify our method in the presence of noise: by using smoothing splines, we trade a tunable amount of interpolation error for a smoother spline curve. We show by experiments on natural sequences that this new flexibility yields segmentation results of higher quality at no additional computational cost. Hence, real-time processing for moving objects segmentation is preserved.

  3. PCA-based localization approach for segmentation of optic disc.

    Science.gov (United States)

    Gopi, Varun P; Anjali, M S; Niwas, S Issac

    2017-12-01

    The optic disc is the origin of the optic nerve, where the axons of retinal ganglion cells join together. The size, shape and contour of optic disc are used for classification and identification of retinal diseases. Automatic detection of eye disease requires development of an efficient algorithm. This paper proposes an efficient method for optic disc segmentation and detection for the diagnosis of retinal diseases. The methodology involves optic disc localization, blood vessel inpainting and optic disc segmentation. Localization is based on principal component analysis, and segmentation is based on Markov random field segmentation. In order to get reasonable background images, blood vessel inpainting is done before segmentation. The proposed method tested with two standard databases MESSIDOR and DRIVE, and achieved an average overlapping score of 92.41, 92.17%, respectively; also validation experiments were done with one local database from Venu Eye Hospital, New Delhi, and obtained an average overlapping score of 91%. An efficient algorithm is developed for detecting optic disc using principal component analysis-based localization and Markov random field segmentation. The comparison with alternative method yielded results that demonstrate the superiority of the proposed algorithm for optic disc detection.

  4. Image segmentation and activity estimation for microPET 11C-raclopride images using an expectation-maximum algorithm with a mixture of Poisson distributions.

    Science.gov (United States)

    Su, Kuan-Hao; Chen, Jay S; Lee, Jih-Shian; Hu, Chi-Min; Chang, Chi-Wei; Chou, Yuan-Hwa; Liu, Ren-Shyan; Chen, Jyh-Cheng

    2011-07-01

    The objective of this study was to use a mixture of Poisson (MOP) model expectation maximum (EM) algorithm for segmenting microPET images. Simulated rat phantoms with partial volume effect and different noise levels were generated to evaluate the performance of the method. The partial volume correction was performed using an EM deblurring method before the segmentation. The EM-MOP outperforms the EM-MOP in terms of the estimated spatial accuracy, quantitative accuracy, robustness and computing efficiency. To conclude, the proposed EM-MOP method is a reliable and accurate approach for estimating uptake levels and spatial distributions across target tissues in microPET (11)C-raclopride imaging studies. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking

    Directory of Open Access Journals (Sweden)

    Shoujun Zhou

    2010-08-01

    Full Text Available Abstract Background Segmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions. Methods This paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO and a vessel structure pattern detector (SPD] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern. Results By appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel

  6. Algorithms

    Indian Academy of Sciences (India)

    Algorithms. 3. Procedures and Recursion. R K Shyamasundar. In this article we introduce procedural abstraction and illustrate its uses. Further, we illustrate the notion of recursion which is one of the most useful features of procedural abstraction. Procedures. Let us consider a variation of the pro blem of summing the first M.

  7. Algorithms

    Indian Academy of Sciences (India)

    number of elements. We shall illustrate the widely used matrix multiplication algorithm using the two dimensional arrays in the following. Consider two matrices A and B of integer type with di- mensions m x nand n x p respectively. Then, multiplication of. A by B denoted, A x B , is defined by matrix C of dimension m xp where.

  8. Algorithm for the automatic computation of the modified Anderson-Wilkins acuteness score of ischemia from the pre-hospital ECG in ST-segment elevation myocardial infarction

    DEFF Research Database (Denmark)

    Fakhri, Yama; Sejersten, Maria; Schoos, Mikkel Malby

    2017-01-01

    BACKGROUND: The acuteness score (based on the modified Anderson-Wilkins score) estimates the acuteness of ischemia based on ST-segment, Q-wave and T-wave measurements obtained from the electrocardiogram (ECG) in patients with ST Elevation Myocardial Infarction (STEMI). The score (range 1 (least...... acute) to 4 (most acute)) identifies patients with substantial myocardial salvage potential regardless of patient reported symptom duration. However, due to the complexity of the score, it is not used in clinical practice. Therefore, we aimed to develop a reliable algorithm that automatically computes...

  9. Vessel network detection using contour evolution and color components

    Energy Technology Data Exchange (ETDEWEB)

    Ushizima, Daniela; Medeiros, Fatima; Cuadros, Jorge; Martins, Charles

    2011-06-22

    Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration, among other applications. There is an extensive literature related to this problem, often excluding the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper relies on an algorithm using front propagation to segment the vessel network. The algorithm includes a penalty in the wait queue on the fast marching heap to minimize leakage of the evolving interface. The method requires no manual labeling, a minimum number of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.

  10. Page 1 Multiferforate Plates in Xylem Vessels of Monocotyledonous ...

    Indian Academy of Sciences (India)

    Transverse section of a root of Alocasia indica showing 6 multiperforate plates in the vessel segments of the metaxylem. X46. FIG. 2.-A number of vessel segments from the root of Crinum asiaticum showing the two ends, x 15. FIG. 3.-Figure showing the arrangement of adjacent vessel segments with long oblique ends.

  11. An automatic 2D CAD algorithm for the segmentation of the IMT in ultrasound carotid artery images

    OpenAIRE

    Ilea, Dana E.; Whelan, Paul F.; Brown, C; Stanton, Alice

    2009-01-01

    Common carotid intima-media thickness (IMT) is a reliable measure of early atherosclerosis - its accurate measurement can be used in the process of evaluating the presence and tracking the progression of disease. The aim of this study is to introduce a novel unsupervised Computer Aided Detection (CAD) algorithm that is able to identify and measure the IMT in 2D ultrasound carotid images. The developed technique relies on a suite of image processing algorithms that embeds a statistical model t...

  12. An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery

    CSIR Research Space (South Africa)

    Mdakane, Lizwe W

    2017-06-01

    Full Text Available Oil slick events caused due to bilge leakage/dumps from ships and from other anthropogenic sources pose a threat to the aquatic ecosystem and need to be monitored on a regular basis. An automatic image-segmentation-based framework to detect oil...

  13. Segmentation of digitized histological sections for quantification of the muscularized vasculature in the mouse hind limb.

    Science.gov (United States)

    Xu, Yiwen; Pickering, J Geoffrey; Nong, Zengxuan; Ward, Aaron D

    2017-04-01

    Immunohistochemical tissue staining enhances microvasculature characteristics, including the smooth muscle in the medial layer of the vessel walls that is responsible for regulation of blood flow. The vasculature can be imaged in a comprehensive fashion using whole-slide scanning. However, since each such image potentially contains hundreds of small vessels, manual vessel delineation and quantification is not practically feasible. In this work, we present a fully automatic segmentation and vasculature quantification algorithm for whole-slide images. We evaluated its performance on tissue samples drawn from the hind limbs of wild-type mice, stained for smooth muscle using 3,3'-Diaminobenzidine (DAB) immunostain. The algorithm was designed to be robust to vessel fragmentation due to staining irregularity, and artefactual staining of nonvessel objects. Colour deconvolution was used to isolate the DAB stain for detection of vessel wall fragments. Complete vessels were reconstructed from the fragments by joining endpoints of topological skeletons. Automatic measures of vessel density, perimeter, wall area and local wall thickness were taken. The segmentation algorithm was validated against manual measures, resulting in a Dice similarity coefficient of 89%. The relationships observed between these measures were as expected from a biological standpoint, providing further reinforcement of the accuracy of this system. This system provides a fully automated and accurate means of measuring the arteriolar and venular morphology of vascular smooth muscle. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

  14. Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms.

    Science.gov (United States)

    Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L; Obajuluwa, Ademola M; Xu, Jianwu; Hori, Masatoshi

    2010-05-01

    Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F = 0.77; p(F case by manual segmentation). The computerized liver extraction scheme provides an efficient and accurate way of measuring liver volumes in CT.

  15. Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations.

    Science.gov (United States)

    Borbély, Bence J; Szolgay, Péter

    2017-01-17

    Model based analysis of human upper limb movements has key importance in understanding the motor control processes of our nervous system. Various simulation software packages have been developed over the years to perform model based analysis. These packages provide computationally intensive-and therefore off-line-solutions to calculate the anatomical joint angles from motion captured raw measurement data (also referred as inverse kinematics). In addition, recent developments in inertial motion sensing technology show that it may replace large, immobile and expensive optical systems with small, mobile and cheaper solutions in cases when a laboratory-free measurement setup is needed. The objective of the presented work is to extend the workflow of measurement and analysis of human arm movements with an algorithm that allows accurate and real-time estimation of anatomical joint angles for a widely used OpenSim upper limb kinematic model when inertial sensors are used for movement recording. The internal structure of the selected upper limb model is analyzed and used as the underlying platform for the development of the proposed algorithm. Based on this structure, a prototype marker set is constructed that facilitates the reconstruction of model-based joint angles using orientation data directly available from inertial measurement systems. The mathematical formulation of the reconstruction algorithm is presented along with the validation of the algorithm on various platforms, including embedded environments. Execution performance tables of the proposed algorithm show significant improvement on all tested platforms. Compared to OpenSim's Inverse Kinematics tool 50-15,000x speedup is achieved while maintaining numerical accuracy. The proposed algorithm is capable of real-time reconstruction of standardized anatomical joint angles even in embedded environments, establishing a new way for complex applications to take advantage of accurate and fast model-based inverse

  16. Validation tools for image segmentation

    Science.gov (United States)

    Padfield, Dirk; Ross, James

    2009-02-01

    A large variety of image analysis tasks require the segmentation of various regions in an image. For example, segmentation is required to generate accurate models of brain pathology that are important components of modern diagnosis and therapy. While the manual delineation of such structures gives accurate information, the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing the parameter settings for individual cases and across a collection of datasets using the Design of Experiment framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms. We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the statistical segmentation algorithm although they perform reasonably well considering their simplicity.

  17. Quantification of Right and Left Ventricular Function in Cardiac MR Imaging: Comparison of Semiautomatic and Manual Segmentation Algorithms

    Science.gov (United States)

    Souto, Miguel; Masip, Lambert Raul; Couto, Miguel; Suárez-Cuenca, Jorge Juan; Martínez, Amparo; Tahoces, Pablo G.; Carreira, Jose Martin; Croisille, Pierre

    2013-01-01

    The purpose of this study was to evaluate the performance of a semiautomatic segmentation method for the anatomical and functional assessment of both ventricles from cardiac cine magnetic resonance (MR) examinations, reducing user interaction to a “mouse-click”. Fifty-two patients with cardiovascular diseases were examined using a 1.5-T MR imaging unit. Several parameters of both ventricles, such as end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF), were quantified by an experienced operator using the conventional method based on manually-defined contours, as the standard of reference; and a novel semiautomatic segmentation method based on edge detection, iterative thresholding and region growing techniques, for evaluation purposes. No statistically significant differences were found between the two measurement values obtained for each parameter (p > 0.05). Correlation to estimate right ventricular function was good (r > 0.8) and turned out to be excellent (r > 0.9) for the left ventricle (LV). Bland-Altman plots revealed acceptable limits of agreement between the two methods (95%). Our study findings indicate that the proposed technique allows a fast and accurate assessment of both ventricles. However, further improvements are needed to equal results achieved for the right ventricle (RV) using the conventional methodology. PMID:26835680

  18. Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform

    OpenAIRE

    Rezaee Kh.; Haddadnia J

    2013-01-01

    Background: Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. Objective: This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast...

  19. Fast blood-flow simulation for large arterial trees containing thousands of vessels.

    Science.gov (United States)

    Muller, Alexandre; Clarke, Richard; Ho, Harvey

    2017-02-01

    Blood flow modelling has previously been successfully carried out in arterial trees to study pulse wave propagation using nonlinear or linear flow solvers. However, the number of vessels used in the simulations seldom grows over a few hundred. The aim of this work is to present a computationally efficient solver coupled with highly detailed arterial trees containing thousands of vessels. The core of the solver is based on a modified transmission line method, which exploits the analogy between electrical current in finite-length conductors and blood flow in vessels. The viscoelastic behaviour of the arterial-wall is taken into account using a complex elastic modulus. The flow is solved vessel by vessel in the frequency domain and the calculated output pressure is then used as an input boundary condition for daughter vessels. The computational results yield pulsatile blood pressure and flow rate for every segment in the tree. This solver is coupled with large arterial trees generated from a three-dimensional constrained constructive optimisation algorithm. The tree contains thousands of blood vessels with radii spanning ~1 mm in the root artery to ~30 μm in leaf vessels. The computation takes seconds to complete for a vasculature of 2048 vessels and less than 2 min for a vasculature of 4096 vessels on a desktop computer.

  20. Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Rezaee Kh

    2013-09-01

    Full Text Available Background: Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. Objective: This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. Method: In the frst step, after designing a flter, the discrete wavelet transform is applied to the input images and the approximate coeffcients of scaling components are constructed. Then, the different parts of image are classifed in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters’ number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. Results: We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM. The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coeffcient was approximately 0.85, which proved the suitable reliability of the system performance. Conclusion: The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.

  1. FPGA Implementation of Gaussian Mixture Model Algorithm for 47 fps Segmentation of 1080p Video

    Directory of Open Access Journals (Sweden)

    Mariangela Genovese

    2013-01-01

    Full Text Available Circuits and systems able to process high quality video in real time are fundamental in nowadays imaging systems. The circuit proposed in the paper, aimed at the robust identification of the background in video streams, implements the improved formulation of the Gaussian Mixture Model (GMM algorithm that is included in the OpenCV library. An innovative, hardware oriented, formulation of the GMM equations, the use of truncated binary multipliers, and ROM compression techniques allow reduced hardware complexity and increased processing capability. The proposed circuit has been designed having commercial FPGA devices as target and provides speed and logic resources occupation that overcome previously proposed implementations. The circuit, when implemented on Virtex6 or StratixIV, processes more than 45 frame per second in 1080p format and uses few percent of FPGA logic resources.

  2. Analysis of Intergrade Variables In The Fuzzy C-Means And Improved Algorithm Cat Swarm Optimization(FCM-ISO) In Search Segmentation

    Science.gov (United States)

    Saragih, Jepronel; Salim Sitompul, Opim; Situmorang, Zakaria

    2017-12-01

    One of the techniques known in Data Mining namely clustering. Image segmentation process does not always represent the actual image which is caused by a combination of algorithms as long as it has not been able to obtain optimal cluster centers. In this research will search for the smallest error with the counting result of a Fuzzy C Means process optimized with Cat swam Algorithm Optimization that has been developed by adding the weight of the energy in the process of Tracing Mode.So with the parameter can be determined the most optimal cluster centers and most closely with the data will be made the cluster. Weigh inertia in this research, namely: (0.1), (0.2), (0.3), (0.4), (0.5), (0.6), (0.7), (0.8) and (0.9). Then compare the results of each variable values inersia (W) which is different and taken the smallest results. Of this weighting analysis process can acquire the right produce inertia variable cost function the smallest.

  3. Association between proximal internal carotid artery steno-occlusive disease and diffuse wall thickening in its petrous segment: a magnetic resonance vessel wall imaging study

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Xiaoyi; Li, Dongye [Capital Medical University and Beijing Institute for Brain Disorders, Center for Brain Disorders Research, Beijing (China); Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); Zhao, Huilin [Shanghai Jiao Tong University, Department of Radiology, Renji Hospital, School of Medicine, Shanghai (China); Chen, Zhensen; Qiao, Huiyu; He, Le; Li, Rui [Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); Cui, Yuanyuan [PLA General Hospital, Department of Radiology, Beijing (China); Zhou, Zechen [Philips Research China, Healthcare Department, Beijing (China); Yuan, Chun [Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); University of Washington, Department of Radiology, Seattle, WA (United States); Zhao, Xihai [Tsinghua University School of Medicine, Center for Biomedical Imaging Research, Department of Biomedical Engineering, Beijing (China); Beijing Institute for Brain Disorders, Center for Stroke, Beijing (China)

    2017-05-15

    Significant stenosis or occlusion in carotid arteries may lead to diffuse wall thickening (DWT) in the arterial wall of downstream. This study aimed to investigate the correlation between proximal internal carotid artery (ICA) steno-occlusive disease and DWT in ipsilateral petrous ICA. Symptomatic patients with atherosclerotic stenosis (>0%) in proximal ICA were recruited and underwent carotid MR vessel wall imaging. The 3D motion sensitized-driven equilibrium prepared rapid gradient-echo (3D-MERGE) was acquired for characterizing the wall thickness and longitudinal extent of the lesions in petrous ICA and the distance from proximal lesion to the petrous ICA. The stenosis degree in proximal ICA was measured on the time-of-flight (TOF) images. In total, 166 carotid arteries from 125 patients (mean age 61.0 ± 10.5 years, 99 males) were eligible for final analysis and 64 showed DWT in petrous ICAs. The prevalence of severe DWT in petrous ICA was 1.4%, 5.3%, 5.9%, and 80.4% in ipsilateral proximal ICAs with stenosis category of 1%-49%, 50%-69%, 70%-99%, and total occlusion, respectively. Proximal ICA stenosis was significantly correlated with the wall thickness in petrous ICA (r = 0.767, P < 0.001). Logistic regression analysis showed that proximal ICA stenosis was independently associated with DWT in ipsilateral petrous ICA (odds ratio (OR) = 2.459, 95% confidence interval (CI) 1.896-3.189, P < 0.001). Proximal ICA steno-occlusive disease is independently associated with DWT in ipsilateral petrous ICA. (orig.)

  4. Retinal vessel enhancement based on the Gaussian function and image fusion

    Science.gov (United States)

    Moraru, Luminita; Obreja, Cristian Dragoş

    2017-01-01

    The Gaussian function is essential in the construction of the Frangi and COSFIRE (combination of shifted filter responses) filters. The connection of the broken vessels and an accurate extraction of the vascular structure is the main goal of this study. Thus, the outcome of the Frangi and COSFIRE edge detection algorithms are fused using the Dempster-Shafer algorithm with the aim to improve detection and to enhance the retinal vascular structure. For objective results, the average diameters of the retinal vessels provided by Frangi, COSFIRE and Dempster-Shafer fusion algorithms are measured. These experimental values are compared to the ground truth values provided by manually segmented retinal images. We prove the superiority of the fusion algorithm in terms of image quality by using the figure of merit objective metric that correlates the effects of all post-processing techniques.

  5. Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features.

    Science.gov (United States)

    Xiao, Ruoxiu; Ding, Hui; Zhai, Fangwen; Zhao, Tong; Zhou, Wenjing; Wang, Guangzhi

    2017-04-01

    In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset. First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster-Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation. Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios. The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Volumetric analysis of lung nodules in computed tomography (CT): comparison of two different segmentation algorithm softwares and two different reconstruction filters on automated volume calculation.

    Science.gov (United States)

    Christe, Andreas; Brönnimann, Alain; Vock, Peter

    2014-02-01

    A precise detection of volume change allows for better estimating the biological behavior of the lung nodules. Postprocessing tools with automated detection, segmentation, and volumetric analysis of lung nodules may expedite radiological processes and give additional confidence to the radiologists. To compare two different postprocessing software algorithms (LMS Lung, Median Technologies; LungCARE®, Siemens) in CT volumetric measurement and to analyze the effect of soft (B30) and hard reconstruction filter (B70) on automated volume measurement. Between January 2010 and April 2010, 45 patients with a total of 113 pulmonary nodules were included. The CT exam was performed on a 64-row multidetector CT scanner (Somatom Sensation, Siemens, Erlangen, Germany) with the following parameters: collimation, 24x1.2 mm; pitch, 1.15; voltage, 120 kVp; reference tube current-time, 100 mAs. Automated volumetric measurement of each lung nodule was performed with the two different postprocessing algorithms based on two reconstruction filters (B30 and B70). The average relative volume measurement difference (VME%) and the limits of agreement between two methods were used for comparison. At soft reconstruction filters the LMS system produced mean nodule volumes that were 34.1% (P LMS and 1.6% for LungCARE®, respectively (both with P LMS measured greater volumes with both filters, 13.6% for soft and 3.8% for hard filters, respectively (P  0.05). There is a substantial inter-software (LMS/LungCARE®) as well as intra-software variability (B30/B70) in lung nodule volume measurement; therefore, it is mandatory to use the same equipment with the same reconstruction filter for the follow-up of lung nodule volume.

  7. CoroEval: a multi-platform, multi-modality tool for the evaluation of 3D coronary vessel reconstructions

    Science.gov (United States)

    Schwemmer, C.; Forman, C.; Wetzl, J.; Maier, A.; Hornegger, J.

    2014-09-01

    We present a software, called CoroEval, for the evaluation of 3D coronary vessel reconstructions from clinical data. It runs on multiple operating systems and is designed to be independent of the imaging modality used. At this point, its purpose is the comparison of reconstruction algorithms or acquisition protocols, not the clinical diagnosis. Implemented metrics are vessel sharpness and diameter. All measurements are taken from the raw intensity data to be independent of display windowing functions. The user can either import a vessel centreline segmentation from other software, or perform a manual segmentation in CoroEval. An automated segmentation correction algorithm is provided to improve non-perfect centrelines. With default settings, measurements are taken at 1 mm intervals along the vessel centreline and from 10 different angles at each measurement point. This allows for outlier detection and noise-robust measurements without the burden and subjectivity a manual measurement process would incur. Graphical measurement results can be directly exported to vector or bitmap graphics for integration into scientific publications. Centreline and lumen segmentations can be exported as point clouds and in various mesh formats. We evaluated the diameter measurement process using three phantom datasets. An average deviation of 0.03 ± 0.03 mm was found. The software is available in binary and source code form at http://www5.cs.fau.de/CoroEval/.

  8. Technical Note: A new zeolite PET phantom to test segmentation algorithms on heterogeneous activity distributions featured with ground-truth contours.

    Science.gov (United States)

    Soffientini, Chiara D; De Bernardi, Elisabetta; Casati, Rosangela; Baselli, Giuseppe; Zito, Felicia

    2017-01-01

    Design, realization, scan, and characterization of a phantom for PET Automatic Segmentation (PET-AS) assessment are presented. Radioactive zeolites immersed in a radioactive heterogeneous background simulate realistic wall-less lesions with known irregular shape and known homogeneous or heterogeneous internal activity. Three different zeolite families were evaluated in terms of radioactive uptake homogeneity, necessary to define activity and contour ground truth. Heterogeneous lesions were simulated by the perfect matching of two portions of a broken zeolite, soaked in two different 18 F-FDG radioactive solutions. Heterogeneous backgrounds were obtained with tissue paper balls and sponge pieces immersed into radioactive solutions. Natural clinoptilolite proved to be the most suitable zeolite for the construction of artificial objects mimicking homogeneous and heterogeneous uptakes in 18 F-FDG PET lesions. Heterogeneous backgrounds showed a coefficient of variation equal to 269% and 443% of a uniform radioactive solution. Assembled phantom included eight lesions with volumes ranging from 1.86 to 7.24 ml and lesion to background contrasts ranging from 4.8:1 to 21.7:1. A novel phantom for the evaluation of PET-AS algorithms was developed. It is provided with both reference contours and activity ground truth, and it covers a wide range of volumes and lesion to background contrasts. The dataset is open to the community of PET-AS developers and utilizers. © 2016 American Association of Physicists in Medicine.

  9. [Pulmonary blood vessels in goats].

    Science.gov (United States)

    Roos, H; Hegner, K; Vollmerhaus, B

    1999-05-01

    The blood vessels in the lung of the goat, which until now have received little attention, are described in detail for the first time. With regard to the segments of the lung, blood vessels are bronchovascular units in the lobi craniales, lobus medius and lobus accessorius, but bronchoartery units in the lobi caudales. We investigated the types of branches of the Aa. pulmonales dextra et sinistra, the inter- and intraspecific principles of the outlet of the pulmonary veins and the importance of bronchopulmonary segmentation of the lungs.

  10. Retinal vessel width measurement at branchings using an improved electric field theory-based graph approach.

    Directory of Open Access Journals (Sweden)

    Xiayu Xu

    Full Text Available The retinal vessel width relationship at vessel branch points in fundus images is an important biomarker of retinal and systemic disease. We propose a fully automatic method to measure the vessel widths at branch points in fundus images. The method is a graph-based method, in which a graph construction method based on electric field theory is applied which specifically deals with complex branching patterns. The vessel centerline image is used as the initial segmentation of the graph. Branching points are detected on the vessel centerline image using a set of detection kernels. Crossing points are distinguished from branch points and excluded. The electric field based graph method is applied to construct the graph. This method is inspired by the non-intersecting force lines in an electric field. At last, the method is further improved to give a consistent vessel width measurement for the whole vessel tree. The algorithm was validated on 100 artery branchings and 100 vein branchings selected from 50 fundus images by comparing with vessel width measurements from two human experts.

  11. Retinal vessel width measurement at branchings using an improved electric field theory-based graph approach.

    Science.gov (United States)

    Xu, Xiayu; Reinhardt, Joseph M; Hu, Qiao; Bakall, Benjamin; Tlucek, Paul S; Bertelsen, Geir; Abràmoff, Michael D

    2012-01-01

    The retinal vessel width relationship at vessel branch points in fundus images is an important biomarker of retinal and systemic disease. We propose a fully automatic method to measure the vessel widths at branch points in fundus images. The method is a graph-based method, in which a graph construction method based on electric field theory is applied which specifically deals with complex branching patterns. The vessel centerline image is used as the initial segmentation of the graph. Branching points are detected on the vessel centerline image using a set of detection kernels. Crossing points are distinguished from branch points and excluded. The electric field based graph method is applied to construct the graph. This method is inspired by the non-intersecting force lines in an electric field. At last, the method is further improved to give a consistent vessel width measurement for the whole vessel tree. The algorithm was validated on 100 artery branchings and 100 vein branchings selected from 50 fundus images by comparing with vessel width measurements from two human experts.

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

    Directory of Open Access Journals (Sweden)

    Shuo-Tsung Chen

    2015-01-01

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

  13. Improving pulmonary vessel image quality with a full model-based iterative reconstruction algorithm in 80kVp low-dose chest CT for pediatric patients aged 0-6 years.

    Science.gov (United States)

    Sun, Jihang; Zhang, Qifeng; Hu, Di; Duan, Xiaomin; Peng, Yun

    2015-06-01

    Full model-based iterative reconstruction (MBIR) algorithm decreasing image noise and improving spatial resolution significantly, combined with low voltage scan may improve image and vessels quality. To evaluate the image quality improvement of pulmonary vessels using a full MBIR in low-dose chest computed tomography (CT) for children. This study was institutional review board approved. Forty-one children (age range, 28 days-6 years, mean age, 2.0 years) who underwent 80 kVp low-dose CT scans were included. Age-dependent noise index (NI) for a 5-mm slice thickness image was used for the acquisition: NI = 11 for 0-12 months old, NI = 13 for 1-2 years old, and NI = 15 for 3-6 years old. Images were retrospectively reconstructed into thin slice thickness of 0.625 mm using the MBIR and a conventional filtered back projection (FBP) algorithm. Two radiologists independently evaluated images subjectively focusing on the ability to display small arteries and diagnosis confidence on a 5-point scale with 3 being clinically acceptable. CT value and image noise in the descending aorta, muscle and fat were measured and statistically compared between the two reconstruction groups. The ability to display small vessels was significantly improved with the MBIR reconstruction. The subjective scores of displaying small vessels were 5.0 and 3.7 with MBIR and FBP, respectively, while the respective diagnosis confidence scores were 5.0 and 3.8. Quantitative image noise for the 0.625 mm slice thickness images in the descending aorta was 15.8 ± 3.8 HU in MBIR group, 57.3% lower than the 37.0 ± 7.3 HU in FBP group. The signal-to-noise ratio and contrast-to-noise ratio for the descending aorta were 28.3 ± 7.9 and 24.05 ± 7.5 in MBIR group, and 12.1 ± 3.7 and 10.6 ± 3.5 in FBP group, respectively. These values were improved by 133.9% and 132.1%, respectively, with MBIR reconstruction compared to FBP reconstruction. Compared to the conventional FBP reconstruction, the image quality and

  14. Research vessels

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, P.S.

    by the research vessels RV Gaveshani and ORV Sagar Kanya are reported. The work carried out by the three charted ships is also recorded. A short note on cruise plans for the study of ferromanganese nodules is added...

  15. Application of hybrid techniques (self-organizing map and fuzzy algorithm) using backscatter data for segmentation and fine-scale roughness characterization of seepage-related seafloor along the western continental margin of India

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Menezes, A.A.A.; Dandapath, S.; Fernandes, W.A.; Karisiddaiah, S.M.; Haris, K.; Gokul, G.S.

    density. 2 I. INTRODUCTION Echo-sounding systems, single beam (SBES) and multi-beam (MBES), allow coincident acquisition of high-resolution seafloor backscatter and bathymetric data [1], [2], which enormously sustains the marine exploration..., the SOM can be utilized to formulate a decision regarding the number of data classes during the online data acquisition, that are then used as an input to the fuzzy C-means (FCM) algorithms for data segmentation [12]. The FCM will require initial...

  16. Pancreas and cyst segmentation

    Science.gov (United States)

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

    2016-03-01

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

  17. Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

    Science.gov (United States)

    Das, Bipul; Saha, Punam K.; Wolf, Ronald; Song, Hee Kwon; Wright, Alexander C.; Wehrli, Felix W.

    2005-04-01

    Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD- weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond

  18. Ex vivo and in vivo label-free imaging of lymphatic vessels using OCT lymphangiography (Conference Presentation)

    Science.gov (United States)

    Gong, Peijun; Es'haghian, Shaghayegh; Karnowski, Karol; Rea, Suzanne; Wood, Fiona M.; Yu, Dao-Yi; McLaughlin, Robert A.; Sampson, David D.

    2017-02-01

    We have been developing an automated method to image lymphatic vessels both ex vivo and in vivo with optical coherence tomography (OCT), using their optical transparency. Our method compensates for the OCT signal attenuation for each A-scan in combination with the correction of the confocal function and sensitivity fall-off, enabling reliable thresholding of lymphatic vessels from the OCT scans. Morphological image processing with a segment-joining algorithm is also incorporated into the method to mitigate partial-volume artifacts, which are particularly evident with small lymphatic vessels. Our method is demonstrated for two different clinical application goals: the monitoring of conjunctival lymphatics for surgical guidance and assessment of glaucoma treatment; and the longitudinal monitoring of human burn scars undergoing laser ablation treatment. We present examples of OCT lymphangiography ex vivo on porcine conjunctivas and in vivo on human burn scars, showing the visualization of the lymphatic vessel network and their longitudinal changes due to treatment.

  19. Validation of an active shape model-based semi-automated segmentation algorithm for the analysis of thigh muscle and adipose tissue cross-sectional areas.

    Science.gov (United States)

    Kemnitz, Jana; Eckstein, Felix; Culvenor, Adam G; Ruhdorfer, Anja; Dannhauer, Torben; Ring-Dimitriou, Susanne; Sänger, Alexandra M; Wirth, Wolfgang

    2017-04-28

    To validate a semi-automated method for thigh muscle and adipose tissue cross-sectional area (CSA) segmentation from MRI. An active shape model (ASM) was trained using 113 MRI CSAs from the Osteoarthritis Initiative (OAI) and combined with an active contour model and thresholding-based post-processing steps. This method was applied to 20 other MRIs from the OAI and to baseline and follow-up MRIs from a 12-week lower-limb strengthening or endurance training intervention (n = 35 females). The agreement of semi-automated vs. previous manual segmentation was assessed using the Dice similarity coefficient and Bland-Altman analyses. Longitudinal changes observed in the training intervention were compared between semi-automated and manual segmentations. High agreement was observed between manual and semi-automated segmentations for subcutaneous fat, quadriceps and hamstring CSAs. With strength training, both the semi-automated and manual segmentation method detected a significant reduction in adipose tissue CSA and a significant gain in quadriceps, hamstring and adductor CSAs. With endurance training, a significant reduction in adipose tissue CSAs was observed with both methods. The semi-automated approach showed high agreement with manual segmentation of thigh muscle and adipose tissue CSAs and showed longitudinal training effects similar to that observed using manual segmentation.

  20. A Heuristic Image Search Algorithm for Active Shape Model Segmentation of the Caudate Nucleus and Hippocampus in Brain MR Images of Children with FASD

    Directory of Open Access Journals (Sweden)

    A A Eicher

    2012-09-01

    Full Text Available Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of Fetal Alcohol Spectrum Disorder (FASD - the most common form of preventable mental retardation worldwide. One approach aims to detect brain abnormalities through an assessment of volume and shape of two sub-cortical structures, the caudate nucleus and hippocampus. We present a method for automatically segmenting these structures from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD. Our method incorporates an Active Shape Model, which is used to learn shape variation from manually segmented training data. A modified discrete Geometrically Deformable Model is used to generate point correspondence between training models. An ASM is then created from the landmark points. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the hippocampus and caudate nucleus. Various popular image search techniques were tested, including an edge detection method and a method based on grey profile Mahalanobis distance measurement. A novel heuristic image search method was also developed and tested. This heuristic method improves image segmentation by taking advantage of characteristics specific to the target data, such as a relatively homogeneous tissue colour in target structures. Results show that ASMs that use the heuristic image search technique produce the most accurate segmentations. An ASM constructed using this technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study.

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

    Science.gov (United States)

    2011-01-01

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

  2. Automatic Segmentation of Spanish Speech Into Syllables

    OpenAIRE

    Mariño Acebal, José Bernardo

    1989-01-01

    This paper presents an algorithm that provides a syllabic segmentation of speech following the syllabification rules of Spanish language. The implemented algorithm is divided into two parts. First, an initial segmentation is made based on energy contour, sonority and duration. Second, a fine adjustement of syllable boundaries and final segmentation is made by applying syllabic rules. Peer Reviewed

  3. Polarimetric Segmentation Using Wishart Test Statistic

    DEFF Research Database (Denmark)

    Skriver, Henning; Schou, Jesper; Nielsen, Allan Aasbjerg

    2002-01-01

    ) approach, which is a merging algorithm for single channel SAR images. The polarimetric version described in this paper uses the above-mentioned test statistic for merging. The segmentation algorithm has been applied to polarimetric SAR data from the Danish dual-frequency, airborne polarimetric SAR, EMISAR......A newly developed test statistic for equality of two complex covariance matrices following the complex Wishart distribution and an associated asymptotic probability for the test statistic has been used in a segmentation algorithm. The segmentation algorithm is based on the MUM (merge using moments....... The results show clearly an improved segmentation performance for the full polarimetric algorithm compared to single channel approaches....

  4. An interactive segmentation method based on superpixel

    DEFF Research Database (Denmark)

    Yang, Shu; Zhu, Yaping; Wu, Xiaoyu

    2015-01-01

    This paper proposes an interactive image-segmentation method which is based on superpixel. To achieve fast segmentation, the method is used to establish a Graphcut model using superpixels as nodes, and a new energy function is proposed. Experimental results demonstrate that the authors' method has...... excellent performance in terms of segmentation accuracy and computation efficiency compared with other segmentation algorithm based on pixels....

  5. Weighted entropy for segmentation evaluation

    Science.gov (United States)

    Khan, Jesmin F.; Bhuiyan, Sharif M.

    2014-04-01

    In many image, video and computer vision systems the image segmentation is an essential part. Significant research has been done in image segmentation and a number of quantitative evaluation methods have already been proposed in the literature. However, often the segmentation evaluation is subjective that means it has been done visually or qualitatively. A segmentation evaluation method based on entropy is proposed in this work which is objective and simple to implement. A weighted self and mutual entropy are proposed to measure the dissimilarity of the pixels among the segmented regions and the similarity within a region. This evaluation technique gives a score that can be used to compare different segmentation algorithms for the same image, or to compare the segmentation results of a given algorithm with different images, or to find the best suited values of the parameters of a segmentation algorithm for a given image. The simulation results show that the proposed method can identify over-segmentation, under-segmentation, and the good segmentation.

  6. The optimal monochromatic spectral computed tomographic imaging plus adaptive statistical iterative reconstruction algorithm can improve the superior mesenteric vessel image quality.

    Science.gov (United States)

    Yin, Xiao-Ping; Zuo, Zi-Wei; Xu, Ying-Jin; Wang, Jia-Ning; Liu, Huai-Jun; Liang, Guang-Lu; Gao, Bu-Lang

    2017-04-01

    To investigate the effect of the optimal monochromatic spectral computed tomography (CT) plus adaptive statistical iterative reconstruction on the improvement of the image quality of the superior mesenteric artery and vein. The gemstone spectral CT angiographic data of 25 patients were reconstructed in the following three groups: 70KeV, the optimal monochromatic imaging, and the optimal monochromatic plus 40%iterative reconstruction mode. The CT value, image noises (IN), background CT value and noises, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image scores of the vessels and surrounding tissues were analyzed. In the 70KeV, the optimal monochromatic and the optimal monochromatic images plus 40% iterative reconstruction group, the mean scores of image quality were 3.86, 4.24 and 4.25 for the superior mesenteric artery and 3.46, 3.78 and 3.81 for the superior mesenteric vein, respectively. The image quality scores for the optimal monochromatic and the optimal monochromatic plus 40% iterative reconstruction groups were significantly greater than for the 70KeV group (Piterative reconstruction group than in the 70KeV group. The optimal monochromatic plus 40% iterative reconstruction group had significantly (Piterative reconstruction using low-contrast agent dosage and low injection rate can significantly improve the image quality of the superior mesenteric artery and vein. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. 2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection

    Directory of Open Access Journals (Sweden)

    Sotirios Raptis

    2010-01-01

    and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency. Specific vessel parameters (centerline, width, location, fall-away rate, main orientation are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs. These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP detection.

  8. ADVANCED CLUSTER BASED IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    D. Kesavaraja

    2011-11-01

    Full Text Available This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

  9. Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means.

    Science.gov (United States)

    Chiu, M J; Lin, C C; Chuang, K H; Chen, J H; Huang, K M

    2001-03-01

    The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI were acquired for segmentation analysis. Single-slice axial T2* fast low-angle shot (FLASH) images were obtained during the functional study. Pixelwise cross-correlation analysis was performed to obtain an activation map. A cascaded algorithm, combining Kohonen feature maps and fuzzy C means, was applied for segmentation. After processing, masks for gray matter, white matter, small vessels, and large vessels were generated. Tissue-specific analysis showed a signal change rate of 4.53% in gray matter, 2.98% in white matter, 5.79% in small vessels, and 7.24% in large vessels. Different temporal patterns as well as different levels of activation were identified in the functional response from various types of tissue. High correlation exists between cortical gray matter and subcortical white matter (r = 0.957), while the vessel behaves somewhat different temporally. The cortical gray matter fits best to the assumed input function (r = 0.957) followed by subcortical white matter (r = 0.829) and vessels (r = 0.726). The automated algorithm of tissue-specific analysis thus can assist functional MRI studies with different modalities of response in different brain regions.

  10. Biomechanical deformable image registration of longitudinal lung CT images using vessel information.

    Science.gov (United States)

    Cazoulat, Guillaume; Owen, Dawn; Matuszak, Martha M; Balter, James M; Brock, Kristy K

    2016-07-07

    Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: an initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix's eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute target registration errors (TRE). The TRE was: [Formula: see text], [Formula: see text] and [Formula: see text] mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical

  11. Heuristically improved Bayesian segmentation of brain MR images ...

    African Journals Online (AJOL)

    Hence involving problem specific heuristics and expert knowledge in designing segmentation algorithms seems to be useful. A two-phase segmentation algorithm based on Bayesian method is proposed in this paper. The Bayesian part uses the gray value in segmenting images and the segmented image is used as the

  12. Adaptive segmentation for scientific databases

    NARCIS (Netherlands)

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

    2008-01-01

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

  13. Model-based automatic segmentation algorithm accurately assesses the whole cardiac volumetric parameters in patients with cardiac CT angiography: a validation study for evaluating the accuracy of the workstation software and establishing the reference values.

    Science.gov (United States)

    Mao, Song Shou; Li, Dong; Vembar, Mani; Gao, Yanlin; Luo, Yanting; Lam, Franklin; Syed, Younus Saleem; Liu, Christine; Woo, Kelly; Flores, Fred; Budoff, Matthew J

    2014-05-01

    The cardiac chamber volumes and functions can be assessed manually and automatically using the current computed tomography (CT) workstation system. We aimed to evaluate the accuracy and precision and to establish the reference values for both segmentation methods using cardiac CT angiography (CTA). A total of 134 subjects (mean age 55.3 years, 72 women) without heart disease were enrolled in the study. The cardiac four-chamber volumes, left ventricular (LV) mass, and biventricular functions were measured with manual, semiautomatic, and model-based fully automatic approaches. The accuracies of the semiautomated and fully automated approaches were validated by comparing them with manual segmentation as a reference. The precision error was determined and compared for both manual and automatic measurements. No significant difference was found between the manual and semiautomatic assessments for the assessment of all functional parameters (P > .05). Using the manual method as a reference, the automatic approach provided a similar value in LV ejection fraction and left atrial volumes in both genders and right ventricular (RV) stroke volume in women (P > .05), with some underestimation of RV volume (P model-based fully automatic segmentation algorithm can help with the assessment of the cardiac four-chamber volume and function. This may help in establishing reference values of functional parameters in patients who undergo cardiac CTA. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

  14. Efficient segmentation by sparse pixel classification

    DEFF Research Database (Denmark)

    Dam, Erik B; Loog, Marco

    2008-01-01

    Segmentation methods based on pixel classification are powerful but often slow. We introduce two general algorithms, based on sparse classification, for optimizing the computation while still obtaining accurate segmentations. The computational costs of the algorithms are derived, and they are dem......Segmentation methods based on pixel classification are powerful but often slow. We introduce two general algorithms, based on sparse classification, for optimizing the computation while still obtaining accurate segmentations. The computational costs of the algorithms are derived......, and they are demonstrated on real 3-D magnetic resonance imaging and 2-D radiograph data. We show that each algorithm is optimal for specific tasks, and that both algorithms allow a speedup of one or more orders of magnitude on typical segmentation tasks....

  15. Intracoronary Compared to Intravenous Abciximab in Patients with ST Segment Elevation Myocardial Infarction Treated with Primary Percutaneous Coronary Intervention Reduces Mortality, Target Vessel Revascularization and Reinfarction after 1 Year

    DEFF Research Database (Denmark)

    Iversen, Allan Zeeberg; Galatius, Soeren; Abildgaard, Ulrik

    2011-01-01

    to the standard intravenous (IV) administration. We have previously reported reduced short-term mortality and need for target vessel revascularization (TVR) with the IC route. We now present long-term data from our randomized trial on IC versus IV abciximab in pPCI-treated STEMI patients. Methods: A total of 355...

  16. Segmentation of MRI Volume Data Based on Clustering Method

    Directory of Open Access Journals (Sweden)

    Ji Dongsheng

    2016-01-01

    Full Text Available Here we analyze the difficulties of segmentation without tag line of left ventricle MR images, and propose an algorithm for automatic segmentation of left ventricle (LV internal and external profiles. Herein, we propose an Incomplete K-means and Category Optimization (IKCO method. Initially, using Hough transformation to automatically locate initial contour of the LV, the algorithm uses a simple approach to complete data subsampling and initial center determination. Next, according to the clustering rules, the proposed algorithm finishes MR image segmentation. Finally, the algorithm uses a category optimization method to improve segmentation results. Experiments show that the algorithm provides good segmentation results.

  17. Segmental neurofibromatosis

    OpenAIRE

    Galhotra, Virat; Sheikh, Soheyl; Jindal, Sanjeev; Singla, Anshu

    2014-01-01

    Segmental neurofibromatosis is a rare disorder, characterized by neurofibromas or cafι-au-lait macules limited to one region of the body. Its occurrence on the face is extremely rare and only few cases of segmental neurofibromatosis over the face have been described so far. We present a case of segmental neurofibromatosis involving the buccal mucosa, tongue, cheek, ear, and neck on the right side of the face.

  18. Atlas-based method for segmentation of cerebral vascular trees from phase-contrast magnetic resonance angiography

    Science.gov (United States)

    Passat, Nicolas; Ronse, Christian; Baruthio, Joseph; Armspach, Jean-Paul; Maillot, Claude; Jahn, Christine

    2004-05-01

    Phase-contrast magnetic resonance angiography (PC-MRA) can produce phase images which are 3-dimensional pictures of vascular structures. However, it also provides magnitude images, containing anatomical - but no vascular - data. Classically, algorithms dedicated to PC-MRA segmentation detect the cerebral vascular tree by only working on phase images. We propose here a new approach for segmentation of cerebral blood vessels in PC-MRA using both types of images. This approach is based on the hypothesis that a magnitude image contains anatomical information useful for vascular structures detection. That information can then be transposed from a normal case to any patient image by image registration. An atlas of the whole head has been developed in order to store such anatomical knowledge. It divides a magnitude image into several "vascular areas", each one having specific vessel properties. The atlas can be applied on any magnitude image of an entire or nearly entire head by deformable matching, thus helping to segment blood vessels from the associated phase image. The segmentation method used afterwards is composed of a topology-conserving region growing algorithm using adaptative threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels which are selected according to their greyscale value and the variation of values in their neighborhood. The topology conservation is guaranteed by only selecting simple points during the growing process. The method has been performed on 15 PC-MRA's of the brain. The results have been validated using MIP and 3D surface rendering visualization; a comparison to other results obtained without an atlas proves that atlas-based methods are an effective way to optimize vascular segmentation strategies.

  19. Design of Content Based Image Retrieval Scheme for Diabetic Retinopathy Images using Harmony Search Algorithm.

    Science.gov (United States)

    Sivakamasundari, J; Natarajan, V

    2015-01-01

    Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Automated segmentation of blood vessel is vital for periodic screening and timely diagnosis. An attempt has been made to generate continuous retinal vasculature for the design of Content Based Image Retrieval (CBIR) application. The typical normal and abnormal retinal images are preprocessed to improve the vessel contrast. The blood vessels are segmented using evolutionary based Harmony Search Algorithm (HSA) combined with Otsu Multilevel Thresholding (MLT) method by best objective functions. The segmentation results are validated with corresponding ground truth images using binary similarity measures. The statistical, textural and structural features are obtained from the segmented images of normal and DR affected retina and are analyzed. CBIR in medical image retrieval applications are used to assist physicians in clinical decision-support techniques and research fields. A CBIR system is developed using HSA based Otsu MLT segmentation technique and the features obtained from the segmented images. Similarity matching is carried out between the features of query and database images using Euclidean Distance measure. Similar images are ranked and retrieved. The retrieval performance of CBIR system is evaluated in terms of precision and recall. The CBIR systems developed using HSA based Otsu MLT and conventional Otsu MLT methods are compared. The retrieval performance such as precision and recall are found to be 96% and 58% for CBIR system using HSA based Otsu MLT segmentation. This automated CBIR system could be recommended for use in computer assisted diagnosis for diabetic retinopathy screening.

  20. A study of reactor vessel integrity assessment

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jae Hoon [Korea Institute of Nuclear Safety, Taejon (Korea, Republic of); Kim, Jong Kyung; Shin, Chang Ho; Seo, Bo Kyun [Hanyang Univ., Seoul (Korea, Republic of)

    1999-02-15

    The fast neutron fluence at the Reactor Pressure Vessel(RPV) of KNGR designed for 60 years lifetime was calculated by full-scope Monte Carlo simulation for reactor vessel integrity assessment. KNGR core geometry was modeled on a three-dimensional representation of the one-sixteenth of the reactor in-vessel component. Each fuel assemblies were modeled explicitly, and each fuel pins were axially divided into 5 segments. The maximum flux of 4.3 x 10{sup 10} neutrons/cm{sup 2}. sec at the RPV was obtained by tallying neutrons crossing the beltline of inner surface of the RPV.

  1. Vessel Operator System

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Operator cards are required for any operator of a charter/party boat and or a commercial vessel (including carrier and processor vessels) issued a vessel permit from...

  2. Segmental Neurofibromatosis

    Directory of Open Access Journals (Sweden)

    Yesudian Devakar

    1997-01-01

    Full Text Available Segmental neurofibromatosis is a rare variant of neurofibromatosis in which the lesions are confined to one segment or dermatome of the body. They resemble classical neurofibromas in their morphology, histopathology and electron microscopy. However, systemic associations are usually absent. We report one such case with these classical features.

  3. Automatic segmentation of tumor-laden lung volumes from the LIDC database

    Science.gov (United States)

    O'Dell, Walter G.

    2012-03-01

    The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

  4. Segmentation of complex document

    Directory of Open Access Journals (Sweden)

    Souad Oudjemia

    2014-06-01

    Full Text Available In this paper we present a method for segmentation of documents image with complex structure. This technique based on GLCM (Grey Level Co-occurrence Matrix used to segment this type of document in three regions namely, 'graphics', 'background' and 'text'. Very briefly, this method is to divide the document image, in block size chosen after a series of tests and then applying the co-occurrence matrix to each block in order to extract five textural parameters which are energy, entropy, the sum entropy, difference entropy and standard deviation. These parameters are then used to classify the image into three regions using the k-means algorithm; the last step of segmentation is obtained by grouping connected pixels. Two performance measurements are performed for both graphics and text zones; we have obtained a classification rate of 98.3% and a Misclassification rate of 1.79%.

  5. Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis

    Science.gov (United States)

    Lau, Kristen C.; Lee, Hyo Min; Singh, Tanushriya; Maidment, Andrew D. A.

    2015-03-01

    Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) uses an iodinated contrast agent to image the three-dimensional breast vasculature. The University of Pennsylvania has an ongoing DE CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 post-contrast). DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. Temporal subtraction of the post-contrast DE images from the pre-contrast DE image is performed to analyze iodine uptake. Our previous work investigated image registration methods to correct for patient motion, enhancing the evaluation of vascular kinetics. In this project we investigate a segmentation algorithm which identifies blood vessels in the breast from our temporal DE subtraction images. Anisotropic diffusion filtering, Gabor filtering, and morphological filtering are used for the enhancement of vessel features. Vessel labeling methods are then used to distinguish vessel and background features successfully. Statistical and clinical evaluations of segmentation accuracy in DE-CBT images are ongoing.

  6. Flattening maps for the visualization of multibranched vessels.

    Science.gov (United States)

    Zhu, Lei; Haker, Steven; Tannenbaum, Allen

    2005-02-01

    In this paper, we present two novel algorithms which produce flattened visualizations of branched physiological surfaces, such as vessels. The first approach is a conformal mapping algorithm based on the minimization of two Dirichlet functionals. From a triangulated representation of vessel surfaces, we show how the algorithm can be implemented using a finite element technique. The second method is an algorithm which adjusts the conformal mapping to produce a flattened representation of the original surface while preserving areas. This approach employs the theory of optimal mass transport. Furthermore, a new way of extracting center lines for vessel fly-throughs is provided.

  7. Segmental Vitiligo.

    Science.gov (United States)

    van Geel, Nanja; Speeckaert, Reinhart

    2017-04-01

    Segmental vitiligo is characterized by its early onset, rapid stabilization, and unilateral distribution. Recent evidence suggests that segmental and nonsegmental vitiligo could represent variants of the same disease spectrum. Observational studies with respect to its distribution pattern point to a possible role of cutaneous mosaicism, whereas the original stated dermatomal distribution seems to be a misnomer. Although the exact pathogenic mechanism behind the melanocyte destruction is still unknown, increasing evidence has been published on the autoimmune/inflammatory theory of segmental vitiligo. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Semiautomatic Segmentation of Glioma on Mobile Devices

    OpenAIRE

    Ya-Ping Wu; Yu-Song Lin; Wei-Guo Wu; Cong Yang; Jian-Qin Gu; Yan Bai; Mei-Yun Wang

    2017-01-01

    Brain tumor segmentation is the first and the most critical step in clinical applications of radiomics. However, segmenting brain images by radiologists is labor intense and prone to inter- and intraobserver variability. Stable and reproducible brain image segmentation algorithms are thus important for successful tumor detection in radiomics. In this paper, we propose a supervised brain image segmentation method, especially for magnetic resonance (MR) brain images with glioma. This paper uses...

  9. Salient object segmentation based on active contouring.

    Directory of Open Access Journals (Sweden)

    Xin Xia

    Full Text Available Traditional saliency detection algorithms lack object semantic character, and the segmentation algorithms cannot highlight the saliency of the segmentation regions. In order to compensate for the defects of these two algorithms, the salient object segmentation model, which is a novel combination of two algorithms, is established in this paper. With the help of a priori knowledge of image boundary background traits, the K-means++ algorithm is used to cluster the pixels for each region; in line with the sensitivity of the human eye to color and with its attention mechanism, the joint probability distribution of the regional contrast ratio and spatial saliency is established. The selection of the salient area is based on the probabilities, for which the region boundary is taken as the initial curve, and the level-set algorithm is used to perform the salient object segmentation of the image. The curve convergence condition is established according to the confidence level for the segmented region, thus avoiding over-convergence of the segmentation curve. With this method, the salient region boundary is adjacent to the object contour, so the curve evolution time is shorter, and compared with the traditional Li algorithm, the proposed algorithm has higher segmentation evaluation scores, with the additional benefit of emphasizing the importance of the object.

  10. Automatic Segmentation of the Corpus Callosum Using a Cell-Competition Algorithm: Diffusion Tensor Imaging-Based Evaluation of Callosal Atrophy and Tissue Alterations in Patients With Systemic Lupus Erythematosus.

    Science.gov (United States)

    Lee, Shiou-Ping; Wu, Chien-Sheng; Cheng, Jie-Zhi; Chen, Chung-Ming; Chen, Yu-Chiang; Chou, Ming-Chung

    2015-01-01

    Patients with neuropsychiatric systemic lupus erythematosus (NPSLE) may exhibit corpus callosal atrophy and tissue alterations. Measuring the callosal volume and tissue integrity using diffusion tensor imaging (DTI) could help to differentiate patients with NPSLE from patients without NPSLE. Hence, this study aimed to use an automatic cell-competition algorithm to segment the corpus callosum and to investigate the effects of central nervous system (CNS) involvement on the callosal volume and tissue integrity in patients with SLE. Twenty-two SLE patients with (N = 10, NPSLE) and without (N = 12, non-NPSLE) CNS involvement and 22 control subjects were enrolled in this study. For volumetric measurement, a cell-competition algorithm was used to automatically delineate corpus callosal boundaries based on a midsagittal fractional anisotropy (FA) map. After obtaining corpus callosal boundaries for all subjects, the volume, FA, and mean diffusivity (MD) of the corpus callosum were calculated. A post hoc Tamhane's T2 analysis was performed to statistically compare differences among NPSLE, non-NPSLE, and control subjects. A receiver operating characteristic curve analysis was also performed to compare the performance of the volume, FA, and MD of the corpus callosum in differentiating NPSLE from other subjects. Patients with NPSLE had significant decreases in volume and FA but an increase in MD in the corpus callosum compared with control subjects, whereas no significant difference was noted between patients without NPSLE and control subjects. The FA was found to have better performance in differentiating NPSLE from other subjects. A cell-competition algorithm could be used to automatically evaluate callosal atrophy and tissue alterations. Assessments of the corpus callosal volume and tissue integrity helped to demonstrate the effects of CNS involvement in patients with SLE.

  11. Semi-automated segment generation for geographic novelty ...

    African Journals Online (AJOL)

    Charles

    correspondence metric (PCM), is evaluated in this approach as a fitness function for segmentation algorithm ... The results show the potential of using edge metrics, as opposed to area metrics, for evaluating segments in an ... traverses the parameter space of the segmentation algorithm, searching for results most closely.

  12. Hierarchical image segmentation for learning object priors

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.; Li, Nan [TEMPLE UNIV.

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  13. Evaluation metrics for bone segmentation in ultrasound

    Science.gov (United States)

    Lougheed, Matthew; Fichtinger, Gabor; Ungi, Tamas

    2015-03-01

    Tracked ultrasound is a safe alternative to X-ray for imaging bones. The interpretation of bony structures is challenging as ultrasound has no specific intensity characteristic of bones. Several image segmentation algorithms have been devised to identify bony structures. We propose an open-source framework that would aid in the development and comparison of such algorithms by quantitatively measuring segmentation performance in the ultrasound images. True-positive and false-negative metrics used in the framework quantify algorithm performance based on correctly segmented bone and correctly segmented boneless regions. Ground-truth for these metrics are defined manually and along with the corresponding automatically segmented image are used for the performance analysis. Manually created ground truth tests were generated to verify the accuracy of the analysis. Further evaluation metrics for determining average performance per slide and standard deviation are considered. The metrics provide a means of evaluating accuracy of frames along the length of a volume. This would aid in assessing the accuracy of the volume itself and the approach to image acquisition (positioning and frequency of frame). The framework was implemented as an open-source module of the 3D Slicer platform. The ground truth tests verified that the framework correctly calculates the implemented metrics. The developed framework provides a convenient way to evaluate bone segmentation algorithms. The implementation fits in a widely used application for segmentation algorithm prototyping. Future algorithm development will benefit by monitoring the effects of adjustments to an algorithm in a standard evaluation framework.

  14. BIOASSAY VESSEL FAILURE ANALYSIS

    Energy Technology Data Exchange (ETDEWEB)

    Vormelker, P

    2008-09-22

    Two high-pressure bioassay vessels failed at the Savannah River Site during a microwave heating process for biosample testing. Improper installation of the thermal shield in the first failure caused the vessel to burst during microwave heating. The second vessel failure is attributed to overpressurization during a test run. Vessel failure appeared to initiate in the mold parting line, the thinnest cross-section of the octagonal vessel. No material flaws were found in the vessel that would impair its structural performance. Content weight should be minimized to reduce operating temperature and pressure. Outer vessel life is dependent on actual temperature exposure. Since thermal aging of the vessels can be detrimental to their performance, it was recommended that the vessels be used for a limited number of cycles to be determined by additional testing.

  15. DR HAGIS-a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients.

    Science.gov (United States)

    Holm, Sven; Russell, Greg; Nourrit, Vincent; McLoughlin, Niall

    2017-01-01

    A database of retinal fundus images, the DR HAGIS database, is presented. This database consists of 39 high-resolution color fundus images obtained from a diabetic retinopathy screening program in the UK. The NHS screening program uses service providers that employ different fundus and digital cameras. This results in a range of different image sizes and resolutions. Furthermore, patients enrolled in such programs often display other comorbidities in addition to diabetes. Therefore, in an effort to replicate the normal range of images examined by grading experts during screening, the DR HAGIS database consists of images of varying image sizes and resolutions and four comorbidity subgroups: collectively defined as the diabetic retinopathy, hypertension, age-related macular degeneration, and Glaucoma image set (DR HAGIS). For each image, the vasculature has been manually segmented to provide a realistic set of images on which to test automatic vessel extraction algorithms. Modified versions of two previously published vessel extraction algorithms were applied to this database to provide some baseline measurements. A method based purely on the intensity of images pixels resulted in a mean segmentation accuracy of 95.83% ([Formula: see text]), whereas an algorithm based on Gabor filters generated an accuracy of 95.71% ([Formula: see text]).

  16. Algorithms and Algorithmic Languages.

    Science.gov (United States)

    Veselov, V. M.; Koprov, V. M.

    This paper is intended as an introduction to a number of problems connected with the description of algorithms and algorithmic languages, particularly the syntaxes and semantics of algorithmic languages. The terms "letter, word, alphabet" are defined and described. The concept of the algorithm is defined and the relation between the algorithm and…

  17. Intracoronary Compared to Intravenous Bolus Abciximab during Primary Percutaneous Coronary Intervention in ST-segment Elevation Myocardial Infarction (STEMI) Patients Reduces 30-day Mortality and Target Vessel Revascularization: A Randomized Trial

    DEFF Research Database (Denmark)

    Iversen, Allan; Abildgaard, Ulrik; Galloe, Anders

    2011-01-01

    patients who underwent pPCI and had indication for abciximab to either IV or IC bolus followed by a 12-hour IV infusion. Primary end-points at 30 days were target vessel revascularization (TVR), recurrent myocardial infarction (MI) or death, and the composite of the three. Secondary end-points were...... bleeding complications. Results: The two groups (IV n = 170;IC n = 185) were similar with respect to baseline characteristics. Mortality at 30 days was 5.3% in the IV group compared to only 1.1% in the IC group (P = 0.02). TVR was performed in 9.4% in the IV group compared to 3.8% in the IC group (P = 0...... bleedings (IV 14.1% vs. IC 9.7%; P = 0.20). Conclusion: IC administration of bolus abciximab in STEMI patients undergoing pPCI reduces 30-day mortality and TVR and tends to reduce MI, compared to IV-bolus. (J Interven Cardiol 2011;24:105-111)....

  18. Optic disc segmentation for glaucoma screening system using fundus images

    Directory of Open Access Journals (Sweden)

    Almazroa A

    2017-11-01

    Full Text Available Ahmed Almazroa,1,2 Weiwei Sun,3 Sami Alodhayb,4 Kaamran Raahemifar,5 Vasudevan Lakshminarayanan6 1King Abdullah International Medical Research Center (KAIMRC, Riyadh, Saudi Arabia; 2Ophthalmology and Visual Science Department, University of Michigan, Ann Arbor, MI, USA; 3School of Resource and Environmental Sciences, Wuhan University, Wuchang, Wuhan, Hubei, China; 4Bin Rushed Ophthalmic Center, Riyadh, Saudi Arabia; 5Department of Electrical and Computer Engineering, University of Ryerson, Toronto, ON, 6School of Optometry, University of Waterloo, ON, Canada Abstract: Segmenting the optic disc (OD is an important and essential step in creating a frame of reference for diagnosing optic nerve head pathologies such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of optic nerve head abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique was applied. As well an important contribution was to involve the variations in opinions among the ophthalmologists in detecting the disc boundaries and diagnosing the glaucoma. Most of the previous studies were trained and tested based on only one opinion, which can be assumed to be biased for the ophthalmologist. In addition, the accuracy was calculated based on the number of images that coincided with the ophthalmologists’ agreed-upon images, and not only on the overlapping images as in previous studies. The ultimate goal of this project is to develop an automated image processing system for glaucoma screening. The disc algorithm is evaluated using a new retinal fundus image dataset called RIGA (retinal images for glaucoma analysis. In the case of low-quality images, a double level set was applied, in which the first level set was considered to be localization

  19. A Searching Method of Candidate Segmentation Point in SPRINT Classification

    Directory of Open Access Journals (Sweden)

    Zhihao Wang

    2016-01-01

    Full Text Available SPRINT algorithm is a classical algorithm for building a decision tree that is a widely used method of data classification. However, the SPRINT algorithm has high computational cost in the calculation of attribute segmentation. In this paper, an improved SPRINT algorithm is proposed, which searches better candidate segmentation point for the discrete and continuous attributes. The experiment results demonstrate that the proposed algorithm can reduce the computation cost and improve the efficiency of the algorithm by improving the segmentation of continuous attributes and discrete attributes.

  20. Guam Abandoned Vessel Inventory

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Guam. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral habitats...

  1. Florida Abandoned Vessel Inventory

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Florida. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral...

  2. Vessel Arrival Info - Legacy

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Vessel Arrival Info is a spreadsheet that gets filled out during the initial stage of the debriefing process by the debriefer. It contains vessel name, trip...

  3. Segmentation: Identification of consumer segments

    DEFF Research Database (Denmark)

    Høg, Esben

    2005-01-01

    It is very common to categorise people, especially in the advertising business. Also traditional marketing theory has taken in consumer segments as a favorite topic. Segmentation is closely related to the broader concept of classification. From a historical point of view, classification has its...... a basic understanding of grouping people. Advertising agencies may use segmentation totarget advertisements, while food companies may usesegmentation to develop products to various groups of consumers. MAPP has for example investigated the positioning of fish in relation to other food products....... The traditionalists are characterised by favouring pork, poultry and beef. Since it is difficult to change consumers' tastes, the short-term consequence may be to focus on the "fish lovers" and target the communication towards these consumers. In the long run, "traditionalists" may be persuaded to revise...

  4. MOVING WINDOW SEGMENTATION FRAMEWORK FOR POINT CLOUDS

    Directory of Open Access Journals (Sweden)

    G. Sithole

    2012-07-01

    Full Text Available As lidar point clouds become larger streamed processing becomes more attractive. This paper presents a framework for the streamed segmentation of point clouds with the intention of segmenting unstructured point clouds in real-time. The framework is composed of two main components. The first component segments points within a window shifting over the point cloud. The second component stitches the segments within the windows together. In this fashion a point cloud can be streamed through these two components in sequence, thus producing a segmentation. The algorithm has been tested on airborne lidar point cloud and some results of the performance of the framework are presented.

  5. ALICE HMPID Radiator Vessel

    CERN Multimedia

    2003-01-01

    View of the radiator vessels of the ALICE/HMPID mounted on the support frame. Each HMPID module is equipped with 3 indipendent radiator vessels made out of neoceram and fused silica (quartz) windows glued together. The spacers inside the vessel are needed to stand the hydrostatic pressure. http://alice-hmpid.web.cern.ch/alice-hmpid

  6. Curvature affects Doppler investigation of vessels: implications for clinical practice.

    Science.gov (United States)

    Balbis, S; Roatta, S; Guiot, C

    2005-01-01

    In clinical practice, blood velocity estimations from Doppler examination of curved vascular segments are normally different from those of nearby straight segments. The observed "accelerations," sometimes considered as a sort of stochastic disturbances, can actually be related to very specific physical effects due to vessel curvature (i.e., the development of nonaxial velocity [NAV] components) and the spreading of the axial velocity direction in the Doppler sample volume with respect to the insonation axis. The relevant phenomena and their dependence on the radius of curvature of the vessels and on the insonation angle are investigated with a beam-vessel geometry as close as possible to clinical setting, with the simplifying assumptions of steady flow, mild vessel curvature, uniform ultrasonic beam and complete vessel insonation. The insonation angles that minimize the errors are provided on the basis of the study results.

  7. Retina image–based optic disc segmentation

    Directory of Open Access Journals (Sweden)

    Ching-Lin Wang

    2016-05-01

    Full Text Available The change of optic disc can be used to diagnose many eye diseases, such as glaucoma, diabetic retinopathy and macular degeneration. Moreover, retinal blood vessel pattern is unique for human beings even for identical twins. It is a highly stable pattern in biometric identification. Since optic disc is the beginning of the optic nerve and main blood vessels in retina, it can be used as a reference point of identification. Therefore, optic disc segmentation is an important technique for developing a human identity recognition system and eye disease diagnostic system. This article hence presents an optic disc segmentation method to extract the optic disc from a retina image. The experimental results show that the optic disc segmentation method can give impressive results in segmenting the optic disc from a retina image.

  8. Single vessel air injection estimates of xylem resistance to cavitation are affected by vessel network characteristics and sample length.

    Science.gov (United States)

    Venturas, Martin D; Rodriguez-Zaccaro, F Daniela; Percolla, Marta I; Crous, Casparus J; Jacobsen, Anna L; Pratt, R Brandon

    2016-10-01

    Xylem resistance to cavitation is an important trait that is related to the ecology and survival of plant species. Vessel network characteristics, such as vessel length and connectivity, could affect the spread of emboli from gas-filled vessels to functional ones, triggering their cavitation. We hypothesized that the cavitation resistance of xylem vessels is randomly distributed throughout the vessel network. We predicted that single vessel air injection (SVAI) vulnerability curves (VCs) would thus be affected by sample length. Longer stem samples were predicted to appear more resistant than shorter samples due to the sampled path including greater numbers of vessels. We evaluated the vessel network characteristics of grapevine (Vitis vinifera L.), English oak (Quercus robur L.) and black cottonwood (Populus trichocarpa Torr. & A. Gray), and constructed SVAI VCs for 5- and 20-cm-long segments. We also constructed VCs with a standard centrifuge method and used computer modelling to estimate the curve shift expected for pathways composed of different numbers of vessels. For all three species, the SVAI VCs for 5 cm segments rose exponentially and were more vulnerable than the 20 cm segments. The 5 cm curve shapes were exponential and were consistent with centrifuge VCs. Modelling data supported the observed SVAI VC shifts, which were related to path length and vessel network characteristics. These results suggest that exponential VCs represent the most realistic curve shape for individual vessel resistance distributions for these species. At the network level, the presence of some vessels with a higher resistance to cavitation may help avoid emboli spread during tissue dehydration. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  9. Integrated active contours for texture segmentation.

    Science.gov (United States)

    Sagiv, Chen; Sochen, Nir A; Zeevi, Yehoshua Y

    2006-06-01

    We address the issue of textured image segmentation in the context of the Gabor feature space of images. Gabor filters tuned to a set of orientations, scales and frequencies are applied to the images to create the Gabor feature space. A two-dimensional Riemannian manifold of local features is extracted via the Beltrami framework. The metric of this surface provides a good indicator of texture changes and is used, therefore, in a Beltrami-based diffusion mechanism and in a geodesic active contours algorithm for texture segmentation. The performance of the proposed algorithm is compared with that of the edgeless active contours algorithm applied for texture segmentation. Moreover, an integrated approach, extending the geodesic and edgeless active contours approaches to texture segmentation, is presented. We show that combining boundary and region information yields more robust and accurate texture segmentation results.

  10. Characterization of human retinal vessel arborisation in normal and amblyopic eyes using multifractal analysis

    Directory of Open Access Journals (Sweden)

    Stefan Tălu

    2015-10-01

    Full Text Available AIM:To characterize the human retinal vessel arborisation in normal and amblyopic eyes using multifractal geometry and lacunarity parameters.METHODS:Multifractal analysis using a box counting algorithm was carried out for a set of 12 segmented and skeletonized human retinal images, corresponding to both normal (6 images and amblyopia states of the retina (6 images.RESULTS:It was found that the microvascular geometry of the human retina network represents geometrical multifractals, characterized through subsets of regions having different scaling properties that are not evident in the fractal analysis. Multifractal analysis of the amblyopia images (segmented and skeletonized versions show a higher average of the generalized dimensions (Dq for q=0, 1, 2 indicating a higher degree of the tree-dimensional complexity associated with the human retinal microvasculature network whereas images of healthy subjects show a lower value of generalized dimensions indicating normal complexity of biostructure. On the other hand, the lacunarity analysis of the amblyopia images (segmented and skeletonized versions show a lower average of the lacunarity parameter Λ than the corresponding values for normal images (segmented and skeletonized versions.CONCLUSION:The multifractal and lacunarity analysis may be used as a non-invasive predictive complementary tool to distinguish amblyopic subjects from healthy subjects and hence this technique could be used for an early diagnosis of patients with amblyopia.

  11. MUSCLE MRI SEGMENTATION USING RANDOM WALKER METHOD

    Directory of Open Access Journals (Sweden)

    A. V. Shukelovich

    2013-01-01

    Full Text Available A technique of marker set construction for muscle MRI segmentation using random walker approach is introduced. The possibility of clinician’s manual labor amount reduction and random walker algorithm optimization is studied.

  12. Discriminative parameter estimation for random walks segmentation.

    Science.gov (United States)

    Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

    2013-01-01

    The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

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

    Science.gov (United States)

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

    2008-02-01

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

  14. A contrario line segment detection

    CERN Document Server

    von Gioi, Rafael Grompone

    2014-01-01

    The reliable detection of low-level image structures is an old and still challenging problem in computer vision. This?book leads a detailed tour through the LSD algorithm, a line segment detector designed to be fully automatic. Based on the a contrario framework, the algorithm works efficiently without the need of any parameter tuning. The design criteria are thoroughly explained and the algorithm's good and bad results are illustrated on real and synthetic images. The issues involved, as well as the strategies used, are common to many geometrical structure detection problems and some possible

  15. Optimally segmented magnetic structures

    DEFF Research Database (Denmark)

    Insinga, Andrea Roberto; Bahl, Christian; Bjørk, Rasmus

    We present a semi-analytical algorithm for magnet design problems, which calculates the optimal way to subdivide a given design region into uniformly magnetized segments.The availability of powerful rare-earth magnetic materials such as Nd-Fe-B has broadened the range of applications of permanent...... is not available.We will illustrate the results for magnet design problems from different areas, such as electric motors/generators (as the example in the picture), beam focusing for particle accelerators and magnetic refrigeration devices....... magnets[1][2]. However, the powerful rare-earth magnets are generally expensive, so both the scientific and industrial communities have devoted a lot of effort into developing suitable design methods. Even so, many magnet optimization algorithms either are based on heuristic approaches[3...

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

    Directory of Open Access Journals (Sweden)

    Xu Zhiliang

    2010-01-01

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

  17. A geometric flow for segmenting vasculature in proton-density weighted MRI.

    Science.gov (United States)

    Descoteaux, Maxime; Collins, D Louis; Siddiqi, Kaleem

    2008-08-01

    Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient's cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton-density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. In this paper we develop a novel geometric flow for segmenting vasculature in PD images, which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. Obtaining vasculature from PD data is of clinical interest since the acquisition of such images is widespread, the scanning process is non-invasive, and the availability of vessel segmentation methods could obviate the need for an additional angiographic or contrast-based sequence during preoperative imaging. The key idea is to first apply Frangi's vesselness measure [Frangi, A., Niessen, W., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer Assisted Intervention, vol. 1496 of Lecture Notes in Computer Science, pp. 130-137] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which allows the flux maximizing flow algorithm of Vasilevskiy and Siddiqi [Vasilevskiy, A., Siddiqi, K., 2002. Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (12), 1565-1578] to be applied to recover vessel boundaries. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We validate the approach quantitatively on a single-subject data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, with an expert segmented version of the TOF volume viewed as the ground truth. We then

  18. Cluster Ensemble-Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang

    2013-07-01

    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  19. Speech Segmentation Using Bayesian Autoregressive Changepoint Detector

    Directory of Open Access Journals (Sweden)

    P. Sovka

    1998-12-01

    Full Text Available This submission is devoted to the study of the Bayesian autoregressive changepoint detector (BCD and its use for speech segmentation. Results of the detector application to autoregressive signals as well as to real speech are given. BCD basic properties are described and discussed. The novel two-step algorithm consisting of cepstral analysis and BCD for automatic speech segmentation is suggested.

  20. Independent histogram pursuit for segmentation of skin lesions

    DEFF Research Database (Denmark)

    Gomez, D.D.; Butakoff, C.; Ersbøll, Bjarne Kjær

    2008-01-01

    In this paper, an unsupervised algorithm, called the Independent Histogram Pursuit (HIP), for segmenting dermatological lesions is proposed. The algorithm estimates a set of linear combinations of image bands that enhance different structures embedded in the image. In particular, the first...... to deal with different types of dermatological lesions. The boundary detection precision using k-means segmentation was close to 97%. The proposed algorithm can be easily combined with the majority of classification algorithms....

  1. Mixed segmentation

    DEFF Research Database (Denmark)

    Bonde, Anders; Aagaard, Morten; Hansen, Allan Grutt

    content analysis and audience segmentation in a single-source perspective. The aim is to explain and understand target groups in relation to, on the one hand, emotional response to commercials or other forms of audio-visual communication and, on the other hand, living preferences and personality traits....... Innovatively, the research process is documented via an interactive data-visualization tool by which readers and fellow peers can access and, by using various filtering options, further analyze the results and, ultimately, reformulate the problem field....

  2. [Segmental neurofibromatosis].

    Science.gov (United States)

    Wagner, G; Meyer, V; Sachse, M M

    2017-11-08

    Thirteen years ago, a 48-year-old man developed numerous neurofibromas in a circumscribed area on the right chest. At the same time, a bilateral seminoma was diagnosed and treated curatively. There was no evidence for other complications of neurofibromatosis. The family history was inconspicuous. The segmental neurofibromatosis (SN) presented in this patient is the result of a mosaic formation resulting from a mutation of the NF1 gene, a tumor suppressor gene. Concomitant, typical diseases of neurofibromatosis generalisata (NFG), including malignant neoplasms, are the exception to SN.

  3. Segmental neurofibromatosis.

    Science.gov (United States)

    Sobjanek, Michał; Dobosz-Kawałko, Magdalena; Michajłowski, Igor; Pęksa, Rafał; Nowicki, Roman

    2014-12-01

    Segmental neurofibromatosis or type V neurofibromatosis is a rare genodermatosis characterized by neurofibromas, café-au-lait spots and neurofibromas limited to a circumscribed body region. The disease may be associated with systemic involvement and malignancies. The disorder has not been reported yet in the Polish medical literature. A 63-year-old Caucasian woman presented with a 20-year history of multiple, flesh colored, dome-shaped, soft to firm nodules situated in the right lumbar region. A histopathologic evaluation of three excised tumors revealed neurofibromas. No neurological and ophthalmologic symptoms of neurofibromatosis were diagnosed.

  4. MULTISCALE SEGMENTATION OF POLARIMETRIC SAR IMAGE BASED ON SRM SUPERPIXELS

    Directory of Open Access Journals (Sweden)

    F. Lang

    2016-06-01

    Full Text Available Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT segmentation algorithm by combining the generalized statistical region merging (GSRM algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.

  5. Pressure vessel design manual

    CERN Document Server

    Moss, Dennis R

    2013-01-01

    Pressure vessels are closed containers designed to hold gases or liquids at a pressure substantially different from the ambient pressure. They have a variety of applications in industry, including in oil refineries, nuclear reactors, vehicle airbrake reservoirs, and more. The pressure differential with such vessels is dangerous, and due to the risk of accident and fatality around their use, the design, manufacture, operation and inspection of pressure vessels is regulated by engineering authorities and guided by legal codes and standards. Pressure Vessel Design Manual is a solutions-focused guide to the many problems and technical challenges involved in the design of pressure vessels to match stringent standards and codes. It brings together otherwise scattered information and explanations into one easy-to-use resource to minimize research and take readers from problem to solution in the most direct manner possible. * Covers almost all problems that a working pressure vessel designer can expect to face, with ...

  6. Maury Journals - German Vessels

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — German vessels observations, after the 1853 Brussels Conference that set International Maritime Standards, modeled after Maury Marine Standard Observations.

  7. Unsupervised Segmentation Methods of TV Contents

    Directory of Open Access Journals (Sweden)

    Elie El-Khoury

    2010-01-01

    Full Text Available We present a generic algorithm to address various temporal segmentation topics of audiovisual contents such as speaker diarization, shot, or program segmentation. Based on a GLR approach, involving the ΔBIC criterion, this algorithm requires the value of only a few parameters to produce segmentation results at a desired scale and on most typical low-level features used in the field of content-based indexing. Results obtained on various corpora are of the same quality level than the ones obtained by other dedicated and state-of-the-art methods.

  8. Segmentation of sows in farrowing pens

    DEFF Research Database (Denmark)

    Tu, Gang Jun; Karstoft, Henrik; Pedersen, Lene Juul

    2014-01-01

    The correct segmentation of a foreground object in video recordings is an important task for many surveillance systems. The development of an effective and practical algorithm to segment sows in grayscale video recordings captured under commercial production conditions is described...... and illumination changes as well as motionless foreground objects. About 97% of the segmented binary images in the validation data sets can be used to track sow behaviours, such as position, orientation and movement. The experimental results demonstrate that the proposed algorithm is able to provide a basis...

  9. Liver segmentation in color images (Conference Presentation)

    Science.gov (United States)

    Ma, Burton; Kingham, T. Peter; Miga, Michael I.; Jarnagin, William R.; Simpson, Amber L.

    2017-03-01

    We describe the use of a deep learning method for semantic segmentation of the liver from color images. Our intent is to eventually embed a semantic segmentation method into a stereo-vision based navigation system for open liver surgery. Semantic segmentation of the stereo images will allow us to reconstruct a point cloud containing the liver surfaces and excluding all other non-liver structures. We trained a deep learning algorithm using 136 images and 272 augmented images computed by rotating the original images. We tested the trained algorithm on 27 images that were not used for training purposes. The method achieves an 88% median pixel labeling accuracy over the test images.

  10. Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting.

    Science.gov (United States)

    Ross, James C; Kindlmann, Gordon L; Okajima, Yuka; Hatabu, Hiroto; Díaz, Alejandro A; Silverman, Edwin K; Washko, George R; Dy, Jennifer; San José Estépar, Raúl

    2013-12-01

    Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. The proposed algorithm is effective for lung lobe

  11. Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

    Energy Technology Data Exchange (ETDEWEB)

    Ross, James C., E-mail: jross@bwh.harvard.edu [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States); Kindlmann, Gordon L. [Computer Science Department and Computation Institute, University of Chicago, Chicago, Illinois 60637 (United States); Okajima, Yuka; Hatabu, Hiroto [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Díaz, Alejandro A. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 and Department of Pulmonary Diseases, Pontificia Universidad Católica de Chile, Santiago (Chile); Silverman, Edwin K. [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 and Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Washko, George R. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Dy, Jennifer [ECE Department, Northeastern University, Boston, Massachusetts 02115 (United States); Estépar, Raúl San José [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States)

    2013-12-15

    Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The

  12. Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

    Science.gov (United States)

    Raudaschl, Patrik F; Zaffino, Paolo; Sharp, Gregory C; Spadea, Maria Francesca; Chen, Antong; Dawant, Benoit M; Albrecht, Thomas; Gass, Tobias; Langguth, Christoph; Lüthi, Marcel; Jung, Florian; Knapp, Oliver; Wesarg, Stefan; Mannion-Haworth, Richard; Bowes, Mike; Ashman, Annaliese; Guillard, Gwenael; Brett, Alan; Vincent, Graham; Orbes-Arteaga, Mauricio; Cárdenas-Peña, David; Castellanos-Dominguez, German; Aghdasi, Nava; Li, Yangming; Berens, Angelique; Moe, Kris; Hannaford, Blake; Schubert, Rainer; Fritscher, Karl D

    2017-05-01

    Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms. © 2017 American Association of Physicists in Medicine.

  13. On the evaluation of segmentation editing tools

    Science.gov (United States)

    Heckel, Frank; Moltz, Jan H.; Meine, Hans; Geisler, Benjamin; Kießling, Andreas; D’Anastasi, Melvin; dos Santos, Daniel Pinto; Theruvath, Ashok Joseph; Hahn, Horst K.

    2014-01-01

    Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings. PMID:26158063

  14. Optimally segmented permanent magnet structures

    DEFF Research Database (Denmark)

    Insinga, Andrea Roberto; Bjørk, Rasmus; Smith, Anders

    2016-01-01

    We present an optimization approach which can be employed to calculate the globally optimal segmentation of a two-dimensional magnetic system into uniformly magnetized pieces. For each segment the algorithm calculates the optimal shape and the optimal direction of the remanent flux density vector......, with respect to a linear objective functional. We illustrate the approach with results for magnet design problems from different areas, such as a permanent magnet electric motor, a beam focusing quadrupole magnet for particle accelerators and a rotary device for magnetic refrigeration....

  15. A Hybrid Technique for Medical Image Segmentation

    Directory of Open Access Journals (Sweden)

    Alamgir Nyma

    2012-01-01

    Full Text Available Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

  16. WORD BASED TAMIL SPEECH RECOGNITION USING TEMPORAL FEATURE BASED SEGMENTATION

    Directory of Open Access Journals (Sweden)

    A. Akila

    2015-05-01

    Full Text Available Speech recognition system requires segmentation of speech waveform into fundamental acoustic units. Segmentation is a process of decomposing the speech signal into smaller units. Speech segmentation could be done using wavelet, fuzzy methods, Artificial Neural Networks and Hidden Markov Model. Speech segmentation is a process of breaking continuous stream of sound into some basic units like words, phonemes or syllable that could be recognized. Segmentation could be used to distinguish different types of audio signals from large amount of audio data, often referred as audio classification. The speech segmentation can be divided into two categories based on whether the algorithm uses previous knowledge of data to process the speech. The categories are blind segmentation and aided segmentation.The major issues with the connected speech recognition algorithms were the vocabulary size will be larger with variation in the combination of words in the connected speech and the complexity of the algorithm is more to find the best match for the given test pattern. To overcome these issues, the connected speech has to be segmented into words using the attributes of speech. A methodology using the temporal feature Short Term Energy was proposed and compared with an existing algorithm called Dynamic Thresholding segmentation algorithm which uses spectrogram image of the connected speech for segmentation.

  17. Bayesian segmentation of brainstem structures in MRI

    DEFF Research Database (Denmark)

    Iglesias, Juan Eugenio; Van Leemput, Koen; Bhatt, Priyanka

    2015-01-01

    In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we...... the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy...

  18. Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method

    Directory of Open Access Journals (Sweden)

    Yun Tian

    2016-01-01

    Full Text Available The segmentation of coronary arteries is a vital process that helps cardiovascular radiologists detect and quantify stenosis. In this paper, we propose a fully automated coronary artery segmentation from cardiac data volume. The method is built on a statistics region growing together with a heuristic decision. First, the heart region is extracted using a multi-atlas-based approach. Second, the vessel structures are enhanced via a 3D multiscale line filter. Next, seed points are detected automatically through a threshold preprocessing and a subsequent morphological operation. Based on the set of detected seed points, a statistics-based region growing is applied. Finally, results are obtained by setting conservative parameters. A heuristic decision method is then used to obtain the desired result automatically because parameters in region growing vary in different patients, and the segmentation requires full automation. The experiments are carried out on a dataset that includes eight-patient multivendor cardiac computed tomography angiography (CTA volume data. The DICE similarity index, mean distance, and Hausdorff distance metrics are employed to compare the proposed algorithm with two state-of-the-art methods. Experimental results indicate that the proposed algorithm is capable of performing complete, robust, and accurate extraction of coronary arteries.

  19. PRESSURE-RESISTANT VESSEL

    NARCIS (Netherlands)

    Beukers, A.; De Jong, T.

    1997-01-01

    Abstract of WO 9717570 (A1) The invention is directed to a wheel-shaped pressure-resistant vessel for gaseous, liquid or liquefied material having a substantially rigid shape, said vessel comprising a substantially continuous shell of a fiber-reinforced resin having a central opening, an inner

  20. Containment vessel drain system

    Energy Technology Data Exchange (ETDEWEB)

    Harris, Scott G.

    2018-01-30

    A system for draining a containment vessel may include a drain inlet located in a lower portion of the containment vessel. The containment vessel may be at least partially filled with a liquid, and the drain inlet may be located below a surface of the liquid. The system may further comprise an inlet located in an upper portion of the containment vessel. The inlet may be configured to insert pressurized gas into the containment vessel to form a pressurized region above the surface of the liquid, and the pressurized region may operate to apply a surface pressure that forces the liquid into the drain inlet. Additionally, a fluid separation device may be operatively connected to the drain inlet. The fluid separation device may be configured to separate the liquid from the pressurized gas that enters the drain inlet after the surface of the liquid falls below the drain inlet.

  1. Computer Aided Segmentation Analysis: New Software for College Admissions Marketing.

    Science.gov (United States)

    Lay, Robert S.; Maguire, John J.

    1983-01-01

    Compares segmentation solutions obtained using a binary segmentation algorithm (THAID) and a new chi-square-based procedure (CHAID) that segments the prospective pool of college applicants using application and matriculation as criteria. Results showed a higher number of estimated qualified inquiries and more accurate estimates with CHAID. (JAC)

  2. Active mask segmentation of fluorescence microscope images.

    Science.gov (United States)

    Srinivasa, Gowri; Fickus, Matthew C; Guo, Yusong; Linstedt, Adam D; Kovacević, Jelena

    2009-08-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.

  3. Retinal vessel detection and measurement for computer-aided medical diagnosis.

    Science.gov (United States)

    Li, Xiaokun; Wee, William G

    2014-02-01

    Since blood vessel detection and characteristic measurement for ocular retinal images is a fundamental problem in computer-aided medical diagnosis, automated algorithms/systems for vessel detection and measurement are always demanded. To support computer-aided diagnosis, an integrated approach/solution for vessel detection and diameter measurement is presented and validated. In the proposed approach, a Dempster-Shafer (D-S)-based edge detector is developed to obtain initial vessel edge information and an accurate vascular map for a retinal image. Then, the appropriate path and the centerline of a vessel of interest are identified automatically through graph search. Once the vessel path has been identified, the diameter of the vessel will be measured accordingly by the algorithm in real time. To achieve more accurate edge detection and diameter measurement, mixed Gaussian-matched filters are designed to refine the initial detection and measures. Other important medical indices of retinal vessels can also be calculated accordingly based on detection and measurement results. The efficiency of the proposed algorithm was validated by the retinal images obtained from different public databases. Experimental results show that the vessel detection rate of the algorithm is 100 % for large vessels and 89.9 % for small vessels, and the error rate on vessel diameter measurement is less than 5 %, which are all well within the acceptable range of deviation among the human graders.

  4. Algorithming the Algorithm

    DEFF Research Database (Denmark)

    Mahnke, Martina; Uprichard, Emma

    2014-01-01

    changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...... would like to emphasize another side to the algorithmic everyday life. We argue that algorithms can instigate and facilitate imagination, creativity, and frivolity, while saying something that is simultaneously old and new, always almost repeating what was before but never quite returning. We show...... this by threading together stimulating quotes and screenshots from Google’s autocomplete algorithms. In doing so, we invite the reader to re-explore Google’s autocomplete algorithms in a creative, playful, and reflexive way, thereby rendering more visible some of the excitement and frivolity that comes from being...

  5. Optimization of Segmentation Quality of Integrated Circuit Images

    Directory of Open Access Journals (Sweden)

    Gintautas Mušketas

    2012-04-01

    Full Text Available The paper presents investigation into the application of genetic algorithms for the segmentation of the active regions of integrated circuit images. This article is dedicated to a theoretical examination of the applied methods (morphological dilation, erosion, hit-and-miss, threshold and describes genetic algorithms, image segmentation as optimization problem. The genetic optimization of the predefined filter sequence parameters is carried out. Improvement to segmentation accuracy using a non optimized filter sequence makes 6%.Artcile in Lithuanian

  6. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    The proposed methodology was evaluated on a publicly available database, STARE. The results reported ... This methodology can help ophthalmologists in better and faster analysis and hence early treatment to the patients. ... Department of Information Science and Technology, Anna University, Chennai 600025, India ...

  7. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    Abstract. Machine Learning techniques have been useful in almost every field of concern. Data Mining, a branch of Machine Learning is one of the most extensively used techniques. The ever-increasing demands in the field of medicine are being addressed by computational approaches in which Big Data analysis, image ...

  8. Text Segmentation Using Exponential Models

    CERN Document Server

    Beeferman, D; Lafferty, G D; Beeferman, Doug; Berger, Adam; Lafferty, John

    1997-01-01

    This paper introduces a new statistical approach to partitioning text automatically into coherent segments. Our approach enlists both short-range and long-range language models to help it sniff out likely sites of topic changes in text. To aid its search, the system consults a set of simple lexical hints it has learned to associate with the presence of boundaries through inspection of a large corpus of annotated data. We also propose a new probabilistically motivated error metric for use by the natural language processing and information retrieval communities, intended to supersede precision and recall for appraising segmentation algorithms. Qualitative assessment of our algorithm as well as evaluation using this new metric demonstrate the effectiveness of our approach in two very different domains, Wall Street Journal articles and the TDT Corpus, a collection of newswire articles and broadcast news transcripts.

  9. CDIS: Circle Density Based Iris Segmentation

    Science.gov (United States)

    Gupta, Anand; Kumari, Anita; Kundu, Boris; Agarwal, Isha

    Biometrics is an automated approach of measuring and analysing physical and behavioural characteristics for identity verification. The stability of the Iris texture makes it a robust biometric tool for security and authentication purposes. Reliable Segmentation of Iris is a necessary precondition as an error at this stage will propagate into later stages and requires proper segmentation of non-ideal images having noises like eyelashes, etc. Iris Segmentation work has been done earlier but we feel it lacks in detecting iris in low contrast images, removal of specular reflections, eyelids and eyelashes. Hence, it motivates us to enhance the said parameters. Thus, we advocate a new approach CDIS for Iris segmentation along with new algorithms for removal of eyelashes, eyelids and specular reflections and pupil segmentation. The results obtained have been presented using GAR vs. FAR graphs at the end and have been compared with prior works related to segmentation of iris.

  10. Algorithms Introduction to Algorithms

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 1. Algorithms Introduction to Algorithms. R K Shyamasundar. Series Article Volume 1 Issue 1 January 1996 pp 20-27. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/001/01/0020-0027 ...

  11. 2013 Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  12. 2011 Passenger Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  13. 2011 Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  14. 2013 Passenger Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  15. 2013 Tanker Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  16. 2013 Cargo Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  17. High Performance Marine Vessels

    CERN Document Server

    Yun, Liang

    2012-01-01

    High Performance Marine Vessels (HPMVs) range from the Fast Ferries to the latest high speed Navy Craft, including competition power boats and hydroplanes, hydrofoils, hovercraft, catamarans and other multi-hull craft. High Performance Marine Vessels covers the main concepts of HPMVs and discusses historical background, design features, services that have been successful and not so successful, and some sample data of the range of HPMVs to date. Included is a comparison of all HPMVs craft and the differences between them and descriptions of performance (hydrodynamics and aerodynamics). Readers will find a comprehensive overview of the design, development and building of HPMVs. In summary, this book: Focuses on technology at the aero-marine interface Covers the full range of high performance marine vessel concepts Explains the historical development of various HPMVs Discusses ferries, racing and pleasure craft, as well as utility and military missions High Performance Marine Vessels is an ideal book for student...

  18. Cheboygan Vessel Base

    Data.gov (United States)

    Federal Laboratory Consortium — Cheboygan Vessel Base (CVB), located in Cheboygan, Michigan, is a field station of the USGS Great Lakes Science Center (GLSC). CVB was established by congressional...

  19. Maury Journals - US Vessels

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — U.S. vessels observations, after the 1853 Brussels Conference that set International Maritime Standards, modeled after Maury Marine Standard Observations.

  20. 2011 Cargo Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  1. 2011 Tanker Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  2. 2013 Fishing Vessel Density

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and...

  3. Coastal Logbook Survey (Vessels)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains catch (landed catch) and effort for fishing trips made by vessels that have been issued a Federal permit for the Gulf of Mexico reef fish,...

  4. Automated coronary CT angiography plaque-lumen segmentation

    Science.gov (United States)

    Cline, Harvey E.; Krishnan, Karthik; Napel, Sandy; Rubin, Geoffrey D.; Turner, Wesley D.; Avila, Ricardo S.

    2009-02-01

    We are investigating the feasibility of a computer-aided detection (CAD) system to assist radiologists in diagnosing coronary artery disease in ECG gated cardiac multi-detector CT scans having calcified plaque. Coronary artery stenosis analysis is challenging if calcified plaque or the iodinated blood pool hides viable lumen. The research described herein provides an improved presentation to the radiologist by removing obscuring calcified plaque and blood pool. The algorithm derives a Gaussian estimate of the point spread function (PSF) of the scanner responsible for plaque blooming by fitting measured CTA image profiles. An initial estimate of the extent of calcified plaque is obtained from the image evidence using a simple threshold. The Gaussian PSF estimate is then convolved with the initial plaque estimate to obtain an estimate of the extent of the blooming artifact and this plaque blooming image is subtracted from the CT image to obtain an image largely free of obscuring plaque. In a separate step, the obscuring blood pool is suppressed using morphological operations and adaptive region growing. After processing by our algorithm, we are able to project the segmented plaque-free lumen to form synthetic angiograms free from obstruction. We can also analyze the coronary arteries with vessel tracking and centerline extraction to produce cross sectional images for measuring lumen stenosis. As an additional aid to radiologists, we also produce plots of calcified plaque and lumen cross-sectional area along selected blood vessels. The method was validated using digital phantoms and actual patient data, including in one case, a validation against the results of a catheter angiogram.

  5. LANL Robotic Vessel Scanning

    Energy Technology Data Exchange (ETDEWEB)

    Webber, Nels W. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-11-25

    Los Alamos National Laboratory in J-1 DARHT Operations Group uses 6ft spherical vessels to contain hazardous materials produced in a hydrodynamic experiment. These contaminated vessels must be analyzed by means of a worker entering the vessel to locate, measure, and document every penetration mark on the vessel. If the worker can be replaced by a highly automated robotic system with a high precision scanner, it will eliminate the risks to the worker and provide management with an accurate 3D model of the vessel presenting the existing damage with the flexibility to manipulate the model for better and more in-depth assessment.The project was successful in meeting the primary goal of installing an automated system which scanned a 6ft vessel with an elapsed time of 45 minutes. This robotic system reduces the total time for the original scope of work by 75 minutes and results in excellent data accumulation and transmission to the 3D model imaging program.

  6. Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques

    OpenAIRE

    Prabhjot Kaur; Pallavi Gupta; Poonam Sharma

    2012-01-01

    This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy C-Means(FCM) algorithm, Kernel Fuzzy C-Means(KFCM), Intuitionistic Kernelized Fuzzy C-Means(KIFCM), Kernelized Type-II Fuzzy C-Means(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qualitatively. These algorithms are implemented on synthetic images in case of without noise along ...

  7. Volume Segmentation and Ghost Particles

    Science.gov (United States)

    Ziskin, Isaac; Adrian, Ronald

    2011-11-01

    Volume Segmentation Tomographic PIV (VS-TPIV) is a type of tomographic PIV in which images of particles in a relatively thick volume are segmented into images on a set of much thinner volumes that may be approximated as planes, as in 2D planar PIV. The planes of images can be analysed by standard mono-PIV, and the volume of flow vectors can be recreated by assembling the planes of vectors. The interrogation process is similar to a Holographic PIV analysis, except that the planes of image data are extracted from two-dimensional camera images of the volume of particles instead of three-dimensional holographic images. Like the tomographic PIV method using the MART algorithm, Volume Segmentation requires at least two cameras and works best with three or four. Unlike the MART method, Volume Segmentation does not require reconstruction of individual particle images one pixel at a time and it does not require an iterative process, so it operates much faster. As in all tomographic reconstruction strategies, ambiguities known as ghost particles are produced in the segmentation process. The effect of these ghost particles on the PIV measurement is discussed. This research was supported by Contract 79419-001-09, Los Alamos National Laboratory.

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

    Science.gov (United States)

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

    2015-03-01

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

  9. Strategic market segmentation

    National Research Council Canada - National Science Library

    Maricic, Branko; Djordjevic, Aleksandar

    2015-01-01

    ..., requires segmented approach to the market that appreciates differences in expectations and preferences of customers. One of significant activities in strategic planning of marketing activities is market segmentation...

  10. Segmentation of the foveal microvasculature using deep learning networks

    Science.gov (United States)

    Prentašić, Pavle; Heisler, Morgan; Mammo, Zaid; Lee, Sieun; Merkur, Andrew; Navajas, Eduardo; Beg, Mirza Faisal; Šarunić, Marinko; Lončarić, Sven

    2016-07-01

    Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.

  11. A Latent Source Model for Patch-Based Image Segmentation.

    Science.gov (United States)

    Chen, George H; Shah, Devavrat; Golland, Polina

    2015-10-01

    Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.

  12. Simplified labeling process for medical image segmentation.

    Science.gov (United States)

    Gao, Mingchen; Huang, Junzhou; Huang, Xiaolei; Zhang, Shaoting; Metaxas, Dimitris N

    2012-01-01

    Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms.

  13. ADAPTACIÓN DEL ALGORITMO MARACAS PARA SEGMENTACIÓN DE LA ARTERIA CARÓTIDA Y CUANTIFICACIÓN DE ESTENOSIS EN IMÁGENES TAC Adaptation of the MARACAS Algorithm for Carotid Artery Segmentation and Stenosis Quantification on CT Images

    Directory of Open Access Journals (Sweden)

    MARIA A ZULUAGA

    Full Text Available En este artículo se describen las adaptaciones hechas al algoritmo MARACAS para segmentar y cuantificar estructuras vasculares en imágenes TAC de la arteria carótida. El algoritmo MARACAS, que está basado en un modelo elástico y en un análisis de los valores y vectores propios de la matriz de inercia, fue inicialmente diseñado para segmentar una sola arteria en imágenes ARM. Las modificaciones están principalmente enfocadas a tratar las especificidades de las imágenes TAC, así como la presencia de bifurcaciones. Los algoritmos implementados en esta nueva versión se clasifican en dos niveles. 1. Los procesamientos de bajo nivel (filtrado de ruido y de artificios direccionales, presegmentación y realce destinados a mejorar la calidad de la imagen y presegmentarla. Estas técnicas están basadas en información a priori sobre el ruido, los artificios y los intervalos típicos de niveles de gris del lumen, del fondo y de las calcificaciones. 2. Los procesamientos de alto nivel para extraer la línea central de la arteria, segmentar el lumen y cuantificar la estenosis. A este nivel, se aplican conocimientos a priori sobre la forma y anatomía de las estructuras vasculares. El método fue evaluado en 31 imágenes suministradas en el concurso Carotid Lumen Segmentation and Stenosis Grading Grand Challenge 2009. Los resultados obtenidos en la segmentación arrojaron un coeficiente de similitud de Dice promedio de 80,4% comparado con la segmentación de referencia, y el error promedio de la cuantificación de estenosis fue 14,4%.This paper describes the adaptations of MARACAS algorithm to the segmentation and quantification of vascular structures in CTA images of the carotid artery. The MARACAS algorithm, which is based on an elastic model and on a multi-scale eigen-analysis of the inertia matrix, was originally designed to segment a single artery in MRA images. The modifications are primarily aimed at addressing the specificities of CT

  14. Efficient segmentation of skin epidermis in whole slide histopathological images.

    Science.gov (United States)

    Xu, Hongming; Mandal, Mrinal

    2015-01-01

    Segmentation of epidermis areas is an important step towards automatic analysis of skin histopathological images. This paper presents a robust technique for epidermis segmentation in whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then estimated by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is suspected to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques.

  15. Supervoxel Segmentation with Voxel-Related Gaussian Mixture Model.

    Science.gov (United States)

    Ban, Zhihua; Chen, Zhong; Liu, Jianguo

    2018-01-05

    Extended from superpixel segmentation by adding an additional constraint on temporal consistency, supervoxel segmentation is to partition video frames into atomic segments. In this work, we propose a novel scheme for supervoxel segmentation to address the problem of new and moving objects, where the segmentation is performed on every two consecutive frames and thus each internal frame has two valid superpixel segmentations. This scheme provides coarse-grained parallel ability, and subsequent algorithms can validate their result using two segmentations that will further improve robustness. To implement this scheme, a voxel-related Gaussian mixture model (GMM) is proposed, in which each supervoxel is assumed to be distributed in a local region and represented by two Gaussian distributions that share the same color parameters to capture temporal consistency. Our algorithm has a lower complexity with respect to frame size than the traditional GMM. According to our experiments, it also outperforms the state-of-the-art in accuracy.

  16. Towards Automatic Image Segmentation Using Optimised Region Growing Technique

    Science.gov (United States)

    Alazab, Mamoun; Islam, Mofakharul; Venkatraman, Sitalakshmi

    Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.

  17. What are the residual stresses doing in our blood vessels?

    Science.gov (United States)

    Fung, Y C

    1991-01-01

    We show that the residual strain and stress in the blood vessels are not zero, and that the zero-stress state of a blood vessel consists of open-sector segments whose opening angles vary along the longitudinal axis of the vessel. When the homeostatic state of the blood vessel is changed, e.g., by a sudden hypertension, the opening angle will change. The time constant of the opening angle change is a few hours (e.g., in the pulmonary artery) or a few days (e.g., in the aorta). From a kinematic point of view, a change of opening angle is a bending of the blood vessel wall, which is caused by a nonuniformly distributed residual strain. From a mechanics point of view, changes of blood pressure and residual strain cause change of stress in the blood vessel wall. Correlating the stress with the change of residual strain yields a fundamental biological law relating the rate of growth or resorption of tissue with the stress in the tissue. Thus, residual stresses are related to the remodeling of the blood vessel wall. Our blood vessel remodels itself when stress changes. The stress-growth law provides a biomechanical foundation for tissue engineering.

  18. Enhancing supply vessel safety

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2002-07-01

    A supply-vessel bridge installation consists of a navigating bridge and a control position aft, from which operators control the ship when close to rigs or platforms, and operate winches and other loading equipment. The international Convention for Safety of I Ale at Sea (SOLAS) does not regulate the layout, so design varies to a large degree, often causing an imperfect working environment. As for other types of ships, more than half the offshore service vessel accidents at sea are caused by bridge system failures. A majority can be traced back to technical design, and operational errors. The research and development project NAUT-OSV is a response to the offshore industry's safety concerns. Analysis of 24 incidents involving contact or collision between supply vessels and offshore installations owned or operated by Norwegian companies indicated that failures in the bridge system were often the cause.

  19. Evaluation of manual and automatic segmentation of the mouse heart from CINE MR images

    NARCIS (Netherlands)

    Heijman, Edwin; Aben, Jean-Paul; Penners, Cindy; Niessen, Petra; Guillaume, René; van Eys, Guillaume; Nicolay, Klaas; Strijkers, Gustav J.

    2008-01-01

    To compare global functional parameters determined from a stack of cinematographic MR images of mouse heart by a manual segmentation and an automatic segmentation algorithm. The manual and automatic segmentation results of 22 mouse hearts were compared. The automatic segmentation was based on

  20. Simultaneous segmentation and correspondence improvement using statistical modes

    Science.gov (United States)

    Sinha, Ayushi; Reiter, Austin; Leonard, Simon; Ishii, Masaru; Hager, Gregory D.; Taylor, Russell H.

    2017-02-01

    With the increasing amount of patient information that is being collected today, the idea of using this information to inform future patient care has gained momentum. In many cases, this information comes in the form of medical images. Several algorithms have been presented to automatically segment these images, and to extract structures relevant to different diagnostic or surgical procedures. Consequently, this allows us to obtain large data-sets of shapes, in the form of triangular meshes, segmented from these images. Given correspondences between these shapes, statistical shape models (SSMs) can be built using methods like Principal Component Analysis (PCA). Often, the initial correspondences between the shapes need to be improved, and SSMs can be used to improve these correspondences. However, just as often, initial segmentations also need to be improved. Unlike many correspondence improvement algorithms, which do not affect segmentation, many segmentation improvement algorithms negatively affect correspondences between shapes. We present a method that iteratively improves both segmentation as well as correspondence by using SSMs not only to improve correspondence, but also to constrain the movement of vertices during segmentation improvement. We show that our method is able to maintain correspondence while achieving as good or better segmentations than those produced by methods that improve segmentation without maintaining correspondence. We are additionally able to achieve segmentations with better triangle quality than segmentations produced without correspondence improvement.

  1. Improving image segmentation by learning region affinities

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.

    2010-11-03

    We utilize the context information of other regions in hierarchical image segmentation to learn new regions affinities. It is well known that a single choice of quantization of an image space is highly unlikely to be a common optimal quantization level for all categories. Each level of quantization has its own benefits. Therefore, we utilize the hierarchical information among different quantizations as well as spatial proximity of their regions. The proposed affinity learning takes into account higher order relations among image regions, both local and long range relations, making it robust to instabilities and errors of the original, pairwise region affinities. Once the learnt affinities are obtained, we use a standard image segmentation algorithm to get the final segmentation. Moreover, the learnt affinities can be naturally unutilized in interactive segmentation. Experimental results on Berkeley Segmentation Dataset and MSRC Object Recognition Dataset are comparable and in some aspects better than the state-of-art methods.

  2. Bayesian automated cortical segmentation for neonatal MRI

    Science.gov (United States)

    Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

    2017-11-01

    Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

  3. GOLD PRESSURE VESSEL SEAL

    Science.gov (United States)

    Smith, A.E.

    1963-11-26

    An improved seal between the piston and die member of a piston-cylinder type pressure vessel is presented. A layer of gold, of sufficient thickness to provide an interference fit between the piston and die member, is plated on the contacting surface of at least one of the members. (AEC)

  4. Network of endocardial vessels.

    Science.gov (United States)

    Lee, Byung-Cheon; Kim, Hong Bae; Sung, Baeckkyoung; Kim, Ki Woo; Sohn, Jamin; Son, Boram; Chang, Byung-Joon; Soh, Kwang-Sup

    2011-01-01

    Although there have been reports on threadlike structures inside the heart, they have received little attention. We aimed to develop a method for observing such structures and to reveal their ultrastructures. An in situ staining method, which uses a series of procedures of 0.2-0.4% trypan blue spraying and washing, was applied to observe threadlike structures on the surfaces of endocardia. The threadlike structures were isolated and observed by using confocal laser scanning microscopy (CLSM) and transmission electron microscopy (TEM). Networks of endocardial vessels (20 μm in thickness) with expansions (40-100 μm in diameter) were visualized; they were movable on the endocardium of the bovine atrium and ventricle. CLSM showed that (1) rod-shaped nuclei were aligned along the longitudinal direction of the endocardial vessel and (2) there were many cells inside the expansion. TEM on the endocardial vessel revealed that (1) there existed multiple lumens (1-7 μm in diameter) and (2) the extracellular matrices mostly consisted of collagen fibers, which were aligned along the longitudinal direction of the endocardial vessel or were locally organized in reticular structures. We investigated the endocardial circulatory system in bovine cardiac chambers and its ultrastructures, such as nucleic distributions, microlumens, and collagenous extracellular matrices. Copyright © 2011 S. Karger AG, Basel.

  5. Pressurized Vessel Slurry Pumping

    Energy Technology Data Exchange (ETDEWEB)

    Pound, C.R.

    2001-09-17

    This report summarizes testing of an alternate ''pressurized vessel slurry pumping'' apparatus. The principle is similar to rural domestic water systems and ''acid eggs'' used in chemical laboratories in that material is extruded by displacement with compressed air.

  6. Remote sensing image segmentation based on Hadoop cloud platform

    Science.gov (United States)

    Li, Jie; Zhu, Lingling; Cao, Fubin

    2018-01-01

    To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

  7. Sub-Markov Random Walk for Image Segmentation.

    Science.gov (United States)

    Dong, Xingping; Shen, Jianbing; Shao, Ling; Van Gool, Luc

    2016-02-01

    A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for transferring intrinsic findings between different RW algorithms, and offer new ideas for designing novel RW algorithms by adding or changing auxiliary nodes. To verify the second benefit, we design a new subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous RW algorithms for seeded image segmentation.

  8. Automatic segmentation of the lumen region in intravascular images of the coronary artery.

    Science.gov (United States)

    Jodas, Danilo Samuel; Pereira, Aledir Silveira; Tavares, João Manuel R S

    2017-08-01

    Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17  mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Segmentation of large images based on super-pixels and community detection in graphs

    OpenAIRE

    Linares, Oscar A. C.; Botelho, Glenda Michele; Rodrigues, Francisco Aparecido; Neto, João Batista

    2016-01-01

    Image segmentation has many applications which range from machine learning to medical diagnosis. In this paper, we propose a framework for the segmentation of images based on super-pixels and algorithms for community identification in graphs. The super-pixel pre-segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches, such as tho...

  10. A comparative study on medical image segmentation methods

    Directory of Open Access Journals (Sweden)

    Praylin Selva Blessy SELVARAJ ASSLEY

    2014-03-01

    Full Text Available Image segmentation plays an important role in medical images. It has been a relevant research area in computer vision and image analysis. Many segmentation algorithms have been proposed for medical images. This paper makes a review on segmentation methods for medical images. In this survey, segmentation methods are divided into five categories: region based, boundary based, model based, hybrid based and atlas based. The five different categories with their principle ideas, advantages and disadvantages in segmenting different medical images are discussed.

  11. Simultaneous Reconstruction and Segmentation with Class-Specific Priors

    DEFF Research Database (Denmark)

    Romanov, Mikhail

    Studying the interior of objects using tomography often require an image segmentation, such that different material properties can be quantified. This can for example be volume or surface area. Segmentation is typically done as an image analysis step after the image has been reconstructed....... This thesis investigates computing the reconstruction and segmentation simultaneously. The advantage of this is that because the reconstruction and segmentation are computed jointly, reconstruction errors are not propagated to the segmentation step. Furthermore the segmentation procedure can be used...... for regularizing the reconstruction process. The thesis provides models and algorithms for simultaneous reconstruction and segmentation and their performance is empirically validated. Two method of simultaneous reconstruction and segmentation are described in the thesis. Also, a method for parameter selection...

  12. Spinal cord grey matter segmentation challenge.

    Science.gov (United States)

    Prados, Ferran; Ashburner, John; Blaiotta, Claudia; Brosch, Tom; Carballido-Gamio, Julio; Cardoso, Manuel Jorge; Conrad, Benjamin N; Datta, Esha; Dávid, Gergely; Leener, Benjamin De; Dupont, Sara M; Freund, Patrick; Wheeler-Kingshott, Claudia A M Gandini; Grussu, Francesco; Henry, Roland; Landman, Bennett A; Ljungberg, Emil; Lyttle, Bailey; Ourselin, Sebastien; Papinutto, Nico; Saporito, Salvatore; Schlaeger, Regina; Smith, Seth A; Summers, Paul; Tam, Roger; Yiannakas, Marios C; Zhu, Alyssa; Cohen-Adad, Julien

    2017-05-15

    An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  13. Hawaii Abandoned Vessel Inventory, Kauai

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Kauai. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral habitats...

  14. CNMI Abandoned Vessel Inventory, Tinian

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Tinian. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral habitats...

  15. Puerto Rico Abandoned Vessel Inventory

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Puerto Rico. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral...

  16. American Samoa Abandoned Vessel Inventory

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for American Samoa. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral...

  17. Hawaii Abandoned Vessel Inventory, Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Oahu, Hawaii. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral...

  18. Hawaii Abandoned Vessel Inventory, Molokai

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Molokai, Hawaii. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral...

  19. CNMI Abandoned Vessel Inventory, Rota

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Rota. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral habitats...

  20. Hawaii Abandoned Vessel Inventory, Lanai

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Lanai. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral habitats...

  1. For-Hire Vessel Directory

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Vessel Directory is maintained as the sample frame for the For-Hire Survey. I contains data on for-hire vessels on the Atlantic and Gulf coasts. Data include...

  2. CNMI Abandoned Vessel Inventory, Saipan

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Saipan. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral habitats...

  3. Hawaii Abandoned Vessel Inventory, Maui

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Abandoned Vessel Project Data for Maui. Abandoned vessels pose a significant threat to the NOAA Trust resources through physical destruction of coral habitats...

  4. Vessels in Transit - Web Tool

    Data.gov (United States)

    Department of Transportation — A web tool that provides real-time information on vessels transiting the Saint Lawrence Seaway. Visitors may sort by order of turn, vessel name, or last location in...

  5. Real-time segmentation of burst suppression patterns in critical care EEG monitoring.

    Science.gov (United States)

    Brandon Westover, M; Shafi, Mouhsin M; Ching, Shinung; Chemali, Jessica J; Purdon, Patrick L; Cash, Sydney S; Brown, Emery N

    2013-09-30

    Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients. A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression. Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings. Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers. Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. Segmentation by watersheds : definition and parallel implementation

    NARCIS (Netherlands)

    Roerdink, Jos B.T.M.; Meijster, Arnold

    1997-01-01

    The watershed algorithm is a method for image segmentation widely used in the area of mathematical morphology. In this paper we first address the problem of how to define watersheds. It is pointed out that various existing definitions are not equivalent. In particular we explain the differences

  7. Pressure vessel design manual

    Energy Technology Data Exchange (ETDEWEB)

    Moss, D.R.

    1987-01-01

    The first section of the book covers types of loadings, failures, and stress theories, and how they apply to pressure vessels. The book delineates the procedures for designing typical components as well as those for designing large openings in cylindrical shells, ring girders, davits, platforms, bins and elevated tanks. The techniques for designing conical transitions, cone-cylinder intersections, intermediate heads, flat heads, and spherically dished covers are also described. The book covers the design of vessel supports subject to wind and seismic loads and one section is devoted to the five major ways of analyzing loads on shells and heads. Each procedure is detailed enough to size all welds, bolts, and plate thicknesses and to determine actual stresses.

  8. New research vessels

    Science.gov (United States)

    1984-04-01

    Two “new” ocean-going research vessels operated by the Scripps Institution of Oceanography and the National Science Foundation (NSF) will soon begin full-time scientific duties off the coast of California and in the Antarctic, respectively. The 37.5-m Scripps vessel, named Robert Gordon Sprout in honor of the ex-president of the University of California, replaces the smaller ship Ellen B. Scripps, which had served the institution since 1965. The new ship is a slightly modified Gulf Coast workboat. Under the name of Midnight Alaskan, it had been used for high-resolution geophysical surveys in American and Latin American waters by such firms as Arco Oil & Gas, Exxon, Pennzoil, and Racal-Decca before its purchase by Scripps from a Lousiana chartering firm last summer.

  9. Detection of Blood Vessels in Color Fundus Images using a Local Radon Transform

    Directory of Open Access Journals (Sweden)

    Reza Pourreza

    2010-09-01

    Full Text Available Introduction: This paper addresses a method for automatic detection of blood vessels in color fundus images which utilizes two main tools: image partitioning and local Radon transform. Material and Methods: The input images are firstly divided into overlapping windows and then the Radon transform is applied to each. The maximum of the Radon transform in each window corresponds to the probable available sub-vessel. To verify the detected sub-vessel, the maximum is compared with a predefined threshold. The verified sub-vessels are reconstructed using the Radon transform information. All detected and reconstructed sub-vessels are finally combined to make the final vessel tree. Results: The algorithm’s performance was evaluated numerically by applying it to 40 images of DRIVE database, a standard retinal image database. The vessels were extracted manually by two physicians. This database was used to test and compare the available and proposed algorithms for vessel detection in color fundus images. By comparing the output of the algorithm with the manual results, the two parameters TPR and FPR were calculated for each image and the average of TPRs and FPRs were used to plot the ROC curve. Discussion and Conclusion: Comparison of the ROC curve of this algorithm with other algorithms demonstrated the high achieved accuracy. Beside the high accuracy, the Radon transform which is integral-based makes the algorithm robust against noise.

  10. Large vessel vasculitides

    OpenAIRE

    Morović-Vergles, Jadranka; Pukšić, Silva; Gudelj Gračanin, Ana

    2013-01-01

    Large vessel vasculitis includes Giant cell arteritis and Takayasu arteritis. Giant cell arteritis is the most common form of vasculitis affect patients aged 50 years or over. The diagnosis should be considered in older patients who present with new onset of headache, visual disturbance, polymyalgia rheumatica and/or fever unknown cause. Glucocorticoides remain the cornerstone of therapy. Takayasu arteritis is a chronic panarteritis of the aorta ant its major branches presenting commonly in y...

  11. Very Versatile Vessel

    Science.gov (United States)

    2009-09-01

    data. This source provides information on aluminum hydrofoil vessels without the added weight of foil structures. The composite armor around the...seating compartment. The sides should also limit wave splash on the deck. The freeboard should contribute reserve buoyancy , increasing large-angle and...Resistance, Powering, and Propulsion Savitsky’s Method Since model testing data or other reliable performance data was unavailable for the proposed

  12. Segmented trapped vortex cavity

    Science.gov (United States)

    Grammel, Jr., Leonard Paul (Inventor); Pennekamp, David Lance (Inventor); Winslow, Jr., Ralph Henry (Inventor)

    2010-01-01

    An annular trapped vortex cavity assembly segment comprising includes a cavity forward wall, a cavity aft wall, and a cavity radially outer wall there between defining a cavity segment therein. A cavity opening extends between the forward and aft walls at a radially inner end of the assembly segment. Radially spaced apart pluralities of air injection first and second holes extend through the forward and aft walls respectively. The segment may include first and second expansion joint features at distal first and second ends respectively of the segment. The segment may include a forward subcomponent including the cavity forward wall attached to an aft subcomponent including the cavity aft wall. The forward and aft subcomponents include forward and aft portions of the cavity radially outer wall respectively. A ring of the segments may be circumferentially disposed about an axis to form an annular segmented vortex cavity assembly.

  13. Novel block segmentation and processing for Chinese-English document

    Science.gov (United States)

    Chien, Bing-Shan; Jeng, Bor-Shenn; Sun, San-Wei; Chang, Gan-How; Shyu, Keh-Hwa; Shih, Chun-Hsi

    1991-11-01

    The block segmentation and block classification of digitized printed documents segmented into regions of texts, graphics, tables, and images are very important in automatic document analysis and understanding. Conventionally, the constrained run length algorithm (CRLA) has been proposed to segment digitized documents, however, it is space-consuming and time- consuming. The CRLA method must define some constrained parameters, so it cannot proceed automatically, and its performance may degrade significantly due to improper parameters. This paper proposes an efficient and effective method for document analysis, sequence connected segmentation and mapping matrix cell algorithm (SCSMMC). This method can analyze both simple and complex documents automatically and it need not define any constraint parameters. This method, which only needs one-reading image of document, can proceed completely and the techniques of segmentation, classification, labeling, and character segmentation proceed at the same time. The proposed document analysis method may also combine with the optical character recognizer to form an adaptive document understanding system.

  14. Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.

    Science.gov (United States)

    García-Lorenzo, Daniel; Lecoeur, Jeremy; Arnold, Douglas L; Collins, D Louis; Barillot, Christian

    2009-01-01

    Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.

  15. Unsupervised tattoo segmentation combining bottom-up and top-down cues

    Science.gov (United States)

    Allen, Josef D.; Zhao, Nan; Yuan, Jiangbo; Liu, Xiuwen

    2011-06-01

    Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a figureground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is efficient and suitable for further tattoo classification and retrieval purpose.

  16. Chinese handwriting recognition an algorithmic perspective

    CERN Document Server

    Su, Tonghua

    2013-01-01

    This book provides an algorithmic perspective on the recent development of Chinese handwriting recognition. Two technically sound strategies, the segmentation-free and integrated segmentation-recognition strategy, are investigated and algorithms that have worked well in practice are primarily focused on. Baseline systems are initially presented for these strategies and are subsequently expanded on and incrementally improved. The sophisticated algorithms covered include: 1) string sample expansion algorithms which synthesize string samples from isolated characters or distort realistic string samples; 2) enhanced feature representation algorithms, e.g. enhanced four-plane features and Delta features; 3) novel learning algorithms, such as Perceptron learning with dynamic margin, MPE training and distributed training; and lastly 4) ensemble algorithms, that is, combining the two strategies using both parallel structure and serial structure. All the while, the book moves from basic to advanced algorithms, helping ...

  17. Multi-segmental neurofibromatosis

    OpenAIRE

    Kumar Sudhir; Kumar Ravi

    2004-01-01

    Neurofibromatosis (NF), one of the commonest phakomatoses, is characterized by varied clinical manifestations. Segmental NF is one of the uncommon subtypes of NF. We report a young adult presenting with asymptomatic skin lesions- neurofibromas and café-au-lait macules- over localized areas of the lower back, affecting more than one segment. None of the family members were found to have features of segmental NF. Segmental NF may be misdiagnosed as a birthmark or remain undiagnosed for l...

  18. A framework for retinal vasculature segmentation based on matched filters.

    Science.gov (United States)

    Meng, Xianjing; Yin, Yilong; Yang, Gongping; Han, Zhe; Yan, Xiaowei

    2015-10-24

    Automatic fundus image processing plays a significant role in computer-assisted retinopathy diagnosis. As retinal vasculature is an important anatomical structure in ophthalmic images, recently, retinal vasculature segmentation has received considerable attention from researchers. A segmentation method usually consists of three steps: preprocessing, segmentation, post-processing. Most of the existing methods emphasize on the segmentation step. In our opinion, the vessels and background can be easily separable when suitable preprocessing exists. This paper represents a new matched filter-based vasculature segmentation method for 2-D retinal images. First of all, a raw segmentation is acquired by thresholding the images preprocessed using weighted improved circular gabor filter and multi-directional multi-scale second derivation of Gaussian. After that, the raw segmented image is fine-tuned by a set of novel elongating filters. Finally, we eliminate the speckle like regions and isolated pixels, most of which are non-vessel noises and miss-classified fovea or pathological regions. The performance of the proposed method is examined on two popularly used benchmark databases: DRIVE and STARE. The accuracy values are 95.29 and 95.69 %, respectively, without a significant degradation of specificity and sensitivity. The performance of the proposed method is significantly better than almost all unsupervised methods, in addition, comparable to most of the existing supervised vasculature segmentation methods.

  19. Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique

    Directory of Open Access Journals (Sweden)

    Youssef El Merabet

    2015-02-01

    Full Text Available In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc. affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM, 84% with mean shift, 82% with color structure code (CSC, 80% with efficient graph-based segmentation algorithm (EGBIS and 71% with JSEG.

  20. Building roof segmentation from aerial images using a lineand region-based watershed segmentation technique.

    Science.gov (United States)

    El Merabet, Youssef; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja

    2015-02-02

    In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG.