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检索条件"机构=Computer Vision and Robotics"

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Retinal vessel tortuosity evaluation via Circular Hough Transform

Retinal vessel tortuosity evaluation via Circular Hough Tran...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Farnoosh Ghadiri HamidReza Pourreza Touka Banaee Morteza Delgir Computer Engineering Department Machine Vision Laboratory Ferdowsi University of Mashhad Iran Computer Engineering Department Machine Vision Laboratory University of Ferdowsi Mashhad Iran Ophthalmic Research Center Khatam-Al-Anbia Hospital Medical science University of Mashhad Iran Computer Science Department Robotics Laboratory Northeastern University USA

Retinal vessel tortuosity has shown to be significantly associated with cardiovascular diseases such as hypertension and diabetes. Despite importance of this field a few techniques have been proposed yet. All previous methods rely on a vessel extraction phase, which its accuracy affects final output and also time consuming. Nobility of presented algorithm is to introduce a method for evaluating retinal vessel tortuosity without any explicit vessel detection. We use the Circular Hough Transform (CHT) based on gradient field of the retinal image. Each vessel curve is detected as a semi-circle by Hough transform and tortuosity of the curve is determined with the help of accumulated value of circle center and its radius. As there are no any specific database for tortuosity evaluation, the algorithm was tasted on database consisting of 40 images, mixture of DRIVE database and images from Khatam-Al-Anbia Hospital consisting of 40 retinal images, of which 20 were tortuous and 20 were non-tortuous. The proposed algorithm can achieve classification rate of 92% along with less computation time in compare of previous methods.

关键词： Transforms Retinal vessels Classification algorithms Biomedical measurements Biomedical imaging Image segmentation

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Challenges of close-range underwater optical mapping

Challenges of close-range underwater optical mapping

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OCEANS - Europe

作者： Ricard Prados Rafael Garcia Javier Escartín László Neumann Computer Vision and Robotics Group University of Girona Girona Spain CNRS/IPGP UMR7154 Équipe de Géosciences Marines Paris France ICREA Barcelona Spain

Underwater optical mapping often involves the use of image mosaicing techniques. High quality mosaicing requires the application of blending methods to achieve continuous and artifact-free mosaics. Image blending has a dilated history of over three decades in the terrestrial and aerial fields. Unfortunately, the nature of the underwater medium adds additional difficulties to the mosaicing and blending tasks. In this paper a survey of the blending methods is given, focusing the attention on its applicability to underwater mosaicing. Image acquisition is performed by Autonomous Underwater Vehicles (AUVs) or Remotely Operated Vehicles (ROVs) in the deep ocean, a medium with aggressive light absorption and disrupting scattering effects that requires of the use of artificial lighting. A comprehensive comparison of the basic features and limitations of some of the most important existing blending techniques is presented. The goal is the generation of seamless and visually pleasant large area photo-mosaics of the seafloor, free from double contouring, ghosting and other disturbing and common blending artifacts.

关键词： Smoothing methods Lighting Context Spline Cameras Visualization Scattering

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Feature extraction for underwater visual SLAM

Feature extraction for underwater visual SLAM

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OCEANS - Europe

作者： Josep Aulinas Marc Carreras Xavier Llado Joaquim Salvi Rafael Garcia Ricard Prados Yvan R. Petillot Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Spain Oceans Systems Laboratory School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

Detecting and selecting proper landmarks is a key issue to solve Simultaneous Localization and Mapping (SLAM). In this work, we present a novel approach to perform this landmark detection. Our approach is based on using three sources of information: 1) three-dimensional topological information from SLAM; 2) context information to characterize regions of interest (RoI); and 3) features extracted from these RoIs. Topological information is taken from the SLAM algorithm, i.e. the three-dimensional approximate position of the landmark with a certain level of uncertainty. Contextual information is obtained by segmenting the image into background and RoIs. Features extracted from points of interest are then computed by using common feature extractors such as SIFT and SURF. This information is used to associate new observations with known landmarks obtained from previous observations. The proposed approach is tested under a real unstructured underwater environment using the SPARUS AUV. Results demonstrate the validity of our approach, improving map consistency.

关键词： Feature extraction Simultaneous localization and mapping Cameras Robustness Vehicles Uncertainty Optical imaging

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A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance

A probabilistic framework for automatic prostate segmentatio...

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IEEE International Conference on Image Processing

作者： S. Ghose A. Oliver R. Marti X. Lladó J. Freixenet J. C. Vilanova F. Meriaudeau Computer Vision and Robotics Group University of Girona Girona Spain Le2i-UMR CNRS 5158 Université de Bourgogne Le Creusot France Girona Magnetic Resonance Center Girona Spain

Prostate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentation. The proposed method achieves a mean Dice similarity coefficient value of 0.96±0.01, and a mean absolute distance value of 0.80±0.24 mm when validated with 24 images from 6 datasets in a leave-one-patient-out validation framework. Our proposed model is automatic, and performs accurate prostate segmentation in presence of intensity heterogeneity and imaging artifacts.

关键词： Image segmentation Active appearance model Accuracy Shape Probabilistic logic Bayesian methods Training

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Multiple mean models of statistical shape and probability priors for automatic prostate segmentation

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International Workshop on Prostate Cancer Imaging: Image Analysis and Image-Guided Interventions, Held in Conjunction with MICCAI 2011

作者： Ghose, Soumya Oliver, Arnau Martí, Robert Lladó, Xavier Freixenet, Jordi Mitra, Jhimli Vilanova, Joan C. Comet, Josep Meriaudeau, Fabrice Computer Vision and Robotics Group University of Girona Campus Montilivi Av. Lluís Santaló s/n 17071 Girona Spain Laboratoire Le2I UMR CNRS 5158 Université de Bourgogne 12 Rue de la Fonderie 71200 Le Creusot France Girona Magnetic Resonance Imaging Center Girona Spain University Hospital Dr. Josep Trueta Girona Spain

ISBN: (纸本)9783642239434

Low contrast of the prostate gland, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow regions, speckle and significant variations in prostate shape, size and inter dataset contrast in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a probabilistic framework for automatic initialization and propagation of multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. Unlike traditional statistical models of shape and intensity priors we use posterior probability of the prostate region to build our texture model of the prostate and use the information in initialization and propagation of the mean model. Furthermore, multiple mean models are used compared to a single mean model to improve segmentation accuracies. The proposed method achieves mean Dice Similarity Coefficient (DSC) value of 0.97±0.01, and mean Mean Absolute Distance (MAD) value of 0.49±0.20 mm when validated with 23 datasets with considerable shape, size, and intensity variations, in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value

关键词： Principal component analysis

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A Non-Linear Diffeomorphic Framework for Prostate Multimodal Registration

A Non-Linear Diffeomorphic Framework for Prostate Multimodal...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Jhimli Mitra Zoltan Kato Robert Marti Arnau Oliver Xavier Llado Soumya Ghose Joan C. Vilanova Fabrice Meriaudeau Computer Vision and Robotics Group Universitat de Girona Girona Spain Le2i-UMR CNRS 5158 Université de Bourgogne Le Creusot France Department of Image Processing and Computer Graphics University of Szeged Szeged Hungary Girona Magnetic Resonance Center Girona Spain

This paper presents a novel method for non-rigid registration of prostate multimodal images based on a nonlinear framework. The parametric estimation of the non-linear diffeomorphism between the 2D fixed and moving images has its basis in solving a set of non-linear equations of thin-plate splines. The regularized bending energy of the thin-plate splines along with the localization error of established correspondences is jointly minimized with the fixed and transformed image difference, where, the transformed image is represented by the set of non-linear equations defined over the moving image. The traditional thin-plate splines with established correspondences may provide good registration of the anatomical targets inside the prostate but may fail to provide improved contour registration. On the contrary, the proposed framework maintains the accuracy of registration in terms of overlap due to the non-linear thin-plate spline functions while also producing smooth deformations of the anatomical structures inside the prostate as a result of established corrspondences. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images in terms of Dice similarity coefficient with an average of 0.982 ± 0.004, average 95% Hausdorff distance of 1.54 ± 0.46 mm and mean target registration and target localization errors of 1.90±1.27 mm and 0.15 ± 0.12 mm respectively.

关键词： Biopsy Magnetic resonance imaging Equations Spline Glands High definition video Accuracy

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Support Aggregation via Non-linear Diffusion with Disparity-Dependent Support-Weights for Stereo Matching

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9th Asian Conference on computer vision

作者： Yoon, Kuk-Jin Jeong, Yekeun Kweon, In So Computer Vision Lab. Dept. Information and Communications GIST South Korea Robotics and Computer Vision Lab. KAIST South Korea

ISBN: (纸本)9783642123061

In stereo matching, homogeneous areas, depth continuity areas, and occluded areas need more attention. Many methods try to handle pixels in homogeneous areas by propagating supports. As a result, pixels in homogeneous areas get assigned disparities inferred from the disparities of neighboring pixels. However, at the same time, pixels in depth discontinuity areas get supports from different depths and/or from occluded pixels, and resultant disparity maps are easy to be blurred. To resolve this problem, we propose a non-linear diffusion-based support aggregation method. Supports are iteratively aggregated with the support-weights, while adjusting the support-weights according to disparities to prevent incorrect supports from different depths and/or occluded pixels. As a result, the proposed method yields good results not only in homogeneous areas but also in depth discontinuity areas as the iteration goes on without the critical degradation of performance.

关键词： Pixels

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Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation

Statistical Shape and Probability Prior Model for Automatic ...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Soumya Ghose Arnau Oliver Robert Marti Xavier Llado Jordi Freixenet Jhimli Mitra Joan C. Vilanova Josep Comet Fabrice Meriaudeau Le2i-UMR CNRS 5158 Universite de Bourgogne Le Creusot France Computer Vision and Robotics Group Universitat de Girona Girona Spain Girona Magnetic Resonance Center Girona Spain University Hospital Dr. Josep Trueta Girona Spain

Accurate prostate segmentation in Trans Rectal Ultra Sound (TRUS) images is an important step in different clinical applications. However, the development of computer aided automatic prostate segmentation in TRUS images is a challenging task due to low contrast, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow, and speckle. Significant variations in prostate shape, size and contrast between the datasets pose further challenges to achieve an accurate segmentation. In this paper we propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. The proposed framework achieves a mean Dice similarity coefficient value of 0.974±0.006, mean mean absolute distance value of 0.49±0.20 mm and mean Hausdorff distance of 1.24±0.56 mm when validated with 23 datasets in a leave-one-patient-out validation framework.

关键词： Shape Image segmentation Mathematical model Accuracy Active appearance model Bayesian methods

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Contextual activity visualization from long-term video observations

Contextual activity visualization from long-term video obser...

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作者： Morris, Brendan Tran Trivedi, Mohan Manubhai Computer Vision and Robotics Research Laboratory University of California San Diego United States

The Contextual Activity Notification Visualization Analysis System (Canvas) provides a user interaction interface for instantaneous feedback of contextual processing units that enable high-level semantic extraction an... 详细信息

关键词： Visualization

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A robust and universal gradient domain imaging solver using gradient variables and locally varying metrics

A robust and universal gradient domain imaging solver using ...

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作者： Neumann, László Hegediis, Ramón Computer Vision and Robotics Group University of Girona Spain ICREA Barcelona Spain

ISBN: (纸本)9783905674248

Gradient Domain Imaging (GDI) has gained a high importance and provoked numerous powerful applications over the last decade. It employs a workflow of creating an inconsistent gradient field (GF) from one or more images using different non-linear operations and finally it determines an image with a consistent, integrable GF that falls near to the prescribed inconsistent one. However, the result is not really predictable, often suffers from halo-effects and other local distortions at higher frequencies as well as from uncontrollable far-effects arising from local gradient-contradictions. The unfolding of these artifacts culminates in an undesired overall image appearance. None of the common GDI solvers can overcome these side-effects as they utilize the same local isotropic'coefficient-pattern' in a sparse matrix description and they differ only in the numerical solution techniques. We present a powerful GDI method solving the problem completely in the gradient domain with gradient-variables and using spatially varying metrics that depends only on the starting inconsistent gradient field. After obtaining the nearest consistent gradient field with the pre-defined metrics we return into the image space by double integration that yields the wanted pixel intensity values. Our method delivers a great aesthetic enhancement by eliminating halo effects and saving small details, furthermore providing a realistic and pleasant overall light distribution at lower frequencies. By significantly extending the range of allowed inconsistency in the prescribed gradient field, it also allows for solving a large class of problems that proved hopeless beforehand. © The Eurographics Association 2010.

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