Results 1 - 10 of 191

Multiscale vessel enhancement filtering

by Alejandro F. Frangi, Wiro J. Niessen, Koen L. Vincken, Max A. Viergever , 1998
"... The multiscale second order local structure of an image (Hessian) isexamined with the purpose of developing a vessel enhancement filter. A vesselness measure is obtained on the basis of all eigenvalues of the Hessian. This measure is tested on two dimensional DSA and three dimensional aortoiliac an ..."
Abstract - Cited by 318 (8 self) - Add to MetaCart
The multiscale second order local structure of an image (Hessian) isexamined with the purpose of developing a vessel enhancement filter. A vesselness measure is obtained on the basis of all eigenvalues of the Hessian. This measure is tested on two dimensional DSA and three dimensional aortoiliac and cerebral MRA data. Its clinical utility is shown by the simultaneous noise and background suppression and vessel enhancement in maximum intensity projections and volumetric displays.

A Review of Vessel Extraction Techniques and Algorithms

by Cemil Kirbas, Francis K. H. Quek - ACM Computing Surveys , 2000
"... Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing r ..."
Abstract - Cited by 185 (0 self) - Add to MetaCart
Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research. While we have mainly targeted the extraction of blood vessels, neurosvascular structure in particular, we have also reviewed some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We have divided vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, and (6) miscellaneous tube-like object detection approaches. Some of these categories are further divided into sub- categories. We have also created tables to compare the papers in each category against such criteria as dimensionality, input type, pre-processing, user interaction, and result type.
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Model-based quantitation of 3-D magnetic resonance angiographic images

by Alejandro F. Frangi, Wiro J. Niessen, Romhild M. Hoogeveen, Theo Van Walsum, Max A. Viergever - IEEE TRANSACTIONS ON MEDICAL IMAGING , 1999
"... Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA’s) is mainly performed on two-dimensional (2-D) maximum intensity p ..."
Abstract - Cited by 72 (1 self) - Add to MetaCart
Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA’s) is mainly performed on two-dimensional (2-D) maximum intensity projections, automated quantification of vascular segments di-rectly from the 3-D dataset is desirable to provide accurate and objective measurements of the 3-D anatomy. A model-based method for quantitative 3-D MRA is proposed. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. A novel image feature to guide the deformation of the central vessel axis is introduced. Subsequently, concepts of deformable models are combined with knowledge of the physics of the acquisition technique to accu-rately segment the vessel wall and compute the vessel diameter and other geometrical properties. The method is illustrated and validated on a carotid bifurcation phantom, with ground truth and medical experts as comparisons. Also, results on 3-D time-of-flight (TOF) MRA images of the carotids are shown. The approach is a promising technique to assess several geometrical vascular parameters directly on the source 3-D images, providing an objective mechanism for stenosis grading.

Tissue Classification Based on 3D Local Intensity Structure for Volume Rendering

by Yoshinobu Sato, Carl-Fredrik Westin, Abhir Bhalerao, Shin Nakajima, Nobuyuki Shiraga, Shigeyuki Yoshida, Zientaray Kikinis, Ron Kikinisy , 1997
"... This paper describes 3D image filters for the enhancement of specific local intensity structures such as line and sheet, and its application to tissue classification for volume rendering. Multi-channel classification is performed by combining different 3D image filter outputs. The resulted method si ..."
Abstract - Cited by 68 (2 self) - Add to MetaCart
This paper describes 3D image filters for the enhancement of specific local intensity structures such as line and sheet, and its application to tissue classification for volume rendering. Multi-channel classification is performed by combining different 3D image filter outputs. The resulted method significantly enlarges the scope of volume rendering, especially in the medical domain. We show the usefulness of the method for different visualization problems. 1 Introduction Volume rendering is a powerful visualization tool especially for medical application [1],[2],[3],[4]. Basic requirement in medical application is to visualize specific tissues of interest with the relation to surrounding structures. Tissue classification is one of the most important processes in the volume rendering pipeline. Most commonly, this process is done based on the histogram of intensity values in original 3D images. Probabilistic or fuzzy classification has been used instead of binary classification in orde...
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Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures

by Michal Sofka, Charles V. Stewart - IEEE TMI , 2006
"... Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter re ..."
Abstract - Cited by 52 (1 self) - Add to MetaCart
Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel centerline extraction algorithm.
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Vessel segmentation using a shape driven flow

by Delphine Nain, Anthony Yezzi, Greg Turk - in Med. Image Comput. Comput.-Assist. Intervention—MICCAI, 2004
"... Abstract. We present a segmentation method for vessels using an implicit deformable model with a soft shape prior. Blood vessels are challenging structures to segment due to their branching and thinning geometry as well as the decrease in image contrast from the root of the vessel to its thin branch ..."
Abstract - Cited by 41 (4 self) - Add to MetaCart
Abstract. We present a segmentation method for vessels using an implicit deformable model with a soft shape prior. Blood vessels are challenging structures to segment due to their branching and thinning geometry as well as the decrease in image contrast from the root of the vessel to its thin branches. Using image intensity alone to deform a model for the task of segmentation often results in leakages at areas where the image information is ambiguous. To address this problem, we combine image statistics and shape information to derive a region-based active contour that segments tubular structures and penalizes leakages. We present results on synthetic and real 2D and 3D datasets. 1
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Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux

by Max W. K. Law, Albert C. S. Chung
"... Abstract. This paper proposes a novel curvilinear structure detector, called Optimally Oriented Flux (OOF). OOF finds an optimal axis on which image gradients are projected in order to compute the image gradient flux. The computation of OOF is localized at the boundaries of local spherical regions. ..."
Abstract - Cited by 38 (4 self) - Add to MetaCart
Abstract. This paper proposes a novel curvilinear structure detector, called Optimally Oriented Flux (OOF). OOF finds an optimal axis on which image gradients are projected in order to compute the image gradient flux. The computation of OOF is localized at the boundaries of local spherical regions. It avoids considering closely located adjacent structures. The main advantage of OOF is its robustness against the disturbance induced by closely located adjacent objects. Moreover, the analytical formulation of OOF introduces no additional computation load as compared to the calculation of the Hessian matrix which is widely used for curvilinear structure detection. It is experimentally demonstrated that OOF delivers accurate and stable curvilinear structure detection responses under the interference of closely located adjacent structures as well as image noise. 1
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Deformable medical image registration: A survey

by Aristeidis Sotiras, Christos Davatzikos, Nikos Paragios - IEEE TRANSACTIONS ON MEDICAL IMAGING , 2013
"... Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudin ..."
Abstract - Cited by 35 (1 self) - Add to MetaCart
Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
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Gray-scale skeletonization of small vessels in magnetic resonance angiography

by Peter J. Yim, Peter L. Choyke, Ronald M. Summers - IEEE Transactions on Medical Imaging , 2000
"... Abstract—Interpretation of magnetic resonance angiography (MRA) is problematic due to complexities of vascular shape and to artifacts such as the partial volume effect. We present new methods to assist in the interpretation of MRA. These include methods for detection of vessel paths and for determin ..."
Abstract - Cited by 33 (0 self) - Add to MetaCart
Abstract—Interpretation of magnetic resonance angiography (MRA) is problematic due to complexities of vascular shape and to artifacts such as the partial volume effect. We present new methods to assist in the interpretation of MRA. These include methods for detection of vessel paths and for determination of branching patterns of vascular trees. They are based on the ordered region growing (ORG) algorithm that represents the image as an acyclic graph, which can be reduced to a skeleton by specifying vessel endpoints or by a pruning process. Ambiguities in the vessel branching due to vessel overlap are effectively resolved by heuristic methods that incorporate a priori knowledge of bifurcation spacing. Vessel paths are detected at interactive speeds on a 500-MHz processor using vessel endpoints. These methods apply best to smaller vessels where the image intensity peaks at the center of the lumen which, for the abdominal MRA, includes vessels whose diameter is less than 1 cm. Index Terms—Magnetic resonance angiography, skeletonization, visualization. I.

CURVES: Curve Evolution for Vessel Segmentation

by Liana M. Lorigo, Olivier D. Faugeras, W. Eric L. Grimson, Renaud Keriven, Ron Kikinis, Arya Nabavi, Carl-fredrik Westin
"... The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning an ..."
Abstract - Cited by 33 (0 self) - Add to MetaCart
The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.