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258
Magnetic resonance image tissue classification using a partial volume model
 NEUROIMAGE
, 2001
"... We describe a sequence of lowlevel operations to isolate and classify brain tissue within T1weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for imag ..."
Abstract

Cited by 104 (5 self)
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We describe a sequence of lowlevel operations to isolate and classify brain tissue within T1weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic Bspline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensitynormalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average � indices of ��0.746 � 0.114 for gray matter (GM) and ��0.798 � 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average � indices �� 0.893 � 0.041 for GM and ��0.928 � 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute’s BrainWeb phantom.
Edge Detection Techniques  An Overview
 INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND IMAGE ANALYSIS
, 1998
"... In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image ..."
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Cited by 94 (2 self)
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In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an illconditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research...
ScaleInvariant Shape Features for Recognition of Object Categories
 Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition
, 2004
"... We introduce a new class of distinguished regions based on detecting the most salient convex local arrangements of contours in the image. The regions are used in a similar way to the local interest points extracted from graylevel images, but they capture shape rather than texture. Local convexity i ..."
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Cited by 71 (15 self)
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We introduce a new class of distinguished regions based on detecting the most salient convex local arrangements of contours in the image. The regions are used in a similar way to the local interest points extracted from graylevel images, but they capture shape rather than texture. Local convexity is characterized by measuring the extent to which the detected image contours support circle or arclike local structures at each position and scale in the image. Our saliency measure combines two cost functions defined on the tangential edges near the circle: a tangentialgradient energy term, and an entropy term that ensures local support from a wide range of angular positions around the circle. The detected regions are invariant to scale changes and rotations, and robust against clutter, occlusions and spurious edge detections. Experimental results show very good performance for both shape matching and recognition of object categories. 1.
An Algorithmic Overview of Surface Registration . . .
 MEDICAL IMAGE ANALYSIS
, 2000
"... This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected ..."
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Cited by 67 (1 self)
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This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration
Edge Detection by Helmholtz Principle
 Journal of Mathematical Imaging and Vision
, 2001
"... . We apply to edge detection a recently introduced method for computing geometric structures in a digital image, without any a priori information. According to a basic principle of perception due to Helmholtz, an observed geometric structure is perceptually \meaningful" if its number of occure ..."
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Cited by 55 (14 self)
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. We apply to edge detection a recently introduced method for computing geometric structures in a digital image, without any a priori information. According to a basic principle of perception due to Helmholtz, an observed geometric structure is perceptually \meaningful" if its number of occurences would be very small in a random situation: in this context, geometric structures are characterized as large deviations from randomness. This leads us to dene and compute edges and boundaries (closed edges) in an image by a parameterfree method. Maximal detectable boundaries and edges are dened, computed, and the results compared with the ones obtained by classical algorithms. Keywords: image analysis, perception, Helmholtz principle, edge detection, large deviations 1. Introduction In statistical methods for image analysis, one of the main problems is the choice of an adequate prior. For example, in the Bayesian model (Geman and Geman, 1984), given an observation \obs", the aim is to ...
Measurement and Integration of 3D Structures by Tracking Edge Lines
, 1992
"... This paper describes techniques for dynamically modeling the 2D appearance and 3D geometry of a scene by integrating information from a moving camera. These techniques are illustrated by the design of a system which constructs a geometric description of a scene from the motion of a camera mounted ..."
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Cited by 54 (6 self)
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This paper describes techniques for dynamically modeling the 2D appearance and 3D geometry of a scene by integrating information from a moving camera. These techniques are illustrated by the design of a system which constructs a geometric description of a scene from the motion of a camera mounted on a robot arm. A framework
Regularized laplacian zero crossings as optimal edge integrators
 International Journal of Computer Vision
, 2001
"... We view the fundamental edge integration problem for object segmentation in a geometric variational framework. First we show that the classical zerocrossings of the image Laplacian edge detector as suggested by Marr and Hildreth, inherently provides optimal edgeintegration with regard to a very na ..."
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Cited by 50 (4 self)
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We view the fundamental edge integration problem for object segmentation in a geometric variational framework. First we show that the classical zerocrossings of the image Laplacian edge detector as suggested by Marr and Hildreth, inherently provides optimal edgeintegration with regard to a very natural geometric functional. This functional accumulates the inner product between the normal to the edge and the gray level imagegradient along the edge. We use this observation to derive new and highly accurate active contours based on this functional and regularized by previously proposed geodesic active contour geometric variational models. 1.
Logical/Linear Operators for Image Curves
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify aspects of linear operator theory and boolean logic. A family of these operators appropriate for measuring the loworder differential struct ..."
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Cited by 48 (7 self)
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We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify aspects of linear operator theory and boolean logic. A family of these operators appropriate for measuring the loworder differential structure of image curves is developed. These L/L operators are derived by decomposing a linear model into logical components to ensure that certain structural preconditions for the existence of an image curve are upheld. Tangential conditions guarantee continuity, while normal conditions select and categorize contrast profiles. The resulting operators allow for coarse measurement of curvilinear differential structure (orientation and curvature) while successfully segregating edge and linelike features. By thus reducing the incidence of falsepositive responses, these operators are a substantial improvement over (thresholded) linear operators which attempt to resolve the same class of features. ...
Are Edges Incomplete?
"... . We address the problem of computing a generalpurpose early visual representation that satisfies two criteria. 1) Explicitness: To be more useful than the original pixel array, the representation must take a significant step toward making important image structure explicit. 2) Completeness: To sup ..."
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Cited by 46 (1 self)
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. We address the problem of computing a generalpurpose early visual representation that satisfies two criteria. 1) Explicitness: To be more useful than the original pixel array, the representation must take a significant step toward making important image structure explicit. 2) Completeness: To support a diverse set of highlevel tasks, the representation must not discard information of potential perceptual relevance. The most prevalent representation in image processing and computer vision that satisfies the completeness criterion is the wavelet code. In this paper, we propose a very different code which represents the location of each edge and the magnitude and blur scale of the underlying intensity change. By making edge structure explicit, we argue that this representation better satisfies the first criterion than do wavelet codes. To address the second criterion, we study the question of how much visual information is lost in the representation. We report a novel method for inver...