White Matter Mapping in DT-MRI Using Geometric Flows

Jonasson, Lisa; Hagmann, Patric; Bresson, Xavier; Meuli, Reto; Cuisenaire, O.; Thiran, Jean‐Philippe

doi:10.1007/978-3-540-45210-2_53

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…In (71; 36; 38; 42; 46; 44) the corpus callosum was segmented, while the cortico-spinal tract was extracted in (38; 46). Other structures such as the lateral ventricles have been also segmented (42).…”

Section: Applications To Medical Image Segmentationmentioning

confidence: 99%

Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging

Luis‐García

Westin

Alberola-López

2011

Computerized Medical Imaging and Graphics

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In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic-semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results.

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Section: Applications To Medical Image Segmentationmentioning

confidence: 99%

Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging

Luis‐García

Westin

Alberola-López

2011

Computerized Medical Imaging and Graphics

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“…There have been many approaches to this task and one of the most popular is the use of variational methods involving active contours; this technique will be the primary focus of this paper. Applications for segmentation are numerous: medical imaging [29,42,19], computer vision [33], stereo reconstruction [25,26,40], illusory contour [43,21,20], point-cloud surface reconstruction Copyright © SIAM Unauthorized reproduction of this article is prohibited [27], scene reconstruction from range data [13], and data clustering [41]. In this work, we consider the application of edge based segmentation with shape priors on difficult settings that include illusory contours, clutter and, occlusions.…”

Section: Introductionmentioning

confidence: 99%

Geodesic Active Contours with Shape Priors for Segmentation, Disocclusion, and Illusory Contour Capture

Householder¹

2020

SIURO

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Image segmentation is the task of finding salient regions of importance in an image. In this work, we take a curve evolution approach to this problem where we deform an initial curve in the inward normal direction with the objective of finding the boundaries of objects in an image. To achieve this, we propose a variational image segmentation model that incorporates a clique based shape signature with a geodesic active contours energy. The model scheme consists of evolving a parametric representation of an active contour to minimize the penalty that the model induces. This penalty is minimized when the curve is on the boundaries of objects in the image, areas with sudden change in pixel intensity i.e. light to dark. We demonstrate successful capture of illusory contours, segmentation of objects in a cluttered background, and segmentation of occluded objects.

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“…The most recent approaches have been to use partial differential equations (PDE), variational methods and level sets [9][10][11][12][13][14][15]. In [9] we presented a geometric flow implemented with level set methods for fiber tract segmentation by measuring the diffusive similarity between voxels. The method is based on the assumption that adjacent voxels in a tract have similar diffusion properties but that the diffusion at different location of the tract can be very different.…”

Section: Introductionmentioning

confidence: 99%

A level set method for segmentation of the thalamus and its nuclei in DT-MRI

et al. 2007

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We present a method for segmenting white matter as well as the gray matter structures from diffusion tensor magnetic resonance images (DT-MRI). The segmentation is done evolving a set of coupled level set functions. The zero level set of each level set function forms a surface in 3D that is driven by the region-based force including all tensors belonging to a certain region. The region-based force is defined by using a very sensitive similarity measure between DT. We apply our method for segmenting the thalamus and its nuclei.This technical paper proposes several new strategies for level set methods to segment efficiently complex objects as present in DT-MRI. First of all, we present a very sensitive similarity measure that distinguishes very subtle differences between regions within, for example, the thalamus. Secondly, we present a new way of selecting the most representative tensor for group of tensors for these kinds of applications. We argue for the importance to use the tensor minimizing the variation within the group of tensors instead of the mean tensor as suggested in other papers on tensor segmentation. The third important point is the necessity of using several coupled level sets to define the background. Methods differentiating only between foreground and background will fail when applied to complex structures such as the brain. It is crucial for a region-based approach to consider all the surrounding structures for a correct definition of the forces driving the segmentation. r

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White Matter Mapping in DT-MRI Using Geometric Flows

Cited by 13 publications

References 25 publications

Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging

Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging

Geodesic Active Contours with Shape Priors for Segmentation, Disocclusion, and Illusory Contour Capture

A level set method for segmentation of the thalamus and its nuclei in DT-MRI

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