Distributed Local MRF Models for Tissue and Structure Brain Segmentation

Abstract

To cite this version:Benoît Scherrer, Florence Forbes, Catherine Garbay, Michel Dojat. Distributed local MRF models for tissue and structure brain segmentation. IEEE Abstract-Accurate tissue and structure segmentation of Magnetic Resonance (MR) brain scans is critical in several applications. In most approaches this task is handled through two sequential steps. We propose to carry out cooperatively both tissue and subcortical structure segmentation by distributing a set of local and cooperative Markov Random Field (MRF) models. Tissue segmentation is performed by partitioning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Similarly, subcortical structure segmentation is performed via local MRF models that integrate localization constraints provided by a priori fuzzy description of brain anatomy. Subcortical structure segmentation is not reduced to a subsequent processing step but joined with tissue segmentation: the two procedures cooperate to gradually and conjointly improve model accuracy.We propose a framework to implement this distributed modeling integrating cooperation, coordination, and local model checking in an efficient way. Its evaluation was performed using both phantoms and real 3T brain scans, showing good results and in particular robustness to nonuniformity and noise with a low computational cost. This original combination of local MRF models, including anatomical knowledge, appears as a powerful and promising approach for MR brain scan segmentation.

Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

“…Chen et al (2009Chen et al ( , 2011 applied Gaussian distributions to model local intensity variations for bias correction and segmentation. Scherrer et al (2009) and Tohka et al (2010) proposed a different kind of local image modeling and segmentation in order to overcome intensity non-uniformity. The principle idea of these methods is to compute tissue intensity models locally in various sub-volumes of the MR image.…”

Identification of Structures in Medical Images

“…Some authors, however, try to minimize the need of a priori knowledge. As an example, Scherrer et al (2009) extracted tissue and sub-cortical structures in MR images by considering a local approach to cope with intensity non-uniformity and a multi-agent based implementation.…”

Connecting Markov random fields and active contour models: application to gland segmentation and classification

“…2 Both methods have since been adapted to address a variety of segmentation problems. MRFs have employed adaptive clustering, 3 multiresolution analysis, 4 and complex boundary models 5 to effectively partition mammographic, 6 multimodal, 7 magnetic resonance, [8][9][10] and color 11 images. AC models have been used to track cell locomotion, 12 analyze cardiac images, [13][14][15][16][17][18] segment retinal vessels, 19 and interrogate histopathological sections.…”