Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

Abstract

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.

Automated separation of gray and white matter in brain MRIs by fastened segments of geodesic contours

“…FSGPC-II has AOMs of 93% and degree of equality of 88% for WM, which is better than that research, although no a priori information is utilized. Angelini et al [22] gave a best minimum error rate of 13.5% while comparing many algorithms. FSGPC-II favors this research by achieving a misclassification rate of 4.8% and 7.7% as evaluated by different experts.…”

Medical Image Segmentation Using Particle Swarm Optimization

“…Let X={X 1 ly of sites with regard to a lique c is a singleton or all neighbours. If c is not a (3) family of random variables variable taking values in the The family X is a random = Λ M .…”

Real-time segmentation by Active Geometric Functions

“…the object (or the foreground) and the background. In order to simultaneously segment multiple objects, similar to level-set framework [19,24,25], more than one deformable model could be introduced at the same time. In order to realize simultaneous multi-object segmentation, these deformable models will be coupled together via some mechanism, such as distance [25] or multi-phase fashion [17,24].…”