Segmentation and interpretation of MR brain images. An improved active shape model

Abstract

This paper reports a novel method for fully automated segmentation that is based on description of shape and its variation using point distribution models (PDM's). An improvement of the active shape procedure introduced by Cootes and Taylor to find new examples of previously learned shapes using PDM's is presented. The new method for segmentation and interpretation of deep neuroanatomic structures such as thalamus, putamen, ventricular system, etc. incorporates a priori knowledge about shapes of the neuroanatomic structures to provide their robust segmentation and labeling in magnetic resonance (MR) brain images. The method was trained in eight MR brain images and tested in 19 brain images by comparison to observer-defined independent standards. Neuroanatomic structures in all testing images were successfully identified. Computer-identified and observer-defined neuroanatomic structures agreed well. The average labeling error was 7%+/-3%. Border positioning errors were quite small, with the average border positioning error of 0.8+/-0.1 pixels in 256 x 256 MR images. The presented method was specifically developed for segmentation of neuroanatomic structures in MR brain images. However, it is generally applicable to virtually any task involving deformable shape analysis.

Brain MR Segmentation through Fuzzy Expectation Maximization and Histogram Based K-Means

“…Several methods have been developed for this purpose, Lee and his colleagues not only applied active contours for automatic segmentation [2] another method watershed transform [3]. Besides, Duta developed a fully automated active shape model for segmentation and interpretation of MR images [4]. Watershed transform is another method that have used by some researchers such as Lee and his colleagues [3].In addition to aforementioned methods, atlas-based segmentation and Learning vector quantization (LVQ) models are two other methods applied in brain MR segmentation [5], [6].…”

Markov Dependence Tree-Based Segmentation of Deep Brain Structures

“…One common feature of the above-mentioned methods [3,4,5,7,8,9,10,11,12,13,14] is the need of sufficient training samples. The training process usually requires a considerable number of segmented images with the known ground truths.…”

“…The problem with fuzzy logic is that it is difficult to give consistently high accuracy to the segmentations of various intracranial structures because the relationship among those structures maintained by fuzzy logic may be weak and imprecise. Another popular technique used in the segmentation of cerebral structures is level sets or active contours [3,5,7,9,10,11], which can model the shapes of each structure. One of the concerns about active contours is the requirement of precise initialization of the starting contours.…”

Topologically controlled segmentation of 3D magnetic resonance images of the head by using morphological operators

“…Examples of the deformable models applied to the brain and brain structure segmentation can be found for instance in Refs. [4][5][6][7][8]. Sometimes, some statistical aspects are included in the model, which require some learning.…”