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% 6 3%. Border positioning errors w...

• Type: Journal
• Year: 1998
• Where: TMI
• Authors: Nicolae Duta, Milan Sonka
• Keywords: Deep Neuroanatomic Structures, Images, Neuroanatomic Structures, TMI 1998