Segmentation and interpretation of MR brain images: an improved active shape model

N Duta; M Sonka

doi:10.1109/42.746716

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Segmentation and interpretation of MR brain images: an improved active shape model

Journal article

Peer reviewed

Segmentation and interpretation of MR brain images: an improved active shape model

N Duta and M Sonka

IEEE transactions on medical imaging, Vol.17(6), pp.1049-1062

12/1998

DOI: 10.1109/42.746716

PMID: 10048862

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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.

Algorithms

Brain - anatomy & histology

Humans

Image Interpretation, Computer-Assisted - methods

Magnetic Resonance Imaging - statistics & numerical data

Magnetic Resonance Imaging - methods

Models, Neurological

Models, Statistical

Observer Variation

Details

Title: Subtitle: Segmentation and interpretation of MR brain images: an improved active shape model
Creators: N Duta - Department of Computer Science, Michigan State University, East Lansing 48823, USA
M Sonka
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.17(6), pp.1049-1062
DOI: 10.1109/42.746716
PMID: 10048862
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers; United States
Language: English
Date published: 12/1998
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984047620902771

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