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Segmentation and quantitative evaluation of brain MRI data with a multi-phase three-dimensional implicit deformable model

Elsa D. Angelini; Ting Song; Brett D. Mensh; Andrew F. Laine

Title:
Segmentation and quantitative evaluation of brain MRI data with a multi-phase three-dimensional implicit deformable model
Author(s):
Angelini, Elsa D.
Song, Ting
Mensh, Brett D.
Laine, Andrew F.
Date:
Type:
Articles
Department:
Biomedical Engineering
Permanent URL:
Book/Journal Title:
Medical imaging 2004 : Image processing : 16-19 February 2004, San Diego, California, USA ; Proceedings of SPIE, vol. 5370
Book Author:
Fitzpatrick, J. Michael
Publisher:
SPIE
Publisher Location:
Bellingham, Wash.
Abstract:
Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with 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 speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours.
Subject(s):
Biomedical engineering
Publisher DOI:
10.1117/12.535860
Item views:
285
Metadata:
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