A statistical 3-D segmentation algorithm for classifying brain tissues in multiple sclerosis

Zhanyu Ge; Vikram Venkatesan; Sunandra Mitra

doi:10.1109/CBMS.2001.941761

A statistical 3-D segmentation algorithm for classifying brain tissues in multiple sclerosis

Zhanyu Ge, Vikram Venkatesan, Sunandra Mitra

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Deterministic annealing (DA) algorithm has been successfully used to segment the simulated magnetic resonance normal brain images [1]. This paper presents the results of applying it to simulated and actual clinical multiple sclerosis (MS) magnetic resonance (MR) brain data with the objective of developing a computer-aided diagnostic (CAD) tool for early detection and follow-up for MS lesions. Multiple sclerosis lesions on T1 simulated brain images [2] can be obtained by segmenting the image data using deterministic annealing algorithm and then performing further arithmetic manipulations on these segmented images. Lesions in clinical T2 multiple sclerosis MR images are isolated entities in the segmented images of white matter, gray matter and cerebrospinal fluid. The achieved results demonstrate the ability of deterministic annealing algorithm to isolate MS lesions from clinical MR data, thus providing a potential CAD tool for the clinicians.

Original language	English
Pages (from-to)	455-460
Number of pages	6
Journal	Proceedings of the IEEE Symposium on Computer-Based Medical Systems
DOIs	https://doi.org/10.1109/CBMS.2001.941761
State	Published - 2001

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title = "A statistical 3-D segmentation algorithm for classifying brain tissues in multiple sclerosis",

abstract = "Deterministic annealing (DA) algorithm has been successfully used to segment the simulated magnetic resonance normal brain images [1]. This paper presents the results of applying it to simulated and actual clinical multiple sclerosis (MS) magnetic resonance (MR) brain data with the objective of developing a computer-aided diagnostic (CAD) tool for early detection and follow-up for MS lesions. Multiple sclerosis lesions on T1 simulated brain images [2] can be obtained by segmenting the image data using deterministic annealing algorithm and then performing further arithmetic manipulations on these segmented images. Lesions in clinical T2 multiple sclerosis MR images are isolated entities in the segmented images of white matter, gray matter and cerebrospinal fluid. The achieved results demonstrate the ability of deterministic annealing algorithm to isolate MS lesions from clinical MR data, thus providing a potential CAD tool for the clinicians.",

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