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Publication Date

2007-01-01

Availability

UM campus only

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering (Engineering)

Date of Defense

2007-09-20

First Committee Member

Akmal Younis - Committee Chair

Second Committee Member

Mansur Kabuka - Committee Member

Third Committee Member

Pardip M. Pattany - Committee Member

Fourth Committee Member

Nigel John - Committee Member

Abstract

Two brain segmentation approaches based on Hidden Markov Models are proposed. The first approach aims to segment normal brain 3D multi-channel MR images into three tissues WM, GM, and CSF. Linear Discriminant Analysis, LDA, is applied to separate voxels belonging to different tissues as well as to reduce their features vector size. The second approach aims to detect MS lesions in Brain 3D multi-channel MR images and to label WM, GM, and CSF tissues. Preprocessing is applied in both approaches to reduce the noise level and to address sudden intensity and global intensity correction. The proposed techniques are tested using 3D images from Montereal BrainWeb data set. In the first approach, the results were numerically assessed and compared to results reported using techniques based on single channel data and applied to the same data sets. The results obtained using the multi channel HMM-based algorithm were better than the results reported for single channel data in terms of an objective measure of overlap, Dice coefficient, compared to other methods. In the second approach, the segmentation accuracy is measured using Dice coefficient and total lesions load percentage

Keywords

Image Egmentation; Hidden Markov Models; Medical Imaging

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