Faculty Advisor or Committee Member

John M. Sullivan, Jr., Advisor

Faculty Advisor or Committee Member

Brian J. Savilonis, Committee Member

Faculty Advisor or Committee Member

Matthew O. Ward, Committee Member

Faculty Advisor or Committee Member

Gregory S. Fischer, Committee Member

Faculty Advisor or Committee Member

Mark W. Richman, Committee Member

Identifier

etd-050809-095211

Abstract

The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image 'signature' based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard's Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye's intensity delineation capability in grayscale image segmentation tasks.

Publisher

Worcester Polytechnic Institute

Degree Name

PhD

Department

Mechanical Engineering

Project Type

Dissertation

Date Accepted

2009-05-08

Accessibility

Unrestricted

Subjects

fibroglandular, adipose, CSF, GM, brain segmentation, segmentation, neural networks, PCNN, brain cropping, small mammals, breast cropping, WM, Expectation Maximization, Gaussian Mixture Models

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