Title page for ETD etd-050809-095211

Document Type dissertation

Author Name Swathanthira Kumar, Murali Murugavel M

Email Address murali.murugavel at gmail.com

URN etd-050809-095211

Title Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks

Degree PhD

Department Mechanical Engineering

Advisors
John M. Sullivan, Jr., Advisor
Brian J. Savilonis, Committee Member
Matthew O. Ward, Committee Member
Gregory S. Fischer, Committee Member
Mark W. Richman, Graduate Committee Rep

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

Date of Presentation/Defense 2009-05-05

Availability unrestricted

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.

Files
SwathanthiraKumar.pdf

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Document Type	dissertation
Author Name	Swathanthira Kumar, Murali Murugavel M
Email Address	murali.murugavel at gmail.com
URN	etd-050809-095211
Title	Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks
Degree	PhD
Department	Mechanical Engineering
Advisors	John M. Sullivan, Jr., Advisor Brian J. Savilonis, Committee Member Matthew O. Ward, Committee Member Gregory S. Fischer, Committee Member Mark W. Richman, Graduate Committee Rep
Keywords	fibroglandular adipose CSF GM brain segmentation segmentation neural networks PCNN brain cropping small mammals breast cropping WM Expectation Maximization Gaussian Mixture Models
Date of Presentation/Defense	2009-05-05
Availability	unrestricted
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.
Files	SwathanthiraKumar.pdf