Etd

Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks

Público

Contenido Descargable

open in viewer

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.

Creator
Colaboradores
Degree
Unit
Publisher
Language
  • English
Identifier
  • etd-050809-095211
Palabra Clave
Advisor
Committee
Defense date
Year
  • 2009
Date created
  • 2009-05-08
Resource type
Rights statement
Última modificación
  • 2023-11-10

Las relaciones

En Collection:

Elementos

Elementos

Permanent link to this page: https://digital.wpi.edu/show/k35694421