Medical image analysis by probabilistic modular neural networks

Yue Wang, Tulay Adah, Sun-Yuan Kung

Research output: Contribution to conferencePaper

Abstract

A probabilistic neural network based technique is presented for unsupervised quantification and segmentation of the brain tissues from magnetic resonance image. The problem is formulated as distribution learning and relaxation labeling that may be particularly useful in quantifying and segmenting abnormal brain tissues where the distribution of each tissue type heavily overlaps. The new technique utilizes suitable statistical models for both the pixel and context images. The quantification is achieved by model-histogram fitting of probabilistic self-organizing mixtures and the segmentation by global consistency labeling through a probabilistic constraint relaxation network. Experimental results show the efficient and robust performance of the new algorithms.

Original languageEnglish (US)
Pages654-663
Number of pages10
StatePublished - Dec 1 1997
Externally publishedYes
EventProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97 - Amelia Island, FL, USA
Duration: Sep 24 1997Sep 26 1997

Other

OtherProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97
CityAmelia Island, FL, USA
Period9/24/979/26/97

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Wang, Y., Adah, T., & Kung, S-Y. (1997). Medical image analysis by probabilistic modular neural networks. 654-663. Paper presented at Proceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97, Amelia Island, FL, USA, .