NEURAL NETWORKS FOR X-RAY IMAGE SEGMENTATION
Darryl Davis, Su Linying & Bernadette Sharp
12th International Conference On Control Systems And Computer Science,
Romania, 1999
Abstract
The paper addresses the application and challenges of using neural networks
to segment gray-level images; approaches we term direct perception. The
work described here is part of Intelligent Multi-Agent Image Analysis System,
which is being developed to promote the automated diagnosis and classification
of digital images. In this paper we show how neural networks may be successfully
segment medical X-ray images of the thigh. Weak use of Back Propagation
neural network, Counter Propagation neural network, Self-Organizing Feature
Map, Bi-directional Associative Memory, and a hybrid network consisted
of BP and SOFM. The comparisons among their performance are made, and some
feature extraction techniques used here are presented. A kind of one layer
neural network, known as WISARD, is used to validate the segmentation performance
based on the segmentation results. A highly general validation information,
the centroid curve of the segmented images, is proposed here.