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Optical Engineering

Method for image segmentation based on an encoder-segmented neural network and its application
Author(s): Ning Li; Youfu Li
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Paper Abstract

An Encoder-Segmented Neural Network (ESNN)-based approach is proposed to improve the efficiency of image segmentation. The features are ranked according to the encoder indicators by which the insignificant features will be eliminated from the original feature vectors and the important features reorganized as the encoded feature vectors for the subsequent clustering. The ESNN developed can improve on the existing Fuzzy c-Means (FCM) algorithm in feature extraction. The cluster number selection can be accomplished automatically. This method was successfully implemented for automatic labeling of tissues in MR brain images. Experimental results show that the ESNN-based approach offers satisfactory performance in both efficiency and adaptability.

Paper Details

Date Published: 1 May 1999
PDF: 13 pages
Opt. Eng. 38(5) doi: 10.1117/1.602050
Published in: Optical Engineering Volume 38, Issue 5
Show Author Affiliations
Ning Li, City Univ. of Hong Kong (China)
Youfu Li, City Univ. of Hong Kong (Hong Kong)

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