Identifying abdominal organs using robust fuzzy inference model

Chien Cheng Lee, Pau-Choo Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The paper proposes to identify abdominal organs from CT image series, by using the shape descriptors, fuzzy rules, and fuzzy-inference-based Radial Basis Function (RBF) neural network. A number of descriptors are applied to ascertain the segmented regions and to form fuzzy rules in our inference system. It has been demonstrated that the RBF neural network and the fuzzy inference are functional equivalent. The traditional RBF network takes Gaussian functions as its basis functions and adopts the least squares criterion as the objective function. However, it suffers from two major problems. First, it is difficult to approximate constant values. Second, when the training patterns incur a large error, the network will interpolate these training patterns incorrectly. In order to cope with these problems, a robust RBF network is proposed in this paper to recognize the organ of interest.

Original languageEnglish
Title of host publicationConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control
Pages1289-1294
Number of pages6
Volume2
Publication statusPublished - 2004
EventConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control - Taipei, Taiwan
Duration: 2004 Mar 212004 Mar 23

Other

OtherConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control
CountryTaiwan
CityTaipei
Period04-03-2104-03-23

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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