Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6044
Title: Modular expert network approach to histogram thresholding
Authors: Li, CH
Tam, PKS
Keywords: Backpropagation
Cellular biophysics
Feedforward neural nets
Image segmentation
Medical expert systems
Medical image processing
Modules
Neural net architecture
Statistical analysis
Issue Date: Jul-1997
Publisher: SPIE-International Society for Optical Engineering
Source: Journal of electronic imaging, July 1997, v. 6, no. 3, p. 286-293 How to cite?
Journal: Journal of electronic imaging 
Abstract: The problem of histogram thresholding is tackled using a modular expert network. The modular expert network is a network of expert modules modulated by a gating network. The expert modules incorporate individual experts' opinions on the thresholding problem. The difficult task of integration of conflicting experts' opinions is achieved through a training of the gating network using backpropagation. The resulting network achieves accurate modeling of the solution mapping through the efficient combination of existing experts. Experimental results show the superior performance of the modular network over classical algorithms. In particular, a near-optimal solution was shown to be achievable using a small training set. Application to a real-world biomedical cell segmentation problem is also given.
URI: http://hdl.handle.net/10397/6044
ISSN: 1017-9909
EISSN: 1560-229X
DOI: 10.1117/12.269904
Rights: Chun Hung Li and Peter K. S. Tam, "Modular expert network approach to histogram thresholding," J. Electron. Imaging., 6(3), p. 286-293 (1997)
Copyright 1997 Society of Photo-Optical Instrumentation Engineers & Society for Imaging Science and Technology. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
http://dx.doi.org/10.1117/12.269904
Appears in Collections:Journal/Magazine Article

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