Rank M-type radial basis functions network for medical image processing applications

José A. Moreno-Escobar; Francisco J. Gallegos-Funes; Volodymyr I. Ponomaryov

doi:10.1117/12.699250

27 February 2007 Rank M-type radial basis functions network for medical image processing applications

José A. Moreno-Escobar, Francisco J. Gallegos-Funes, Volodymyr I. Ponomaryov

Proceedings Volume 6497, Image Processing: Algorithms and Systems V; 649715 (2007) https://doi.org/10.1117/12.699250
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

In this paper we present the capability of the Rank M-Type Radial Basis Function (RMRBF) Neural Network in medical image processing applications. The proposed neural network uses the proposed RM-estimators in the scheme of radial basis function to train the neural network. The RMRBF-based training is less biased by the presence of outliers in the training set and was proved an accurate estimation of the implied probabilities. Other RBF based algorithms were compared with our approach in pdf estimation on the microcalcification detection in mammographic image analysis. From simulation results we observe that the RMRBF gives better estimation of the implied pdfs and has show better classification capabilities.

Citation Download Citation

José A. Moreno-Escobar, Francisco J. Gallegos-Funes, and Volodymyr I. Ponomaryov "Rank M-type radial basis functions network for medical image processing applications", Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 649715 (27 February 2007); https://doi.org/10.1117/12.699250

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