Centroid Neural Network with Spatial Constraints

Authors:
Dong-Chul Park
Affiliations:
Dept. of Information Engineering, Myong Ji University, Korea
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

A Centroid Neural Network with spatial constraints (CNN-S) is proposed in this paper. The spatial constraints are applied to the Centroid Neural Network(CNN) algorithm to reduce noise in Magnetic Resonance(MR) images. MR image segmentation is performed to illustrate the application of the proposed algorithm. The proposed algorithm incorporates a novel approach of using the weights of attributes to evaluate the roles of the latter. Experiments and results on MR images from Internet Brain Segmentation Repository(IBSR) show that the proposed algorithm provides a superior performance over other algorithms such as Fuzzy C-Means(FCM)and Fuzzy C-Means with spatial constraints(FCM-S).