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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11741

Title: A Computer-Aided System for Mass Detection and Classification in Digitized Mammograms
Authors: Yang, Sheng-Chih;Wang, Chuin-Mu;Chung, Yi-Nung;Hsu, Giu-Cheng;Lee, San-Kan;Chung, Pau-Choo;Chang, Chein-I
Contributors: 電機工程學系
Keywords: Classification;Detection;Entropic thresholding;Fractal dimension (FD);Joint entropy(JE);Local entropy (LE);Probabilistic neural network (PNN);Spatial gray level difference matrix (SGLDM);Texture feature;Shape feature
Date: 2005-10
Issue Date: 2012-07-02T02:06:32Z
Publisher: Center for Biomedical Engineering, Taiwan, TAIWAN
Abstract: This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholdiing techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.
Relation: Biomedical Engineering Applications, Basis,and Communications, 17(5): 215-228
Appears in Collections:[電機工程學系] 期刊論文

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