Automatic classification of clustered microcalcifications by a multiple expert system

被引:39
作者
De Santo, M
Molinara, M
Tortorella, F
Vento, M
机构
[1] Univ Cassino, Dipartimento Automazione Elettromagnetismo Ingn I, I-03043 Cassino, Italy
[2] Univ Salerno, Dipartimento Ingn Informazione & Ingn Elettria, I-84084 Fisciano, SA, Italy
关键词
breast cancer; clustered microcalcifications; mammography; multiple expert systems; computer aided diagnosis; COMPUTER-AIDED DETECTION; DIGITAL MAMMOGRAMS; NEURAL-NETWORK; DIGITIZED MAMMOGRAMS; DECISION COMBINATION; BREAST-CANCER; DIAGNOSIS; ALGORITHM; FEATURES; MASSES;
D O I
10.1016/S0031-3203(03)00004-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A significant visual clue of the disease is the presence of clusters of microcalcifications. The automatic recognition of malignant clusters of microcalcifications, which could be very helpful for diagnostic purposes, is a very difficult task because of the small size of the microcalcifications and of the poor quality of the mammographic images. In this paper we propose a novel approach for classifying clusters of microcalcifications, based on a Multiple Expert System; such system aggregates several experts, some of which are devoted to classify the single microcalcifications while others are aimed to classify the cluster considered as a whole. The final output results from the suitable combination of the two groups of experts. The tests performed on a standard database of 40 mammographic images have confirmed the effectiveness of the approach. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:1467 / 1477
页数:11
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