Abstract
In this paper, we propose a novel method for accurate automated discrimination of breast tumors (carcinoma, fibroadenoma, and cyst). We defined 199 features related to diagnostic observations noticed when a doctor judges breast tumors, such as internal echo, shape, and boundary echo. These features included novel features based on a parameter of log-compressed K distribution, which reflect physical characteristics of ultrasonic B-mode imaging. Furthermore, we propose a discrimination method of breast tumors by using an ensemble classifier based on the multiclass AdaBoost algorithm with effective features selection. Verification by analyzing 200 carcinomas, 30 fibroadenomas, and 30 cysts showed the usefulness of the newly defined features and the effectiveness of the discrimination by using an ensemble classifier trained by AdaBoost. © 2011 Wiley Periodicals, Inc. Electron Comm Jpn, 94(9): 18–29, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10356
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