Breast Cacer Diagnosis from Biopsy Images Using a Fully Automatic Method

摘要：

The most reliable way to diagnose breast cancer in the current practice of medicine is through pathological examination of a biopsy which has a certain level of subjectivity. To reduce this subjectivity and have a mathematical model for diagnosing breast cancer tissues, a fully automatic method based on microscopic biopsy image is presented. The novel technique is based on a four-step procedure: the pathologic images are denoised and enhanced based on k-nearest-neighbor (KNN) and histogram equalization method; morphology features are extracted using wavelet moment invariants; a rough set (RS) is applied to reduce features dimensions and select the best features; a multi-category proximal support vector machine (MPSVM) is designed to reliably differentiate normal, in situ and invasive breast cancer tissues. The experiments demonstrate that the proposed method is effective and useful for classifying breast tumors.

作者: Lijuan Liu Mingrong Deng

作者单位: School of Management, Zhejiang University, Hangzhou,China Dept of Information Engineering, Zhejiang School of Management, Zhejiang University, Hangzhou,China

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）