Computer-aided diagnostic system for breast cancer by detecting microcalcifications

摘要

Abstract: X-ray mammography is an important diagnostic imaging modality for early detection of breast cancer. The early detection of the breast cancer can reduce the mortality of middle-aged women, especially in the developed country. Computer aided diagnosis (CAD) technologies have been developed to assist radiologists to detect breast cancer in early stage. This paper presents a KCAD (KAIST Computer-Aided Diagnosis) system for detection of breast cancer, which consists of personal computer, high resolution X-ray film scanner, high-resolution display and application softwares. There are three algorithms implemented in the application softwares. The first algorithm is the enhancement of the digitized X-ray mammograms based on the gradient operation. The second algorithm is to detect the clustered microcalcifications based on the statistical texture analysis, which is called surrounding region dependence method (SRDM). The SRDM matrix is computed for each ROI, which has 128 by 128 pixels. The SRDM matrix characterizes the small and high-density regions in mammograms, which can be recognized as microcalcifications. Four textural features are computed from the SRDM matrix. Using these features, the neural network classifies the regions as normal or microcalcification region. The third algorithm is the classification of the clustered microcalcifications as malignant or benign based on the shape analysis. The microcalcifications are segmented using SRDM. Four shape features are extracted from each microcalcification and five representatives are computed for each shape feature. Twenty-one shape-based values containing the number of microcalcifications are used to classify the region as malignant or benign. These algorithms are verified by real experiments. !10