Prostate Cancer Detection using Texture and Clinical Features in Ultrasound Image

In this paper, we propose a new computer aided diagnosis method for prostate cancer detection in ultrasound image. With multi resolution autocorrelation texture features and clinical features such as location and shape of tumor, we could maintain high specificity with high sensitivity for prostate cancer detection. Multi resolution autocorrelation can detect cancer suspicious region efficiently with high specificity and sensitivity. And clinical features filters out false positive region by prior knowledge of location and the shape of prostate cancer. Those features are put to Support Vector Machine to classify cancer region or non-cancer region. The proposed method will be helpful in making a more reliable diagnosis,and increasing diagnosis efficiency.