AUTOMATIC BREAST CANCER ASSESSMENT IN HER-2/neu IMMUNOHISTOCHEMISTRY

Abstract

Breast cancer is the second most common cancer in females, after lung cancer in the world. In Taiwan, there are about 8500 female suffering from breast cancer every year. The incidence of breast cancer has exceeded cervical cancer and has become the most common female cancer. Immunohistochemistry (IHC) image is widely applied to the diagnosis of breast cancer, but it requires a great deal of manpower and time. The IHC images are scoring as {0+, 1+, 2+ and 3+} corresponding to no staining, weak, moderate and strong staining, respectively. With the growing of image processing techniques, computer-assisted technologies are the best solution to reduce the variability of pathologists evaluation and provide highly specific per-cell information. Therefore, in this paper, we proposed an automatic method to assess the grade of breast cancer in IHC images. The proposed method consists of four steps, including ROI extraction, feature extraction, feature selection and a hierarchical SVM classifier. The hierarchical SVM classifier is utilized to score the IHC images into 0+ (no staining), 1+ (weak), 2+ (moderate) and 3+ (strong staining). According to the experimental results, the proposed method can automatically and effectively asses the score of IHC images; it provides important information to help physicians treat breast cancer.