Title :
Automatic Analysis of HER-2/neu Immunohistochemistry in Breast Cancer
Author :
Chang, Chuan-Yu ; Huang, Ya-Chi ; Ko, Chien-Chuan
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Yunlin Univ. of Sci. & Technol., Yunlin, Taiwan
Abstract :
Breast cancer is the second most common cancer in females, after lung cancer in the world. In Taiwan, there are about 7500 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. Therefore, in this paper, we proposed a 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 SVM classifier. According to the experimental results, the proposed method can automatically and effectively asses the score of IHC images.
Keywords :
cancer; feature extraction; learning (artificial intelligence); medical image processing; support vector machines; HER-2/neu immunohistochemistry; IHC image; ROI extraction; SVM classifier; Taiwan; automatic analysis; breast cancer; cervical cancer; feature extraction; feature selection; female cancer; lung cancer; Breast cancer; Entropy; Feature extraction; Image color analysis; Immune system; Support vector machines; IHC image; ROI; SFFS; SVM classifier; breast cancer;
Conference_Titel :
Innovations in Bio-Inspired Computing and Applications (IBICA), 2012 Third International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4673-2838-8
DOI :
10.1109/IBICA.2012.72