To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Mitosis Detection in Breast Cancer Histopathology Images using Statistical, Color and Shape-Based Features |
|
Author |
Tahir Mahmood, Sheikh Ziauddin, Ahmad R. Shahid and Asad Safi |
| Citation |
Vol. 25 No. 7 pp. 95-100 |
|
Abstract |
This paper presents an automated technique for mitosis detection in breast cancer histopathology images. Mitosis detection is the first step towards mitosis counting which is one of the metrics used for grading of breast cancer. A number of automated techniques for mitosis detection have been proposed in literature wherein different sets of features have been used such as textural, morphological, and statistical features. The proposed scheme uses statistical, shape, and color-based features. We use Support Vector Machine (SVM) to classify the candidates into mitosis and non-mitosis. Our experiments show that the proposed technique outperforms the existing techniques by achieving precision, recall, and F-measure of 0.80, 0.90, and 0.85, respectively. |
|
Keywords |
Breast Cancer; Mitosis Detection; Statistical Features; Support Vector Machine; HOG |
|
URL |
