Name
Title | ||
|---|---|---|
Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images. |
Abstract | ||
|---|---|---|
Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10916-017-0773-9 | J. Medical Systems |
Keywords | Field | DocType |
Breast cancer grading,Histopathology image,Maximized inter-class weighted mean,Mitosis detection | Data mining,Mitosis,Pattern recognition,Breast cancer,Segmentation,Histopathology,Support vector machine,Artificial intelligence,Magnification,Medicine,Pathology | Journal |
Volume | Issue | ISSN |
41 | 9 | 1573-689X |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramin Nateghi | 1 | 2 | 0.70 |
| Habibollah Danyali | 2 | 49 | 11.07 |
| Mohammad Sadegh Helfroush | 3 | 70 | 11.30 |