Cancerous Nuclei Detection and Scoring in Breast Cancer Histopathological Images
This work addresses early detection and prognosis for breast cancer patients, but it is incremental as it builds on existing methods in histopathological grading.
The paper tackled the problem of detecting and scoring nuclear pleomorphism in breast cancer histopathological images, achieving an accuracy of 86.6% in detecting cancerous nuclei.
Early detection and prognosis of breast cancer are feasible by utilizing histopathological grading of biopsy specimens. This research is focused on detection and grading of nuclear pleomorphism in histopathological images of breast cancer. The proposed method consists of three internal steps. First, unmixing colors of H&E is used in the preprocessing step. Second, nuclei boundaries are extracted incorporating the center of cancerous nuclei which are detected by applying morphological operations and Difference of Gaussian filter on the preprocessed image. Finally, segmented nuclei are scored to accomplish one parameter of the Nottingham grading system for breast cancer. In this approach, the nuclei area, chromatin density, contour regularity, and nucleoli presence, are features for nuclear pleomorphism scoring. Experimental results showed that the proposed algorithm, with an accuracy of 86.6%, made significant advancement in detecting cancerous nuclei compared to existing methods in the related literature.