Comparative Analysis of Segmentation Techniques using Histopathological Images of Breast Cancer

Breast cancer is one of the most common disease from which most of the females are suffering. Histopathological images play remarkable notch in the medical domain. Segmentation of breast cancer images for cell analysis is the utmost thought-provoking task because of uncertainties present in these images. Identifying cancerous cells effectively in histopathological images may help in early diagnosis of breast cancer. In this paper, comparative analysis of different state-of-art segmentation techniques have been carried out to extract cancerous cells in histopathological images using Triple Negative Breast Cancer (TNBC) dataset. The experimental results of segmentation techniques have been analysed with respect to Accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). Experiments using histopathological images validates Spatial Fuzzy C-Means with Level Set finds the cancerous breast cells effectively.