Evaluation of Histopathological Images Segmentation Techniques for Breast Cancer Detection

Breast cancer classification and detection using histopathological images is considered a difficult process due to the complexity of the characteristics of histopathology images. This paper presents an automated system for the classification and detection of breast cancer from microscopic histological images where the images are classified into benign, in situ, invasive, and normal. The proposed approach involves several steps which are image preprocessing (Enhancement), image segmentation, feature extraction, feature selection, and finally image classification. The proposed approach utilizes and compares two segmentation methods which are clustering and Global thresholding using Otsu's method. Initially, images are segmented using K-means and Global thresholding methods. Then, features (morphological and texture) are extracted from the images for the two methods. Moreover, feature selection is done by using Principal Component Analysis (PCA). Finally, K-means and Global thresholding methods are compared in the classification process by using different classifiers. The results show better performance for the Global thresholding.