Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

摘要

Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing theabnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affectedarea. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiatethem. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads toearly diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzyclustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First ordermoments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzyclustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is appliedto choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken fromthe Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separatedthe masses from mammogram images and proved to have good results in terms of cluster validity indices indicatingthe clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposedmethod proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists indiagnosing breast cancer at an early stage.