IFCM clustering segmentation based on genetic algorithm
Meiju Liu, Xiaozheng Yu, Yixuan Shi · 2021
In the early diagnosis of breast cancer(BC), computer aided design (CAD) is particularly important, and the accurate detection of breast mass in mammography plays an important role Objective: In order to distinguish the mass region from other background regions, an effective segmentation scheme was proposed in Mammographic Image Analysis Society (MIAS) segmentation Methods: First, the initial clustering center of intuitionistic fuzzy C-means (IFCM) is determined by genetic algorithm (GA), and then the image is segmented by IFCM algorithm to turn the random initial clustering center into a purpose-selected one, so as to ensure the optimal result of the final clustering center results: The average segmentation precision of MIAS.I images with noise level of 5%,7% and 9% were 90.15% and 86.85% and 87.31%. Conclusion: This method combines the advantages of the two algorithms to segment the location of mass more accurately and quickly.