Breast tumor segmentation on medical images using combination of fuzzy clustering and threshold

Abstract

Breast tumor segmentation and boundary detection are crucial stages in breast cancer therapy and follow-up. Radiologists can minimize the high workload associated with screening for breast cancer by automating this complex process. This article proposes a system for segmenting breast and unaffected areas (breast) tumors on medical images using a combination of fuzzy clustering and thresholding (FCMT) tools. This computer diagnostic method works with each section of the mammary gland without learning segmentation and definition of boundaries. As part of the approbation, two databases of breast mammogram images were used. To increase the image quality, we used pre-processing techniques such as contrast augmentation before applying the FCMT for segmentation. To assess the effectiveness of the devised approach, the Mean Square Error, dice coefficient, Structured Similarity Index, Peak Signal-to-Noise Ratio, accuracy, and sensitivity were computed. Using the proposed FCMT segmentation technique, a mean intersection over union (IoU) of 93.85 was attained. The presented approach is more resilient and accurate in segmenting tumor progression on medical images, according to the findings of the experiments.