Medical Image Segmentation Using Seeded Fuzzy C-Means: A Semi-Supervised Clustering Algorithm

One of the least invasive means of diagnosing certain diseases is through the use of imaging tests. Automated analysis usually begins by detecting the regions to be investigated and extracting relevant information such as shape and texture. In this paper, we propose a semi-supervised clustering algorithm called Seeded Fuzzy C-means to segment regions of interest in medical images. Seeded Fuzzy C-means is based on the well-known Fuzzy C-means; however, it also uses information provided by the physician to insert constraints during group choices. In this way, the element is placed in a group with a higher degree of certainty. We evaluated the proposed algorithm on a total of 2,200 images in leukemia, skin cancer, cervical cancer, and glaucoma image databases. Two semi-supervised clustering algorithms were implemented from literature to perform this analysis. The results illustrate the ability of the algorithm to efficiently assist physicians in detecting regions of interest, as it achieved an "Excellent" Kappa index in most of the tests.