Implementing Fuzzy C-Means Clustering for Medical Image Analysis

Precise segmentation and grouping are essential in medical image analysis to recognize and diagnose different illnesses. This paper investigates the use of fuzzy C-Means (FCM) clustering in medical image analysis, with a focus on how well it works for tasks involving picture segmentation and clustering. Compared to conventional hard clustering techniques, FCM is a soft clustering technique that gives each data point a degree of membership to several clusters, allowing for a more nuanced comprehension of complicated visual data. When choosing the ideal number of clusters, the Fuzzy Partition Coefficient (FPC) is utilized as a crucial indicator to assess the caliber of the clustering outcomes. Extensive testing shows that the FCM algorithm performs better at segmenting medical images, especially when there are exact borders between various tissues or structures.

کلیدواژه ها:

Fuzzy C-means clustering ، Fuzzy Partition Coefficient ، Image clustering ، Image segmentation