Fuzzy C-Means based on Minkowski distance for liver CT image segmentation

This paper presents a Fuzzy C-Means based image segmentation approach that benefits from the Minkowski distance as the dissimilarity measure, denoted as FCM-M, instead of the traditional Euclidean distance, herein identified as FCM-E. The proposed approach was applied on Liver CT images, and a thorough comparison between both methods was carried out. FCM-M provided better accuracy when compared to the traditional FCM-E, with an area under the ROC curve of 85.44% and 47.96%, respectively. In terms of statistical significant analysis, a twofold benefit was obtained from using the proposed approach: the performance of the image segmentation procedure was maintained, or even slightly increased in some situations, while the CPU processing time was significantly decreased. The advantages inherent to the proposed FCM-M pave the way to a whole new chain of fully automatic segmentation methods.