Image Clustering Algorithms to Identify Complicated Cerebral Diseases. Description and Comparison

This article presents two algorithms developed based on two different techniques, from clusterization theory, namely k-means clustering technique and Fuzzy C-means technique, respectively. In this context, the study offers a sustained comparison of the two algorithms in order to properly choose one of them, depending on the image to be analyzed and the solution that is desired. Algorithms are used in image processing, respectively as application of image processing techniques in brain computed tomography image analysis. There were also compared the results obtained by running the algorithms with a different number of centroids, as well as the execution times of each algorithm in part. Image processing and obtaining the results presented in this document was made possible by using the MATLAB R2018b environment. This fact is possible because some components of the brain, such as the blood vessel network or the neural network, have a fractal arrangement, which makes it easy to analyze their structure, in order to provide predictions or treatments to patients in discussion afflicted with a serious brain disease, as accurately as possible.