CLUSTERING OF MEDICAL IMAGES USING MULTIWAVELET TRANSFORM AND K-MEANS ALGORITHMS

Abstract

Imaging data and biological records are both included in the field of medical informatics. Medical image clustering is an important area of research that is getting increased interest in both academia and the health professions. It addresses the issues of medical diagnosis, analysis, and education. Several medical imaging methods and applications based on data mining have been made and tested to deal with these problems. This paper looks at how image classification techniques diagnose diseases in the human body. It concerns the imaging modalities, dataset, and the pros and cons of each method. An optimal clustering technique of medical images using multiwavelet transforms proposed that combines the multiwavelet transform filterbanks with the k-means clustering algorithm to improve the performance and get a clinically meaningful clustering shape. In comparison with other methods of clustering, it was shown that this method has a much higher cluster classification than those published before. A user-friendly Matlab program has been constructed to test and get the results of the proposed algorithms