A Hybrid Multifarious Clustering Algorithm for the Analysis of Memmogram Images

Abstract

A number of clustering algorithms were used to analyze many databases in the field of image clustering. The main objective of this research work was to perform a comparative analysis of the two of the existing partitions based clustering algorithms and a hybrid clustering algorithm. The results verification done by using classification algorithms via its accuracy. The perfor-mance of clustering and classification algorithms were carried out in this work based on the tumor identification, cluster quality and other parameters like run time and volume complexity. Some of the well known classification algorithms were used to find the accuracy of produced results of the clustering algorithms. The performance of the clustering algorithms proved mean-ingful in many domains, particularly k-Means, FCM. In addition, the proposed multifarious clustering technique has revealed their efficiency in terms of performance in predicting tumor affected regions in mammogram images. The color images are converted in to gray scale images and then it is processed. Finally, it is identified the best method for the analysis of finding tumor in breast images. This research would be immensely useful to physicians and radiologist to identify cancer affected area in the breast.