Improved Retrieval of Medical Images Using K-means Clustering Algorithm

Due to the rapid and increasing progress of medical equipment and medical imaging machines, a large number of digital images are produced in therapeutic centers and stored in the large databases. Retrieving a set of images which are most similar to a query image is a major challenge in this field. A popular way to address this problems is to apply image retrieval based on a bag of visual words. A popular technique for producing visual words is to use K-means clustering algorithm. However, the effectiveness of K-means algorithm highly depends on the initial selection of cluster centroids which are selected randomly in the original K-means algorithm. Therefore, the image retrieval task is affected by poor clustering solutions due to random selection of cluster centroids. The goal of this paper is to overcome this problem and improving the accuracy of content based image retrieval from medical image databases using the optimal selection of initial points in K-means clustering algorithm. In the proposed method, after extracting SIFT features from the color images, the optimal centroid points are selected based on different ranges, weights and means of samples and visual words are created based on the selected centroid. The result of experiments on applying three different algorithms for selecting initial cluster centroids show that selecting the optimized initial points results in producing more discriminative visual words and provide favorable accuracy for medical image retrieval systems.