A content based image retrieval using K-means algorithm

The study of Medical image retrieval, which is concerned with efficiently and effectively accessing desired medical images from varied and large image collections, has become more important, challenging and interesting. Until now extracting the lesions by automatic segmentation is considered the bottleneck in content-based medical image retrieval. Above that many approaches which are based on one-to-one blocks of an image are still sensitive to rotation, shifting and scaling. To address the last problems, we propose a new approach based on Hungarian algorithm which compares one block from the query image to all blocks from each image in the dataset and returns the closet matching. This comparison is based on features vector of gray-level intensity for each block. We used K-mean clustering algorithm to separate each window into K clusters. We will enforce the histogram of each cluster into Gaussian distribution, and then based on this histogram, the mean, variance and skewness are computed. The proposed content-based medical image retrieval approach gives acceptable results.