Medical Image Indexing and Retrieval using Multiple Features

Automatic medical image classification refers to assigning an medical image into a class, among a number of image categories. Due to computational complexity, it is an important task in the content based image retrieval (CBIR). In this paper, we propose a method for classification and retrieval of medical images using multi-feature extraction method. Here, edge and patch based methods are used to extract feature vectors. We demonstrate that these features provide discriminative information useful for classification of medical images by considering eight categories of images, namely, hand, skull, chest, mammogram, chest side view, skull side view, knee and neck. Using multi-feature extraction method improves the accuracy of CBIR. In this paper, three different types of similarity measures are used for comparing the query image with database images. The experimental results suggest that the proposed method has the ability to retrieve relevant images for a given input query image and provides good retrieval performance than compared with the single feature extraction method.