Content-Based Medical Image Retrieval Based on Image Feature Projection in Relevance Feedback Level

Abstract

The purpose of this study is to design a content-based medical image retrieval system and provide a new method to reduce semantic gap between visual features and semantic concepts. Generally performance of the retrieval systems based on only visual contents decrease because these features often fail to describe the high level semantic concepts in user’s mind. In this paper this problem is solved using a new approach based on projection of relevant and irrelevant images in to a new space with low dimensionality and less overlapping in relevance feedback level. For this purpose, first we change the feature space using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques and then classify the feedback images applying Support Vector Machine (SVM) classifier. The proposed framework has been evaluated on a database consisting of 10,000 medical X-ray images of 57 semantic classes. The obtained results show that the proposed approach significantly improves the accuracy of retrieval system.

Keywords

• Content-based image retrieval
• semantic gap
• relevance feedback
• principal component analysis
• linear discriminant analysis
• support vector machine