A Hybrid Feature Modeling Approach for Content-Based Medical Image Retrieval

With the proliferation of various imaging based diagnostic procedures in the healthcare field, patient-specific scan images constitute huge volumes of data that needs to be well-organized and managed for supporting clinical decision support applications. One such crucial application with a significant impact on point-of-care treatment quality is a Content Based Medical Image Retrieval (CBMIR) system that can assist doctors in disease diagnosis based on similar image retrieval. Medical images are multi-dimensional and often contain manifold information, due to which efficient techniques for optimal feature extraction from large-scale image collections are the need of the day. In this paper, an efficient CBMIR model is proposed that is built on multi-level feature sets extracted from medical images. Four different feature extraction techniques are used to optimally represent images in a multi-dimensional feature space, for facilitating classification using supervised machine learning algorithms and top-k similar image retrieval. Experimental validation of proposed model on the standard ImageCLEF 2009 dataset containing 12,560 X-ray images across 116 classes showed promising results in terms of classification accuracy of 85.91%.