Query Expansion Based on Top-Ranked Images for Content-Based Medical Image Retrieval

Recently the collection of varied digital image databases has increased. Many users have found that searching and retrieving required images from large collections are very difficult tasks, and successful and effective retrieval methods were developed to provide an effective and efficient search and retrieval process. In most content-based image retrieval (CBIR) methods, various visual features have been considered indirect to retrieve the images from databases. Content-based medical image retrieval (CBMIR), like any CBIR method, is a technique for retrieving medical images on the basis of automatically derived image features, such as colour and texture. Although a number of methods and approaches have been suggested, retrieval performance remains one of the most challenging problems in current CBMIR studies, this due to the well-known `semantic gap' issue that exists between machine-captured low-level image features and human-perceived high-level semantic concepts. To bridge this gap, much research has been proposed. This study proposes two expansion methods to increase and enhance precision of the retrieval model; both of our proposed methods depend on top-ranked images. Our first expansion method is to reformulate the new expand query image based on mean values for features of top-ranked images, while the second method is based on selecting the most important features only. The expansion process was performed on eighteen colour features and twelve texture features extracted from two common medical images datasets, Kvasir and PH2. Experimental results show that our proposed methods performed better and have a precision of 95.8% and 80.7% for Kvasir and PH2 data set, respectively.