Abstract
Of late, the amount of digital X-ray images that are produced in hospitals is increasing incredibly fast. Efficient storing, processing and retrieving of X-ray images have thus become an important research topic. With the exponential need that arises in the search for the clinically relevant and visually similar medical images over a vast database, the arena of digital imaging techniques is forced to provide a potential and path-breaking methodology in the midst of technical advancements so as to give the best match in accordance to the user’s query image. CBIR helps doctors to compare X-rays of their current patients with images from similar cases and they could also use these images as queries to find the similar entries in the X-ray database. This paper focuses on six different classes of X-ray images, viz. chest, skull, foot, spine, pelvic and palm for efficient image retrieval. Initially the various X-rays are automatically classified into the six-different classes using BPNN and SVM as classifiers and GLCM co-efficient as features for classification. Indexing is done to make the retrieval fast and retrieval of similar images is based on the city block distance. Â
Highlights
CBIR techniques could be valuable to radiologists in accessing medical images by identifying similar images in image archives that could assist with decision support
This paper focuses on six different classes of X-ray images namely chest, skull, spine, foot, pelvic and palm
Preprocessing of X-Ray images for noise reduction and enhancement is done by average filter and connected component labelling is carried out to segment the region of interest
Summary
CBIR techniques could be valuable to radiologists in accessing medical images by identifying similar images in image archives that could assist with decision support. Radiologists always utilize broader patients specific or demographic knowledge such as clinical history or results of other tests, in their decision making process as such. It is expected that decision support system would incorporate these data as well. The role for CBIR in medical applications is potentially very powerful, in addition to enabling similarity based indexing, the framework could provide computer aided diagnostic support based on image content as well as on other meta-data associated with medical images. In mammography Xray interpretation, there is a variation in sensitivity, specificity and area under the receiver operating characteristic curve among radiologists [1,2]. The efficacy of the image retrieval system would be greatly dependent on the percentage accuracy of the required image, for which it is being trained
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