Content Based Image Retrieval System (CBIR) is used to retrieve images similar to the query image. These systems have a wide range of applications in various fields. Medical subject headings, key words, and bibliographic references can be augmented with the images present within the articles to help clinicians to potentially improve the relevance of articles found in the querying process. In this research, image feature analysis and classification techniques are explored to differentiate images found in biomedical articles which have been categorized based on modality and utility. Examples of features examined in this research include: features based on different histograms of the image, texture features, fractal dimensions etc. Classification algorithms used for categorization were 1) Mean shift clustering 2) Radial basis clustering. Different combinations of features were selected for classification purposes and it was observed that features incorporating soft decision based HSV histogram features give the best results. A library of features was then developed which can be used in RapidMiner. Experimental results for various combinations of features have also been included --Abstract, page iii
