Local versus Global Features for Content-Based Image Retrieval

It is now recognized in many domains that content based image retrieval CBIR from a database of im ages cannot be carried out by using completely auto mated approaches One such domain is medical ra diology for which the clinically useful information in an image typically consists of gray level variations in highly localized regions of the image Currently it is not possible to extract these regions by auto matic image segmentation techniques To address this problem we have implemented a human in the loop a physician in the loop more speci cally approach in which the human delineates the pathology bearing regions PBR and a set of anatomical landmarks of the image at the time the image is entered into the database From the regions thus marked our approach applies low level computer vision and image processing algorithms to extract features related to the variations of gray scale texture shape etc The extracted fea tures create an index that characterizes the image To form an image based query the physician rst marks the PBR s The system then extracts the relevant im age features computes the distance of the query image to all image indices in the database and retrieves the n most similar images Our approach is based on the assumption that medical image characterization must contain features local to the PBR s The focus of this paper is to assess the utility of localized versus global features for the domain of HRCT images of the lung and to evaluate the system s sensitivity to physician subjectivity in delineating the PBR s