Automated Object Extraction for Medical Image Retrieval Using the Insight Toolkit (ITK)

Abstract

Abstract. Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten years. It has the potential to help better managing the rising amount of visual medical data. One of the most frequent application fields for contentbased medical image retrieval (CBIR) is diagnostic aid. By submitting an image showing a certain pathology to a CBIR system, the medical expert can easily find similar cases. A major problem is the background surrounding the object in many medical images. System parameters of the imaging modalities are stored around the images in text as well as patient name or a logo of the institution. With such noisy input data, image retrieval often rather finds images where the object appears in the same area and is surrounded by similar structures. Whereas in specialised application domains, segmentation can focus the research on a particular area, PACS-like (Picture Archiving and Communication System) databases containing a large variety of images need a more general approach. This article describes an algorithm to extract the important object of the image to reduce the amount of data to be analysed for CBIR and focuses analysis to the important object. Most current solutions index the entire image without making a difference between object and background when using varied PACS-like databases or radiology teaching files. Our requirement is to have a fully automatic algorithm for object extraction. Medical images have the advantage to normally have one particular object more or less in the centre of the image. The database used for evaluating this task is taken from a radiology teaching file called casimage and the retrieval component is an open source retrieval engine called medGIFT.

Hit Noise Reduction in Some X-Ray Images

“…A number of advancements for X-ray inspection systems have recently been implemented in the image presented to the operator [1]. One of the identified problems of retrieval is that a large part of the images does not contain any important information for retrieval but rather noise [2]. Motion correction of coronary X-ray images can help sharpen image results.…”