A review of intelligent content-based indexing and browsing of medical images

Abstract

Physicians are beginning to be able to gain access, through the Internet, to the world's collections of multimedia medical information such as MRI (magnetic resonance imaging) and CT (computer tomography) image archives, videos of surgical operations and medical lectures, textual patient records and media annotations. New techniques and tools are needed to represent, index, store and retrieve digital content efficiently across large collections. In this review, we trace the development of visual information systems for healthcare and medicine from Picture Archiving and Communications Systems (PACS) to the recent advances in content-based image retrieval, whereby images are retrieved based on their visual content similarity - that is, colour, texture, and shape. Medical images, unlike consumer-oriented images, pose additional challenges to content-based image retrieval, in that visual features of normal and pathological images are typically separated by only subtle differences in visual appearance. Intelligent image retrieval and browsing therefore requires a combination of prior knowledge of the medical domain, image content and image annotation analysis. To this end, we also overview theI-Browse project, conducted jointly by the Clinical School of the University of Cambridge and the City University of Hong Kong, which aims to develop techniques which enable a physician to search over image archives through a combination of semantic and iconic contents

Combination Independent Content Feature with Watermarking Annotation for Medical Image Retrieval

“…From Fig.1, it could be seen that 3 IC is approximate to the original image with its size being quarter of the original, which works as the ICF of the medical image, and the others show some details of the original image. 3 IC is reasonable to be considered as a certain low frequency component of the original image, while the others are the high frequency ones.…”

Gaps in content-based image retrieval

“…According to Tang et al, different combinations between text and images for input and output might be used [4]. In general, it is easier to make inferences from text to images than from images to text.…”

“…Although this approach was originally developed for multimedia repositories such as the Word Wide Web, techniques for content-based access to medical image repositories are a subject of high interest in recent research, and remarkable efforts have been reported [3,4,5]. In particular, CBIR for picture archiving and communication systems (PACS) discussed in [6,7,8] can make a significant positive impact to health informatics and health care.…”

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

“…The radiology department of the University Hospitals of Geneva alone produces currently more than 25, 000 images per day, mostly in multi-slice tomographic series. The importance of retrieval of medical images was identified early [8,9,10] and a large number of projects has started to index various kinds of medical images [11]. Not all of the projects are analysing the visual image content, some simply use the accompanying textual information for retrieval [12].…”