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

Visual data such as videos and images play an important role in medical diagnosis and training. The ability to express spatial and visual queries and to search for relevant visual information from image archives poses a major challenge for the development of advanced image indexing. Searching technologies must not only be robust but also be capable of scaling up to large collections across wide networks. Research issues in medical image retrieval With the advent of advanced medical imaging modalities such as CT, MRI, ultrasonograms, etc., images obtained by these new techniques as well as by conventional means (such as histo-logical slides) have become indispensable in medical diagnosis. At the same time, with the development of digital libraries, global communication infrastructures and image databases, large collections of medical images depicting various pathological cases can be accessed conveniently and remotely through high-speed networks. The availability of these large collections in recent years has shifted the problem from information availability to accessibility, that is, how to find the images we want easily and quickly. Content-based image indexing and retrieval aims to automate some of the manual process associated with indexing and retrieving large collections. However, since medical images of a given class (e.g. histological images, chest X-rays, or cardiac ultrasound images) are very similar and differ only in minute detail, it is expected that current content-based indexing techniques based solely on specific image characteristics , e.g. texture, colour, shape, may not be sufficiently precise for medical images. Investigating the feasibility of medical image indexing based on several conceptual levels of image content seems the key to developing an intelligent medical information system. Technologies must also be developed to understand and easily search across databases, handling variations on several levels of abstraction, for example, primitive image features and semantic meanings as well as high-level concepts , and combine the different levels of conceptual and iconic features when necessary. An advantage of indexing images based on multi-level contents rather than solely low-level features such as texture and colour is that it would readily provide the basic framework required for 'semantic interoperability' when one tries to search through not only one but a federation of image collections from different disciplines. Achieving this will require breakthroughs in image description as well as in image and object retrieval protocols. Physicians are beginning to be able to gain access, through the Internet, to the world's collections of multimedia medical …