Term dependency extraction using rule-based Bayesian Network for medical image retrieval.

Text-Based Medical Image Retrieval (TBMIR) has been known to be successful in retrieving medical images with textual descriptions. Usually, these descriptions are very brief and cannot express the whole visual content of the image in words, hence negatively affect the retrieval performance. One of the solutions offered in the literature is to form a Bayesian Network thesaurus taking advantage of some medical terms extracted from the image datasets.

Document/query expansion based on selecting significant concepts for context based retrieval of medical images.

In the medical image retrieval literature, there are two main approaches: content-based retrieval using the visual information contained in the image itself and context-based retrieval using the metadata and the labels associated with the images. We present a work that fits in the context-based category, where queries are composed of medical keywords and the documents are metadata that succinctly describe the medical images. A main difference between the context-based image retrieval approach and the textual document retrieval is that in image retrieval the narrative description is very brief and typically cannot describe the entire image content, thereby negatively affecting the retrieval quality.