Content-Based Image Retrieval (CBIR) has been one on the most vivid research areas in the
field of computer vision over the decades. The availability of large and steadily growing
amounts of visual and multimedia data, and the development of the Internet underline the need
to create thematic access methods that are more than simple text-based queries or requests
based on matching exact database fields. Content-based Image Retrieval (CBIR) aids
radiologist to identify similar medical images in recalling previous cases during diagnosis.
Although several algorithms have been introduced to extract the content of the medical images,
the process is still a challenge due to the nature of the feature itself where most of them are
extracted in low level form. In addition to the dimensionality reduction problem caused by the
low-level features, current features are also insufficient to convey the semantic meaning of the
images. This article gives an overview of available literature in the field of content-based access
to medical image data and on the technologies used in the field. The shortcomings of the current
CBIR systems are discussed and future directions toward context-based medical image
retrieval are expressed.