Abstract:
Content based medical image retrieval is one of the most intriguing research area of
modern image retrieval and biomedical image processing. In the biomedical domain the
volume of information is rapidly increasing. For managing this huge amount of volume
of information content based medical image retrieval techniques are indispensable. Medical
researchers, physiotherapists, dentists and clinicians vividly use online databases to
access the relevant biomedical bibliographic citations based on keyword search on different
elds such as the main text, author, and date. However, a picture is worth a
thousand words, and even more so in medical domain as images of diverse modalities
constitute an important source of anatomical and functional information for the diagnosis
of diseases, research, education. Researchers have proposed di erent methods to
extract the meaningful information from images as well as retrieve images from query
image. Though text based image retrieval is a well traversed approach, the e cient
way of content based medical image retrieval is always preferred for bridging semantic
gaps. We propose a novel approach for medical image retrieval where we represent
the image with concepts which refers to distinguishable visual texture and shape definitions
representing the visualness of an image. For concept learning we have used
self-clustered weighted entropy based concept feature space trained by self-organizing
map. We also proposed a new ranking procedure which is based on normalization of
histogram of concepts. The hypothesis that such approaches would improve biomedical
image retrieval is validated through experiments on ImageCLEFmed2009 IRMA data
set, which is collected with o cial permission of IRMA.
