  A patient is often willing to quickly get, from his physician, reliable
analysis and concise explanation according to provided linked medical images.
The fact of making choices individually by the patient's physician may lead to
malpractices and consequently generates unforeseeable damages. The Institute of
Medicine of the National Sciences Academy(IMNAS) in USA published a study
estimating that up to 98,000 hospital deathseach year can be attributed to
medical malpractice [1]. Moreover, physician, in charge of medical image
analysis, might be unavailable at the right time, which may complicate the
patient's state. The goal of this paper is to provide to physicians and
patients, a social network that permits to foster cooperation and to overcome
the problem of unavailability of doctors on site any time. Therefore, patients
can submit their medical images to be diagnosed and commented by several
experts instantly. Consequently, the need to process opinions and to extract
information automatically from the proposed social network became a necessity
due to the huge number of comments expressing specialist's reviews. For this
reason, we propose a kind of comments' summary keywords-based method which
extracts the major current terms and relevant words existing on physicians'
annotations. The extracted keywords will present a new and robust method for
image indexation. In fact, significant extracted terms will be used later to
index images in order to facilitate their discovery for any appropriate use. To
overcome this challenge, we propose our Terminology Extraction of Annotation
(TEA) mixed approach which focuses on algorithms mainly based on statistical
methods and on external semantic resources.
