  We present in this paper a new approach for the automatic annotation of
medical images, using the approach of "bag-of-words" to represent the visual
content of the medical image combined with text descriptors based approach
tf.idf and reduced by latent semantic to extract the co-occurrence between
terms and visual terms. A medical report is composed of a text describing a
medical image. First, we are interested to index the text and extract all
relevant terms using a thesaurus containing MeSH medical concepts. In a second
phase, the medical image is indexed while recovering areas of interest which
are invariant to change in scale, light and tilt. To annotate a new medical
image, we use the approach of "bagof-words" to recover the feature vector.
Indeed, we use the vector space model to retrieve similar medical image from
the database training. The calculation of the relevance value of an image to
the query image is based on the cosine function. We conclude with an experiment
carried out on five types of radiological imaging to evaluate the performance
of our system of medical annotation. The results showed that our approach works
better with more images from the radiology of the skull.
