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We present in this paper a new approach for the automatic annotation ofmedical images, using the approach of "bag-of-words" to represent the visualcontent of the medical image combined with text descriptors based approachtf.idf and reduced by latent semantic to extract the co-occurrence betweenterms and visual terms. A medical report is composed of a text describing amedical image. First, we are interested to index the text and extract allrelevant terms using a thesaurus containing MeSH medical concepts. In a secondphase, the medical image is indexed while recovering areas of interest whichare invariant to change in scale, light and tilt. To annotate a new medicalimage, we use the approach of "bagof-words" to recover the feature vector.Indeed, we use the vector space model to retrieve similar medical image fromthe database training. The calculation of the relevance value of an image tothe query image is based on the cosine function. We conclude with an experimentcarried out on five types of radiological imaging to evaluate the performanceof our system of medical annotation. The results showed that our approach worksbetter with more images from the radiology of the skull.

Using a bag of Words for Automatic Medical Image Annotation with a Latent Semantic