New Approach for Automatic Medical Image Annotation Using the Bag-of-words Model
Riadh BOUSLIMI, Jalel AKAICHI
Journal of Information Security Research, Vol. 7, No. 2, 2016.
DOI: 
Link: 
Abstract:
In this paper, we present a new approach for semantic automatic annotation of medical images. Indeed, the
proposed approach uses the bag of words model to represent the visual content of the medical image combined with text
descriptors based on term frequencyâ€“inverse document frequency technique and reduced by latent semantic to extract the cooccurrence
between text and visual terms. In a first phase, we are interested in indexing texts and extracting all relevant terms
using a thesaurus containing medical subject headings and concepts. In a second phase, medical images are indexed while
recovering areas of interest which are invariant to change in scale such as light and tilt. To annotate a new medical image,
we use the bag of words model to recover the feature vector. Indeed, we use the vector space model to retrieve similar medical
images from the training database. The computation of the relevance value of an image according to a query image is based
on the cosine function. To evaluate the performance of our proposed approach, we present an experiment carried out on five
types of radiological imaging. The results showed that our approach works efficiently, especially with more images taken
from the radiology of the skull.