Affiliations: [a] Laboratoire d’Analyse et Modélisation des Phénomènes Aléatoires (LAMPA), UMMTO, Tizi-Ouzou, Algeria
| [b] Laboratoire Lorrain en Informatique et ses Applications (LORIA Lab), University of Lorraine (Nancy), France
Correspondence:
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Corresponding author: Noureddine Bourkache, Laboratoire d’Analyse et Modélisation des Phénomènes Aléatoires (LAMPA), UMMTO, BP 17 RP, 15000, Tizi-Ouzou, Algeria. E-mail: noureddine.bourkache@ummto.dz.
Abstract: BACKGROUND:Medical diagnostic support systems are important tools in the field of radiology. However, the precision obtained, during the exploitation of high homogeneity image datasets, needs to be improved. OBJECTIVE:To develop a new learning system dedicated to public health practitioners. This study presents an upgraded version dedicated to radiology experts for better clinical decision-making when diagnosing and treating the patient (CAD approach). METHODS:Our system is a hybrid approach based on a matching of semantic and visual attributes of images. It is a combination of two complementary subsystems to form the intermodal system. The first one named α based on semantic attributes. Indexing and image retrieval based on specific keywords. The second system named β based on low-level attributes. Vectors characterizing the digital content of the image (color, texture and shape) represent images. Our image database consists of 930 X-ray images including 320 mammograms acquired from the mini-MIAS database of mammograms and 610 X-rays acquired from the Public Hospital Establishment (EPH-Rouiba Algeria). The combination of two subsystems gives rise to the intermodal system: α-subsystem offers an overall result (based on visual descriptors), then β-subsystem (low level descriptors) refines the result and increases relevance. RESULTS:Our system can perform a specific image search (in a database of images with very high homogeneity) with an accuracy of around 90% for a recall of 25%. The average (overall) accuracy of the system exceeds 70%. CONCLUSION:The results obtained are very encouraging, and demonstrate efficiency of our approach, particularly for the intermodal system.
Keywords: Image indexing, medical image, data base indexing, information retrieval, content base retrieval, query by image, cancer diagnosis
