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Article Dans Une Revue Journal of X-Ray Science and Technology Année : 2022

Images indexing and matched assessment of semantics and visuals similarities applied to a medical learning X-ray image base

Résumé

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 key words. 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.
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Dates et versions

hal-03788097 , version 1 (26-09-2022)

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Noureddine Bourkache, Mourad Laghrouche, Mourad Lahdir, Sahbi Sidhom. Images indexing and matched assessment of semantics and visuals similarities applied to a medical learning X-ray image base. Journal of X-Ray Science and Technology, 2022, Clinical applications of Diagnosis and Therapeutics, 30, 6 issues (30:4 online 26 August 2022), pp.1-21. ⟨10.3233/XST-221180⟩. ⟨hal-03788097⟩
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