Abstract
Medical images are an essential input for the timely diagnosis of pathologies. Despite its wide use in the area, searching for images that can reveal valuable information to support decision-making is difficult and expensive. However, the possibilities that open when making large repositories of images available for search by content are unsuspected. We designed a content-based image retrieval system for medical imaging, which reduces the gap between access to information and the availability of useful repositories to meet these needs. The system operates on the principle of query-by-example, in which users provide medical images, and the system displays a set of related images. Unlike metadata match-driven searches, our system drives content-based search. This allows the system to conduct searches on repositories of medical images that do not necessarily have complete and curated metadata. We explore our system’s feasibility in computational tomography (CT) slices for SARS-CoV-2 infection (COVID-19), showing that our proposal obtains promising results, advantageously comparing it with other search methods.
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Acknowledgments
This work was funded by ANID FONDEF grant 19I10023, ANID FONDECYT grant 11170475, ANID Basal Project FB0008, and ANID PIA/APOYO AFB180002. Dr. Mendoza acknowledges support from ANID Fondecyt grant 1200211.
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Molina, G. et al. (2022). A New Content-Based Image Retrieval System for SARS-CoV-2 Computer-Aided Diagnosis. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_33
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DOI: https://doi.org/10.1007/978-981-16-3880-0_33
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