Content-Based Medical Image Retrieval for Medical Radiology Images
Authors: 
Dario Barać, Teo Manojlović, Mateja Napravnik, Franko Hržić, Mihaela Mamula Saračević, Damir Miletić, Ivan ŠtajduharAuthors Info & Claims
Dario Barać, Teo Manojlović, Mateja Napravnik, Franko Hržić, Mihaela Mamula Saračević, Damir Miletić, Ivan ŠtajduharAuthors Info & ClaimsPages 45 - 59
Abstract
In this study, a CBIR system for medical radiology images of various anatomical regions and imaging modalities was designed and implemented. Three descriptor-extraction retrieval methods were evaluated: BoVW, HOG, and CNN. Optimal configuration for each method was selected empirically. We used a dataset consisting of 38, 000 2D images, originating from CHC Rijeka, Croatia. Retrieval with ImageNet-pretrained CNNs outperformed both BoVW and HOG retrieval. Re-ranking retrieved images using a different method improved results in almost all cases. Out of all methods compared, CNN retrieval (ShuffleNet combined with average pooling) with HOG re-ranking performed best. Our experiments can be recreated using the code available at https://github.com/dbarac/cbmir.
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Index Terms
- Content-Based Medical Image Retrieval for Medical Radiology Images
Index terms have been assigned to the content through auto-classification.
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Published In
Jul 2024
386 pages
ISBN:978-3-031-66534-9
DOI:10.1007/978-3-031-66535-6
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Springer-Verlag
Berlin, Heidelberg
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Published: 25 July 2024
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