Abstract
Dermoscopic image analysis is an important field that helps in skin cancer diagnosis. Removing image artefacts, such as hair or measuring tools, remains challenging due to the lack of datasets with artefact ground truth and clear samples. In this work, we present a comprehensive study of different segmentation and inpainting algorithms, as well as present our own solution for segmentation. To the best of our knowledge, this is one of the first papers that introduce both qualitative and quantitative analysis of framework performance at different stages, by evaluating the proposed methods on an artificially created dataset, based on ISIC database modification. We are able to show that our methods achieve a Dice score of 0.59 on hair segmentation and 51.36 Peak signal-to-noise ratio (PSNR) in image inpainting.
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Research project partly supported by the program “Excellence initiative - research university” for the AGH University of Science and Technology.
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Kozaczko, K., Szpot, R., Brodzicki, A., Wójcicka, A. (2023). Automatic Artifact Removal from Dermoscopic Images. In: Pawelczyk, M., Bismor, D., Ogonowski, S., Kacprzyk, J. (eds) Advanced, Contemporary Control. PCC 2023. Lecture Notes in Networks and Systems, vol 709. Springer, Cham. https://doi.org/10.1007/978-3-031-35173-0_19
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