Optimizing skin image segmentation with fourier and graph-based methods
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Date
2024
Authors
Kinshakov, E.
Parfenenko, Yu.
Journal Title
Journal ISSN
Volume Title
Publisher
Національний університет "Києво-Могилянська академія"
Abstract
This paper introduces advanced methods for skin disease image segmentation using the Dermnet dataset, one of the largest resources in dermatology. Traditional approaches like Watershed and thresholding often fail due to the complex textures, color variations, and noise present in skin images. To address these challenges, novel techniques were proposed. First, the Fourier transform reduces high-frequency noise, preparing images for segmentation. Then, min-cut/max-flow graph algorithms minimize energy functions, enabling precise separation of pathological and healthy areas. Additionally, a piecewise smooth approximation improves boundary detection, refining segmentation results. Experiments demonstrated a 15% accuracy improvement over traditional methods. Processing time was also significantly reduced, enhancing the reliability and efficiency of automated diagnostic systems.
Description
Keywords
segmentation, machine learning, image processing, skin diseases, Fourier transform, graph algorithms, computational optimization, piecewise approximation, conference materials
Citation
Kinshakov E. V. Optimizing skin image segmentation with fourier and graph-based methods / Kinshakov E. V., Parfenenko Yu. V. // Теоретичні та прикладні аспекти побудови програмних систем : працi 15 міжнародної науково-практичної конференції, Київ, 23-24 грудня 2024 р. / [за заг. ред.: М. М. Глибовця, Т. В. Панченка та iн. ; Факультет інформатики Національного університету "Києво-Могилянська академія" та ін.]. - Київ : НаУКМА, 2024. - C. 26-28.