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Updated: Dec 11, 2025
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
Published on: August 8, 2025
Fast fully automatic skin lesions segmentation probabilistic with Parzen window
João V Souza das Chagas1, Roberto F Ivo2, Matheus T Guimarães3
1Laboratório de Processamento de Imagens, Sinais e Computação Aplicada (LAPISCO), Instituto Federal do Ceará, Fortaleza-CE, Brazil; Departamento da Indústria, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Brazil.
Abstract:
Cutaneous melanoma accounts for over 90% of all melanoma, causing up to 55,500 annual deaths. However, it is a potentially curable type of cancer. Since melanoma is potentially curable, the disease's mortality rate is directly linked to late detection. This work proposes an approach that presents the balance between time and efficiency. This paper proposes the method of fast and automatic segmentation of skin lesions using probabilistic characteristics with the Parzen window (SPPW). The results obtained by the method were based on PH2 and ISIC datasets. The SPPW approach reached the following averages between the two datasets Specificity of 98.55%, Accuracy of 95.48%, Dice of 91.12%, Sensitivity of 88.45%, Mattheus of 87.86%, and Jaccard Index of 84.90%. The highlights of the proposed method are its short average segmentation time per image, and its metrics values, which are often higher than the ones obtained by other methods. Therefore, the SPPW method of segmentation is a quick, viable, and easily accessible option to aid in the diagnosis of diseased skin.
