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Paper details
Number 4 - December 2022
Volume 32 - 2022
Segmentation of the melanoma lesion and its border
Grzegorz Surówka, Maciej Ogorzałek
Abstract
Segmentation of the border of the human pigmented lesions has a direct impact on the diagnosis of malignant melanoma.
In this work, we examine performance of (i) morphological segmentation of a pigmented lesion by region growing with the
adaptive threshold and density-based DBSCAN clustering algorithm, and (ii) morphological segmentation of the pigmented
lesion border by region growing of the lesion and the background skin. Research tasks (i) and (ii) are evaluated by a human
expert and tested on two data sets, A and B, of different origins, resolution, and image quality. The preprocessing step
consists of removing the black frame around the lesion and reducing noise and artifacts. The halo is removed by cutting out
the dark circular region and filling it with an average skin color. Noise is reduced by a family of Gaussian filters 3×3−7×7
to improve the contrast and smooth out possible distortions. Some other filters are also tested. Artifacts like dark thick hair
or ruler/ink markers are removed from the images by using the DullRazor closing images for all RGB colors for a hair
brightness threshold below a value of 25 or, alternatively, by the BTH transform. For the segmentation, JFIF luminance
representation is used. In the analysis (i), out of each dermoscopy image, a lesion segmentation mask is produced. For the
region growing we get a sensitivity of 0.92/0.85, a precision of 0.98/0.91, and a border error of 0.08/0.15 for data sets A/B,
respectively. For the density-based DBSCAN algorithm, we get a sensitivity of 0.91/0.89, a precision of 0.95/0.93, and a
border error of 0.09/0.12 for data sets A/B, respectively. In the analysis (ii), out of each dermoscopy image, a series of
lesion, background, and border segmentation images are derived. We get a sensitivity of about 0.89, a specificity of 0.94
and an accuracy of 0.91 for data set A, and a sensitivity of about 0.85, specificity of 0.91 and an accuracy of 0.89 for data
set B. Our analyses show that the improved methods of region growing and density-based clustering performed after proper
preprocessing may be good tools for the computer-aided melanoma diagnosis.
Keywords
computer-aided diagnosis, DBSCAN, melanoma, region growing, segmentation