Digital Library of Zielona Góra contains 9 272 digital objects
Object
Title: Segmentation of the melanoma lesion and its border
Contributor:
Yao, Baozhen - ed. ; Wang, Shuaian - ed. ; Asian, Sobhan - ed.
Subtitle:
Group publication title:
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.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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 32, number 4 (2022) ; click here to follow the link
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2022)
Last modified:
Jul 24, 2025
In our library since:
Jul 24, 2025
Number of object content hits:
6
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/101552
Show description in RDF format:
Show description in OAI-PMH format:
-
-
-
-
-
-
International Journal of Applied Mathematics and Computer Science (AMCS), volume 32, number 1 (2022)
-
International Journal of Applied Mathematics and Computer Science (AMCS), volume 32, number 2 (2022)
-
International Journal of Applied Mathematics and Computer Science (AMCS), volume 32, number 3 (2022)
-
International Journal of Applied Mathematics and Computer Science (AMCS), volume 32, number 4 (2022)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
| Edition name | Date |
|---|---|
| Segmentation of the melanoma lesion and its border | Jul 24, 2025 |
Objects Similar
Kowal, Marek Filipczuk, Paweł Korbicz, Józef (1951- ) - red. Kowal, Marek - red.
Tadeusiewicz, Ryszard (1947- ) Ogiela, Marek R. Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Suchy, Dawid Simiński, Krzysztof Kitowski, Zygmunt - ed. Piskur, Paweł - ed. Hożyń , Stanisław - ed.
Baran, Mateusz Puig, Vicenç - ed. Sauter, Dominique - ed. Aubrun, Christophe - ed. Schulte, Horst - ed.
Kowal, Marek Skobel, Marcin Gramacki, Jarosław Korbicz, Józef (1951- ) Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Kowal, Marek Skobel, Marcin Nowicki, Norbert Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Tabor, Zbisław Korbicz, Józef (1951- ) - ed.
Ptak, Roman Żygadło, Bartosz Unold, Olgierd Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
