Skin Lesion Segmentation Using Multiple Density Clustering Algorithm MDCUT And Region Growing

Skin lesion segmentation is a key step in a diagnosis system based on dermoscopic images. This paper proposes a method to detect the skin lesion accurately. The images are first cleansed to remove noise. Then, pertinent features are extracted from RGB, HSV and XYZ color spaces. Cluster analysis is u...

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Bibliographic Details
Published in2018 IEEE ACIS 17th International Conference on Computer and Information Science (ICIS) pp. 74 - 79
Main Authors Louhichi, Soumaya, Gzara, Mariem, Abdallah, Hanene Ben
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Clustering algorithms
density based clustering
dermoscopic images
Feature extraction
Image color analysis
Image segmentation
Lesions
MDCUT
region growing
segmentation
Shape
Skin
skin lesion
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DOI10.1109/ICIS.2018.8466531

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Summary:Skin lesion segmentation is a key step in a diagnosis system based on dermoscopic images. This paper proposes a method to detect the skin lesion accurately. The images are first cleansed to remove noise. Then, pertinent features are extracted from RGB, HSV and XYZ color spaces. Cluster analysis is used for segmentation. We take advantage of the multiple density clustering algorithm MDCUT [1] to solve the problem of image segmentation using region growing. We demonstrate how MDCUT algorithm is used to automatically determine the needed parameters for region growing image segmentation. Experiments on medical skin lesion image and comparison with the ground truth segmentation results demonstrate the validity of our method.
DOI:10.1109/ICIS.2018.8466531