Graph-based skin lesion segmentation of multispectral dermoscopic images

Accurate skin lesion segmentation is critical for automated early skin cancer detection and diagnosis. We present a novel method to detect skin lesion borders in multispectral dermoscopy images. First, hairs are detected on infrared images and removed by inpainting visible spectrum images. Second, s...

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Bibliographic Details
Published in2014 IEEE International Conference on Image Processing (ICIP) pp. 897 - 901
Main Authors Lezoray, O., Revenu, M., Desvignes, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2014
Subjects
Clustering algorithms
dermoscopy
graphs
Hair
hair detection
hair removal
Image color analysis
Image segmentation
Lesions
Malignant tumors
segmentation
Skin
Skin cancer
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Summary:Accurate skin lesion segmentation is critical for automated early skin cancer detection and diagnosis. We present a novel method to detect skin lesion borders in multispectral dermoscopy images. First, hairs are detected on infrared images and removed by inpainting visible spectrum images. Second, skin lesion is pre-segmented using a clustering of a superpixel partition. Finally, the pre-segmentation is globally regularized at the superpixel level and locally regularized in a narrow band at the pixel level.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2014.7025180