Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.
Comput Med Imaging Graph
; 52: 89-103, 2016 09.
Article
em En
| MEDLINE
| ID: mdl-27215953
ABSTRACT
Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Cutâneas
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Algoritmos
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Reconhecimento Automatizado de Padrão
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Interpretação de Imagem Assistida por Computador
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Melanoma
Tipo de estudo:
Diagnostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Revista:
Comput Med Imaging Graph
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Bélgica