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Comput Methods Programs Biomed. 2016 Jul;131:127-41. doi: 10.1016/j.cmpb.2016.03.032. Epub 2016 Apr 8.

Computational methods for the image segmentation of pigmented skin lesions: A review.

Author information

1
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
2
Departamento de Computação, Faculdade de Ciências, Universidade Estadual Paulista, av. Eng. Luiz Edmundo Carrijo Coube, 14-01, 17033-360 Bauru, SP, Brazil.
3
Departamento de Ciências de Computação e Estatística, Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista, rua Cristóvão Colombo, 2265, 15054-000 São José do Rio Preto, SP, Brazil.
4
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, rua Dr. Roberto Frias, 4200-465 Porto, Portugal. Electronic address: tavares@fe.up.pt.

Abstract

BACKGROUND AND OBJECTIVES:

Because skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis. This paper aims to present a review of the current methods, and outline a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation.

METHODS:

Techniques that have been proposed to achieve these tasks were identified and reviewed. As to the image segmentation task, the techniques were classified according to their principle.

RESULTS:

The techniques employed in each step are explained, and their strengths and weaknesses are identified. In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results.

CONCLUSIONS:

The image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency.

KEYWORDS:

Image acquisition; Image pre-processing; Image segmentation; Pigmented skin lesion images

PMID:
27265054
DOI:
10.1016/j.cmpb.2016.03.032
[Indexed for MEDLINE]

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