Determining the asymmetry of skin lesion with fuzzy borders

Vincent To Yee Ng; Benny Y.M. Fung; Tim K. Lee

doi:10.1016/j.compbiomed.2003.11.004

Determining the asymmetry of skin lesion with fuzzy borders

Vincent To Yee Ng
, Benny Y.M. Fung
, Tim K. Lee

Research output: Journal article publication › Journal article › Academic research › peer-review

52 Citations (Scopus)

Abstract

It is highly desirable to identify malignant melanoma, a common cancer, at an early stage. One important clinical feature of this cancer is asymmetrical skin lesions. In this paper, we propose an adaptive fuzzy approach that uses symmetric distance (SD) to measure lesions with fuzzy borders. The use of a number of SD variations and the adoption of a backpropagation neural network enhances the discriminative power of the approach. Digitized images from the Lesion Clinic in Vancouver, Canada, demonstrate the accurate classification of asymmetric lesions at around 80%.

Original language	English
Pages (from-to)	103-120
Number of pages	18
Journal	Computers in Biology and Medicine
Volume	35
Issue number	2
DOIs	https://doi.org/10.1016/j.compbiomed.2003.11.004
Publication status	Published - 1 Feb 2005

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Asymmetry measurement
Medical imaging
Melanoma
Skin lesion

ASJC Scopus subject areas

Computer Science Applications
Health Informatics

More information

10.1016/j.compbiomed.2003.11.004

Cite this

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abstract = "It is highly desirable to identify malignant melanoma, a common cancer, at an early stage. One important clinical feature of this cancer is asymmetrical skin lesions. In this paper, we propose an adaptive fuzzy approach that uses symmetric distance (SD) to measure lesions with fuzzy borders. The use of a number of SD variations and the adoption of a backpropagation neural network enhances the discriminative power of the approach. Digitized images from the Lesion Clinic in Vancouver, Canada, demonstrate the accurate classification of asymmetric lesions at around 80\%.",

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AU - Lee, Tim K.

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N2 - It is highly desirable to identify malignant melanoma, a common cancer, at an early stage. One important clinical feature of this cancer is asymmetrical skin lesions. In this paper, we propose an adaptive fuzzy approach that uses symmetric distance (SD) to measure lesions with fuzzy borders. The use of a number of SD variations and the adoption of a backpropagation neural network enhances the discriminative power of the approach. Digitized images from the Lesion Clinic in Vancouver, Canada, demonstrate the accurate classification of asymmetric lesions at around 80%.

AB - It is highly desirable to identify malignant melanoma, a common cancer, at an early stage. One important clinical feature of this cancer is asymmetrical skin lesions. In this paper, we propose an adaptive fuzzy approach that uses symmetric distance (SD) to measure lesions with fuzzy borders. The use of a number of SD variations and the adoption of a backpropagation neural network enhances the discriminative power of the approach. Digitized images from the Lesion Clinic in Vancouver, Canada, demonstrate the accurate classification of asymmetric lesions at around 80%.

KW - Asymmetry measurement

KW - Medical imaging

KW - Melanoma

KW - Skin lesion

UR - https://www.scopus.com/pages/publications/9644282965

U2 - 10.1016/j.compbiomed.2003.11.004

DO - 10.1016/j.compbiomed.2003.11.004

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IS - 2

ER -

Determining the asymmetry of skin lesion with fuzzy borders

Abstract

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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