Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions f...

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Bibliographic Details
Published in:IET image processing 2016-06, Vol.10 (6), p.448-455
Main Authors: Kasmi, Reda, Mokrani, Karim
Format: Article
Language:English
Subjects:
ABCD features
Algorithms
Asymmetry
automatic ABCD rule
automatic ABCD scoring
Automation
benign skin lesions
biomedical optical imaging
cancer
Classification
Colour
colour asymmetry
Construction
dermoscopic images
dermoscopic structures
dermoscopy
feature extraction
Gabor filters
geodesic active contours
image classification
lesion boundaries
Lesions
malignant melanoma classification
medical image processing
melanoma detection
Scoring
scoring method
skin
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Summary:The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.
ISSN:1751-9659
1751-9667
1751-9667
DOI:10.1049/iet-ipr.2015.0385