Open AccessUsing 3D differential forms to characterize a pigmented lesion in vivo
Author(s) -
Zhou Yu,
Smith Melvyn,
Smith Lyndon,
Warr Rob
Publication year - 2010
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2009.00384.x
Subject(s) - artificial intelligence , melanoma , dermatoscopy , pattern recognition (psychology) , receiver operating characteristic , classifier (uml) , melanoma diagnosis , lesion , differential diagnosis , computer science , skin lesion , mathematics , dermatology , medicine , pathology , cancer research , machine learning
Background/purpose: After the formulation of ABCD rules, many new feature extraction methods are emerging to describe the asymmetry, border irregularity, color variation and diameter of malignant melanoma. In this paper, a new research direction orthogonal to ABCD rules that characterizes 3D local disruption of skin surfaces to realize automatic recognition of melanoma is described. Methods: This paper examines 3D differential forms of skin surfaces to characterize the local geometrical properties of melanoma. Firstly, 3D data of skin surfaces are obtained using a photometric stereo device. Then differential forms of lesion surfaces are determined to describe the geometrical texture patterns involved. Using only these geometrical features, a simple least‐squared error‐based linear classifier can be constructed to realize the classification of malignant melanomas and benign lesions. Results: As with the 3D data of 35 melanoma and 66 benign lesion samples collected from local pigmented lesion clinics, the optimal sensitivity and specificity of the constructed linear classifier are 71.4% and 86.4%, respectively. The total area enclosed by the corresponding receiver operating characteristics curve is 0.823. Conclusion: This study indicates that differential forms obtained from 3D data are very promising in characterizing melanoma. Combining these features with other skin features such as border irregularity and color variation might further improve the accuracy and reliability of the automatic diagnosis of melanoma.