Background/Purpose: It is known that the standard features for lesion classification are ABCD features, that is, asymmetry, border irregularity, colour variegation and diameter of lesion. However, the observation that skin patterning tends to be disrupted by malignant but not by benign skin lesions suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using both skin line direction and intensity for lesion classification was encouraging. But these features have not been combined with the ABCD features. This paper explores the possibility of combing features from skin pattern and ABCD analysis to enhance classification performance.
Methods: The skin line direction and intensity were extracted from a local tensor matrix of skin pattern. Meanwhile, ABCD analysis was conducted to generate six features. They were asymmetry, border irregularity, colour (red, green and blue) variegations and diameter of lesion. The eight features of each case were combined using a principal component analysis (PCA) to produce two dominant features for lesion classification.
Results: A larger set of images containing malignant melanoma (MM) and benign naevi were processed as above and the scatter plot in a two-dimensional dominant feature space showed excellent separation of benign and malignant lesions. An ROC (receiver operating characteristic) plot enclosed an area of 0.94.
Conclusions: The classification results showed that the individual features have a limited discrimination capability and the combined features were promising to distinguish MM from benign lesion.
Computer Engineering | Diagnosis
She, Z., Liu, Y., & Damatoa, A. (2007) ‘Combination of features from skin pattern and ABCD analysis for lesion classification’. Skin Research & Technology, 13(1), 25–33
Digital Commons Citation
She, Zhishun; Liu, Y; and Damatoa, A, "Combination of features from skin pattern and ABCD analysis for lesion classification" (2007). Computing. Paper 31.