Extraction of skin lesion texture features based on independent component analysis.

BACKGROUND/PURPOSE: During the recent years, many diagnostic methods have been proposed aiming at early detection of malignant melanoma. The texture of skin lesions is an important feature to differentiate melanoma from other types of lesions, and different techniques have been designed to quantify this feature. In this paper, we discuss a new approach based on independent component analysis (ICA) for extraction of texture features of skin lesions in clinical images.

METHODS: After preprocessing and segmentation of the images, features that describe the texture of lesions and show high discriminative characteristics are extracted using ICA, and then these features, along with the color features of the lesions, are used to construct a classification module based on support vector machines for the recognition of malignant melanoma vs. benign nevus.

RESULTS: Experimental results showed that combining melanoma and nevus color features with proposed ICA-based texture features led to a classification accuracy of 88.7%.

CONCLUSION: ICA can be used as an effective tool for quantifying the texture of lesions.