Combination of features from skin pattern and ABCD analysis for lesion classification

Abstract

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. AbstractBackground/purpose: It has been 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 and benign naevi were processed as above and the scatter plot in a two-dimensional dominant feature sp...

Principal component model of multispectral data for near real-time skin chromophore mapping

“…[21][22][23][24][25] To our best knowledge, no attempt was done for extraction of blood oxygenation using PCA from RGB or multispectral images or for comparison of PCA to reconstruction results. The reconstruction results of four healthy volunteers undergoing occlusion of the upper arm and imaged at the forearm ͑Fig.…”

“…The usage ranges from noise reduction and image enhancement 19 in multispectral data for biological cell analysis 20 to pattern analysis for skin lesion classification. 21 Applied to RGB images, PCA was used on relative color features for unsupervised lesion classification. [22][23][24] When imaging the skin in the visible light range, the dominant absorbing materials are blood and melanin and should therefore explain most of the variance in multispectral data.…”

Computer Aided Melanoma Skin Cancer Detection Using Image Processing

“…In Some techniques, the symmetry feature is calculated based on geometrical measurements on the whole lesion, e.g. symmetric distance and circularity 9 Other studies, propose the circularity index, as a measure of irregularity of borders in dermoscopy images 14,1 The paper [11] gives the overview of the most important implementations in the literature and compares the performance of several classifiers on the specific skin lesion diagnostic problem.…”

Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors

“…One of the biggest advantages of using PCA lies in its speed of computation, which allows real-time analysis of large image sets. Applied to optical imaging, PCA has been used for noise reduction and image enhancement [16], in multi-spectral imaging data for biological cell analysis [17], and to pattern analysis for skin lesion classification [18]. Applied to RGB images, PCA was used on relative color features for unsupervised lesion classification [14,19,20].…”