Concentric decile segmentation of white and hypopigmented areas in dermoscopy images of skin lesions allows discrimination of malignant melanoma

Abstract

Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. White areas, prominent in early malignant melanoma and melanoma in situ, contribute to early detection of these lesions. An adaptive detection method has been investigated to identify white and hypopigmented areas based on lesion histogram statistics. Using the Euclidean distance transform, the lesion is segmented in concentric deciles. Overlays of the white areas on the lesion deciles are determined. Calculated features of automatically detected white areas include lesion decile ratios, normalized number of white areas, absolute and relative size of largest white area, relative size of all white areas, and white area eccentricity, dispersion, and irregularity. Using a back-propagation neural network, the white area statistics yield over 95% diagnostic accuracy of melanomas from benign nevi. White and hypopigmented areas in melanomas tend to be central or paracentral. The four most powerful features on multivariate analysis are lesion decile ratios. Automatic detection of white and hypopigmented areas in melanoma can be accomplished using lesion statistics. A neural network can achieve good discrimination of melanomas from benign nevi using these areas. Lesion decile ratios are useful white area features.

Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

“…First, our results confirm results from a previous study that found that white or hypopigmented areas in the outer three deciles of the lesion were needed for the best discrimination of melanoma vs. benign [38]. Second, we have confirmed the clinical impression that colors within the lesion periphery, specifically the outer lesion deciles, are important in describing benign lesions, since a circumferential "hazy border", with coloration intermediate between that of skin and lesion, implies a benign lesion [32] and a "pink rim" is a sign of a melanoma [39].…”

Automatic dirt trail analysis in dermoscopy images

“…The features in the individual objects therefore have meaningful information for detecting dirt trails, allowing discrimination of BCC from benign skin lesions. Future studies could incorporate assessments of cluster elongation and global distribution analysis using lesion deciles [10]. It is likely that parameters describing paracentral location of dirt trail objects will be useful in achieving better discrimination results.…”

Fuzzy logic color detection: Blue areas in melanoma dermoscopy images

“…The outer 5th of all pixels closest to the lesion boundary are the 1st quintile, and the inner 5th of all pixels are closest to the center, modified from [27]. …”