Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation

Abstract

Malignant melanoma is among the fastest increasing malignancies in many countries. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. In non-Caucasian populations, melanomas are frequently located in acral volar areas and their dermoscopic appearance differs from the non-acral ones. Although lesion segmentation is a natural preliminary step towards its further analysis, so far virtually no acral skin lesion segmentation method has been proposed. Our goal was to develop an effective segmentation algorithm dedicated for acral lesions. We obtain a superpixel oversegmentation of a lesion image by performing clustering in a joint color-spatial 5d space defined by coordinates of CIELAB color space and spatial coordinates of the image. We then construct a region adjacency graph based on this superpixel representation. We obtain the ultimate segmentation result by performing a hierarchical region merging. The proposed segmentation method has been tested on 134 color dermoscopic images of different types of acral melanocytic lesions (including melanoma) from various sources. It achieved an average Dice index value of 0.85, accuracy 0.91, precision 0.89, sensitivity 0.87, and specificity 0.88. Experimental results suggest the effectiveness of the proposed method, which would help improve the accuracy of other diagnostic algorithms for acral melanoma detection. The results also suggest that the computational approach towards lesion segmentation yields more stable output than manual segmentation by dermatologists.