# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
## 1 Introduction
### Related Works  
To the best of our knowledge, only one acral skin lesion segmentation method has been proposed so far--[PERSON] et al. combined [PERSON]'s thresholding with region growing [9].  
Regarding classical algorithms for non-acral skin lesion segmentation, in-depth surveys on lesion border detection in dermoscopy images can be found in [10; 11]. The proposed methods include threshold-based [12; 13; 14], region growing [15; 16; 17] and active-contour-based methods [18; 19]. Thresholding methods try to segment the skin lesions based on a threshold value, usually calculated by analyzing pre-defined image features such as the intensity histogram. Region growing based methods recursively merge pixels or regions together in a hierarchical manner. Methods based on energy functions (active contour methods) attempt to determine skin lesion boundaries by minimizing a cost function defined on image characteristics such as edges and smoothness.  
There have also been proposed approaches based on convolutional neural networks, which predict pixel-wise labeling of the images [20; 21]. Both approaches use fully convolutional networks (FCNs) to automatically segment the skin lesions. However, they address the issue of FCN producing coarse segmentation boundaries for challenging skin lesions differently: through a multistage segmentation approach in which multiple FCNs learn complementary visual characteristics of different skin lesions [20] or using a superpixel-based approach [21].  
Figure 1: Examples of dermoscopic images showing acral melanocytic lesions.  
Recently, a few methods using neutrosophic clustering have been proposed [22; 23; 24]. Their general framework consists of three steps. First, the dermoscopic images are mapped into a neutrosophic set domain (e.g., using the shearlet transform results for the images [22] or statistics based on gradient and intensity values [23; 24]) over three memberships: true, indeterminate, and false memberships. Then, a neutrosophic clustering algorithm (c-means in [22; 23] or k-means in [24]) is used for clustering the uncertain data. Finally, a skin lesion is identified using either cluster statistics [23; 24] of the adaptive region growing approach [22].  
Despite an apparent lack of dedicated algorithms for ALM segmentation, a few methods for pattern analysis and lesion classification have been proposed. [PERSON] et al. differentiated between acral volar melanomas and nevus using principal component analysis applied to color-, shape-, and texture-related properties of dermoscopic images, as well as developed pattern detectors for typical ALM dermatoscopic patterns using similar features [25]. [PERSON] et al. proposed a pattern classification method based on color symmetry and multiscale texture analysis in a perceptually uniform colorspace [26]. Recently, [PERSON] et al. developed an automatic algorithm for the detection of furrow and ridge patterns in volar lesions [8]. [PERSON] et al. extracted lesion area using [PERSON]'s thresholding, [PERSON] et al. used regions of interest of predefined size, whereas [PERSON] et al. did not perform any kind of lesion segmentation.