# Hair Removal Combining Saliency, Shape and Color
## 1 Introduction  
In almost every specialist area of medicine, including dermatology, image analysis is transforming the diagnostic methods. In particular, computer-aided diagnosis systems for dermoscopic images have proven to be useful tools to improve significantly the common dermoscopic diagnostic practice, which is usually characterized by limited accuracy and is mainly based on visual inspection. Indeed, to differentiate melanoma from other pigmented skin lesions, these systems display morphological features not easily perceptible by the naked eye and support the assessment process of the human expert [1; 2]. Typically, a computer-aided system is structured in four main consecutive steps: preprocessing, segmentation, feature extraction, and classification, each playing a key role in enabling correct diagnosis [3]. During the preprocessing, the dermoscopic image is subjected to noise removal, image enhancement, color quantization, and artifacts removal processes [4; 5]. Noise removal and image enhancement techniques are employed to minimize the effects due to different illumination conditions and poor resolution of the acquisition process [6]. Color quantization [7; 8; 9; 10] is a technique of reducing the total number of unique colors in the image often used as a preprocessing step for many applications that are carried out more efficiently on a numerically smaller color set. For example, color quantization is employed effectively as a preliminary computation phase for skin lesion segmentation [11; 12; 13; 14; 15]. The removal methods of artifacts, such as bubbles, hair, shadows and reflections, aims to eliminate their negative effect and disturbance on the diagnostic operations of the area of interest (i.e., the skin lesion) [16]. Particularly, if the area of the skin lesion is partiallyoccluded by the hair, although such occlusions may not be critical for human investigation, this presence poses major challenges for automatic image analysis method such as segmentation and classification. Indeed, the hair removal (HR) methods and skin lesion segmentation (SLS) methods are highly correlated. Usually, SLS methods can determine the skin lesion region without the need to apply preliminary hair removal since these methods can include explicitly or implicitly hair removal operations. However, it is appropriate to consider that (a) in any case, a partial hair removal facilitates and increases the efficiency of the segmentation step; (b) the hair presence can lead to errors in lesion detection in some situations, especially when there is a massive presence of hair (see Figure 1); (c) for diagnostic and therapeutic purposes, HR is helpful at least to visualize the free-hair lesion to the expert. To address the hair issue, several hair removal methods have been proposed. HR methods usually consist of two steps: (a) the detection of occluding hair and generation of the hair binary mask; (b) the removal of the detected hair. Typically, hair detection is accomplished through object detection methods enucleating thin items, while hair removal is obtained through standard inpainting methods. As reported in [17], at least six main hair removal methods are widely used in the literature [18; 19; 20; 21; 22; 23]. The method proposed in [18] by [PERSON] et al., also known as Dullrazor, consists of four steps. The hair regions are initially detected through the morphological closing operator on each RGB color channel separately and with three structuring elements having different directions (step 1). To generate the binary mask, a thresholding process is applied to the absolute difference between the original color channel and the image generated by the closing (step 2). The mask pixels undergo a bilinear interpolation between two nearby not-mask pixels (step 3). Finally, to the resulting image, an adaptive median filter is applied (step 4). The method [19] by [PERSON] et al. also consists of four steps. The hair area is improved using a morphological closing top-hat operator (step 1).