**2. Related work**

The pixel-based interpolation technique was proposed by Satheesha et al. [13] to locate a quadratic curve, which detects bended hairs in the binary image mask for removal and replacement. Gabor filtering and PDE-based image reconstruction was proposed by Nasonova et al. [14] for hair removal problem. Moreover, for edge sharpening, they have utilized a warping algorithm to move pixels from the neighborhood of the blurred edge closer to the edge while the overall luminosity and texture patterns of the skin lesions are preserved. In [15], two fundamental steps are proposed to automatically detect and remove hairs from dermoscopy images: generation of a binary image mask by isolating hairs and ruler marking. From the RGB dermoscopy image, a red channel is utilized to perform noise removal followed by a generation of the binary image mask via an adaptive canny edge detector. Furthermore, a repaired task in view of polygons inpainting is implemented on the white regions of the generated mask.

The work of [16] relied on two classes of images: gray scale and RGB images. In gray scale images, in light of edge property, a circular mask is used to remove the nonskin pixels followed by a repair operation accomplished by a normalization process of pixel values. In RGB images, in light of histogram values, a frequency of occurrence of each bin is measured followed by the calculation of minimum distance among neighborhood pixels. An algorithm presented by Abbas et al. [17] for automatically detecting and repairing hair occlusion in dermoscopy images. In the detection stage, hairs are segmented utilizing MF-FDOG, thresholding, and morphological edge-based methods connected for improvement. In the repair stage, to inpaint the image without loss of texture patterns of skin lesions, the fast marching technique is implemented. MRF-based Multi-Label Optimization and Dual-Channel Quaternion Tubularness Filters are proposed by Mirzaalian et al. [18] for hair improvements in dermoscopy images. Their method was approved and contrasted with different methods regarding: hair segmentation accuracy, image inpainting quality, and image classification accuracy. To remove hairs by detecting hair pixels in a binary image mask followed by replacement through pixel interpolation is implemented via the Generalized Radon Transform (GRT). The Radon Transform was chosen to locate quadratic curves characterized by rational angle and scaling [19].

An effective detection of artifacts proposed by Okuboyejo et al. [20] consists of a two-stage artifact detection termed: fast image restoration (FIR) by means of canny algorithm and line segment detection (LSD) operation. To remove artifacts from dermoscopic images, the fast marching method (FMM) was applied at each stage while preserving morphological features during artifacts removal. A threshold set model for digital hair removal from dermoscopic images proposed by Okuboyejo et al. and Koehoorn et al. [21, 22]. They proposed a gap-detection algorithm to find hairs for every threshold and merge results in a single mask image. To locate hairs in the generated mask, morphological filters and medial descriptors are combined. The proposed work of [23] automatically detects and removes hairs and ruler markings from dermoscopy images. In detection stage, they used a curvilinear structure and modeling, and additional feature guided exemplar-based inpainting stage. Extensions to the fast marching method are introduced by Hearn [24] with the aim to enhance the segmentation of medical image data. The proposed algorithm used

#### *Computer Methods and Programs in Biomedical Signal and Image Processing*


#### **Table 2.**

*Comparison of existing digital hair removal methods.*

to limit the occurrence of bleeding across boundaries, including automatic starting point selection and statistical region combination.

Two removal hair approaches are conducted by Sultana et al. [25]. The first method is based on a simple morphological closing operation with a disk-shaped structural element while the top-hat transform combined with a bicubic interpolation utilized in the second approach. An effective hair removal algorithm for dermoscopic imagery is implemented by Bibiloni et al. [26]. They utilized soft color morphology operators that able to cope with color images. The hair removal filter used is basically made out of a morphological curvilinear object detector and a morphological-based inpainting algorithm. A simple approach to automatic hair and consequently noise removal were discussed by Acebuque-Salido and Ruiz [27]. The process starts with a median filter on each color space of RGB, a bottom-hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels. The detected hair regions are then filled up using harmonic inpainting. Their experiments were carried out on the PH2 dataset and compared to DullRazor method. Furthermore, they generated synthetic hair on skin images and measured the reconstruction quality using peak signal-to-noise ratio. In the work done by Al-abayechi et al. [28], a hair was removed, and reflective light was reduced using morphological operations and a median filter.

An algorithm for the automated hairs detection was implemented by Chakraborti et al. [29] to 50 dermoscopic melanoma images. They used an adaptive, canny edgedetection method, followed by morphological filtering and an arithmetic addition operation. Their proposed method produced 6.57% segmentation error (SE), 96.28% true detection rate (TDR) and 3.47% false positive rate (FPR). The proposed algorithm by Toossi et al. [30] divided into two phases: detection and removal. In detection, light and dark hairs and ruler marking are segmented via an adaptive canny edge detector and refinement by morphological operators. In removal, the hairs are repaired in view of multi-resolution coherence transport inpainting.

In addition to the above-mentioned hair removal methods, several aspects are captured in **Table 2**.
