# Hair Removal Combining Saliency, Shape and Color
## 3 Experimental Results
### Datasets  
We test our method by considering images available on two publicly available databases of dermoscopic images: PH\({}^{2}\)[28] and ISIC2016 [29]. PH\({}^{2}\) is a dermoscopic image database acquired at the Dermatology Service of Hospital Pedro Hispano to support comparative studies on segmentation/classification methods. This database includes clinical/histological diagnosis, medical annotation, and the evaluation of many dermoscopic criteria. It provides 200 dermoscopic RGB images and the corresponding ground truth, including 80 atypical nevi, 80 common nevi, and 40 melanomas. All the images are 8-bit RGB and have resolution 760 \(\times\) 560 pixels. ISIC2016 is one of the largest databases of dermoscopic images of skin lesions with quality control held by the International Symposium on Biomedical Imaging (ISBI) to improve melanoma diagnosis. It includes images representative of both benign and malignant skin lesions. For each image, the ground truth is also available. ISIC2016 consists of 397 (75 melanomas) and 900 (173 melanomas) annotated images as testing and training data, respectively. The images are 8-bit RGB and have a size ranging from 542 \(\times\) 718 to 2848 \(\times\) 4288. PH\({}^{2}\) and ISIC2016 databases contain numerous images with complex backgrounds and complicated skin conditions with the presence of hair and other artifacts/aberrations. Since in PH\({}^{2}\) and ISIC2016 hairless and hairy images are not distinguished, it is not possible to evaluate the performance of an HR method on each total dataset, and it is necessary to separate them preliminarily. Hence, from PH\({}^{2}\) and ISIC2016 we extract two datasets, denoted as _H-data_ and _NH-data_, each constituted by 170 images, which respectively contain images with evident hair and images without hair. These images are selected randomly and subdivided into the two datasets according to a human visualinspection. These datasets, totally comprising 340 images, are available at the Github link indicated in the section Data Availability Statement. To accurately and comprehensively validate the goodness of detecting hair and, at the same time, to make a deeper comparison with the published results of the existing methods [18; 19; 20; 21; 22; 23], which in the following we indicate with the name of the first author (i.e., [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]), we also consider a specific dataset available in [37]. This dataset, here call _NH13-data_ and shown in Figure 4, is constituted by 13 images without hair. We consider also the hairy images obtained starting from _NH13-data_ by the GAN method [38] and HairSim method [39], that starting from a hair-free dermoscopic image, provide a hair-occluded image and the corresponding binary hair-mask. These datasets are available in [37], are denoted as _H13 GAN-data_ and _H13 Sim-data_, and are shown in Figures 5 and 6, respectively. Moreover, to validate the performance of the method on a larger dataset, we simulate the presence of hair on _NH-data_ using the HairSim method by generating the _HSim-data_ set. Note that for _HSim-data_ and _H-data_, only the methods Lee, Xie, and HR-SSC are considered. The choice of these methods is based on the fact that first, they are the  
Figure 4: Image dataset _NH13-data_ proposed in [37]. Figure 5: Image dataset _H13 GAN-data_ generated by applying the GAN method [38] to _NH13-data_ and published in [37]. Figure 6: Image dataset _H13 Sim-data_ generated by applying the HairSim method [39] to _NH13-data_ and published in [37]. only methods, including the deep learning class of methods, with an available source code, second, they are used widely in the literature, and third, they have higher quality measures in [37]. We consider all images of \(H\)_-data_ and _HSim-data_, but, given the high number of images, we limit to show the results for a13 images sample for each dataset, here named _sH-data_ and _sHSim-data_, respectively.