# Dermoscopy Images Enhancement via Multi-Scale Morphological Operations
[PERSON]  
1 Computer Engineer Department, Universidad American, Asuncion 1206, Paraguay; [EMAIL_ADDRESS] (J.C.M.-R.); mgarcia@upoes (M.G.-T.); [EMAIL_ADDRESS] (D.P.-R.); [EMAIL_ADDRESS] (D.P.-R.); [EMAIL_ADDRESS] (D.N.L.C.) [PERSON]  
1 Computer Engineer Department, Universidad American, Asuncion 1206, Paraguay; [EMAIL_ADDRESS] (J.C.M.-R.); mgarcia@upoes (M.G.-T.); [EMAIL_ADDRESS] (D.P.-R.); [EMAIL_ADDRESS] (D.N.L.C.) [PERSON]  
1 Computer Engineer Department, Universidad American, Asuncion 1206, Paraguay; [EMAIL_ADDRESS] (J.C.M.-R.); mgarcia@upoes (M.G.-T.); [EMAIL_ADDRESS] (D.P.-R.); [EMAIL_ADDRESS] (D.N.L.C.) [PERSON]  
1 Computer Engineer Department, Universidad American, Asuncion 1206, Paraguay; [EMAIL_ADDRESS] (J.C.M.-R.); mgarcia@upoes (M.G.-T.); [EMAIL_ADDRESS] (D.P.-R.); [EMAIL_ADDRESS] (D.P.-R.); [EMAIL_ADDRESS] (D.N.L.C.) [PERSON]  
1 Computer Engineer Department, Universidad American, Asuncion 1206, Paraguay; [EMAIL_ADDRESS] (J.C.M.-R.); mgarcia@upoes (M.G.-T.); [EMAIL_ADDRESS] (D.P.-R.); [EMAIL_ADDRESS] (D.P.-R.); sebastian.grillo@ua. brightness. Studies have shown that HE introduces saturation and over-enhancement in the images [4; 5]. Several improved techniques have been proposed to maintain average image brightness, reducing saturation effects, thus avoiding unnatural image enhancement. Some of these techniques are: brightness preserving bi-histogram equalization (BBHE) [6], dual sub-image histogram equalization (DSIHE) [7], minimum mean brightness error bi-histogram equalization (MMBEBHE) [8], and quadri-histogram equalization with limited contrast (QHELC) [9]. With the emergence of mathematical morphology (MM) based approaches in recent decades, new techniques have been developed for contrast enhancement [10; 11; 12; 13]. Due to its ability to extract dark and light features from images using structuring elements of different shapes and sizes [14], the top-hat transformation has received a lot of attention. In [15], the top-hat transformation was used to correct the illumination of images with melanocytic lesions as a preprocessing for a subsequent feature extraction study. In [16], a method for segmentation of retinal blood vessels is presented. Vessel enhancement is performed using the contrast enhancement technique based on the top-hat transform. Various authors have proposed to use a multi-scale approach, called multi-scale top-hat transformation (MTH). An advantage of MTH is that it allows to process the image content from the most global to the most detailed level. Several works propose to improve different types of medical images by integrating MTH in a morphological based image enhancement approach [10; 17; 18; 19; 20; 21; 22; 23; 24]. Currently, in the field of computer vision, MTH-based algorithms are used as a preliminary step for other applications based on artificial intelligence. For example, in [25], a deep learning approach using convolutional neural networks was proposed to detect vessel regions in angiography images. In this work, the multi-scale top-hat transform for contrast enhancement (MSTH) algorithm [10] was used to preprocess the images by enhancing their contrast. In [26], a method for edge detection in images based on top-hat operators with multidirectional and multiscale structuring elements was proposed. A multimodal medical image fusion scheme based on multiscale top-hat transform combined with morphological contrast operators is presented in [27]. In [28], an automatic coronary artery segmentation approach was proposed. In this work, in the preprocessing stage, the input image was processed with the MSTH algorithm for better segmentation of coronary arteries. In [29], MSTH was used as the first step of an algorithm for detecting bright lesions in retinal images. In [30] proposed _Sine-Net_, an automated tool based on a fully convolutional deep learning architecture for the segmentation of blood vessels in retinal images.