# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 1 Introduction  
A major challenge in the scientific field in recent years is skin cancer. Among the types of skin cancers, melanoma is the most aggressive. It has a greater capacity to spread to other organs; thus, it is responsible for the highest death rate even with its low incidence [1; 2]. The increased incidence of skin cancer comes from excessive exposure to the sun, especially in individuals with fair skin, which, in addition to causing premature aging, can cause pre-malignant and malignant lesions. Among the various types of skin cancers, melanoma (see Figure 1) is the least common and at the same time the most worrying for its ability to spread to other parts of the body, even when the lesion is still small [3]. In the initial stage, which is where there is greater possibility of cure, a prior diagnosis is necessary, but this is hampered by human visual factors, such as visual fatigue, often resulting in failure in the demarcation of the lesion. The automatic segmentation of the area of interest of the image is necessary in order to obtain a precision of the contour reducing the occurrence of errors, besides being essential for the other stages of extraction and classification of the skin lesion. It is very important how an image is processed because it is from it that a given case can be diagnosed in time for a possible cure [3; 6; 7]. It is possible to define an approach for this type of skin cancer through digital image processing techniques. This approach will provide reliability and agility in the diagnosis for those skilled in the art. The areas of digital image processing applications increasingly require methods capable of highlighting the information contained in the images for human interpretation and analysis [8]. A better prognosis of the disease is associated with its early diagnosis, that is, early detection (in situ melanoma). The lesion at a more advanced stage, that is, after its infiltration into the lower layers of the skin, results in a great possibility of metastasis with almost no cure. This causes skin cancer to have the highest mortality rate compared to other non-melanoma tumors [6; 7; 8]. The vast majority of melanoma diagnosis methods are based on the so-called ABCD rule, in which physical factors observed in the lesions are analyzed, such as asymmetry, border, staining, and diameter. This methodology was first proposed by [PERSON] in 1994 [9] due to the difficulty of early diagnosis of melanoma and its common confusion with benign nevi and pints (in the early stages). A unique apparatus termed dermatoscopic is employed to estimate the chance of types of skin cancer [6; 7; 8]. Dermatoscopy is a noninvasive technique for the recognition of superficial structures of the skin and serves for magnification (up to 400\(\times\)) and evaluation of cutaneous tissue images. The parameters of the ABCD rule can be observed through the dermatoscope, requiring a certain degree of experience and knowledge from its operator, which is a differential, but subjective factor in the task of classifying skin tumors based on visual signals (images) [9; 10]. Since each presentation of skin cancer has its own characteristics as to form and color, each exam has as a first step to differentiate melanocyte lesions from non-melanocytes, and later to classify them as benign, malignant, or suspect. Early research on automatic systems to estimate the chance of skin cancer used dermoscopy images [11]. These images are required for the partition algorithm program for dermoscopy images of skin cancer. The most segmentation algorithms use dermoscopy images, which have a higher difference between the lesion and skin space. The segmentation algorithmic program operates to identify the state of the boundary lesion [12; 13]. The skin cancer segmentation algorithm program must be correct because  
Figure 1: Difficulties of dermoscopic images: (**a**) smooth transition between lesion and skin; (**b**) existing hair; (**c**) multiple colored lesions; and (**d**) specular reflections [4; 5]. (Scale bar: 10 mm).