# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 1 Introduction
the segmentation is employed as an associate for distinguishing and extraction in the malignant classification algorithms. 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. Segmenting from the lesions' region for the digital images may have a disadvantage because of color deviation. The existence of color deviation will have an unconstructive shock on the segmentation and classification. Segmentation algorithms are needed to implement color alteration [7; 14]. For the skin cancer images, there are different kinds of segmentation rules that have been developed [15]. One more method for the detection of the skin cancer is to work on the skin surface information because traditional skin and lesion regions have completely different skin surfaces. [PERSON] et al. [14] used texture features based on the G-means clustering method to segment skin cancer region. The work of [PERSON] et al. [16] focused on the characteristic skin tumor margins; the margin being the initial and most significant characteristic to spot. Melanoma margins and exterior contours are moderately simply identified by the human observer; however, automatic margin findings may be a complex problem. For example, lesion image segmentation is an extremely difficult problem because of the numerous details. Figure 1 shows different dermoscopy images with various deviations and artifacts. On the other hand, segmentation is challenging because of the great variety of lesion forms, dimensions, the presence of dark hair and colors along with various skin types and textures [4; 5]. [PERSON], et al. [17] developed a computerized method for detecting malignant melanoma close to the first stage. In the preliminary stage, essential segmentation approaches are applied simultaneously to work out the binary mask of the melanoma. Then, to work out the melanoma of the skin, the local and global limitations are evaluated. [PERSON] et al. [18] advance a technique of image segmentation that explains a technique for the selection of margins. These techniques are theoretically straightforward and used to handle the loud noise and are appropriate for round images [10; 19]. The artificial skin lesion merging (ASLM) method is implemented by [PERSON] and [PERSON] [13], which consists of four steps for the process of image segmentation by considering the different types of skin lesion and its dimensions and color. The analysis of melanoma detection for this method has the best sensitivity and higher accuracy when compared to other methods used for PH2 (Pedro Hispano Hospital Datasets) [15]. One of the method's detections of the melanoma skin cancer is segmentation by utilizing a mixture of STOLZ and OTSU technologies. This method is used to obtain optimum results of this processing including ABCDE parameters and classifying the types of skin cancer (lesion cancer). [PERSON] et al. [11] introduced a comprehensive comparative analysis of different segmentation methods for detecting skin cancer lesions. [PERSON] et al. [20] used color constancy to improve the dermoscopy image categorization for the skin lesions detection method. In Reference [13], the intuitive mining feature from two different database images has been studied using the machine learning method. [PERSON] et al. [14] presented a skin lesion segmentation technique by using deep Fully Convolutional Network (FCN) that can segment three samples of skin lesions (for example, healthy moles and unhealthy moles). [PERSON] et al. [21] used various dermoscopy datasets for a skin lesion full resolution convolutional network (FrCN) for a better performance when compared with existing methods of melanoma detection that leads to an enhancement in the segmentation analysis.