Abstract：
Skin cancer affects people of all ages and is a common disease. The death toll from skin cancer rises with a late diagnosis. An automated mechanism for early-stage skin cancer detection is required to diminish the mortality rate. Visual examination with scanning or imaging screening is a common mechanism for detecting this disease, but due to its similarity to other diseases, this mechanism shows the least accuracy. This article introduces an innovative segmentation mechanism that operates on the ISIC dataset to divide skin images into critical and non-critical sections. The main objective of the research is to segment lesions from dermoscopic skin images. The suggested framework is completed in two steps. The first step is to pre-process the image; for this, we have applied a bottom hat filter for hair removal and image enhancement by applying DCT and color coefficient. In the next phase, a background subtraction method with midpoint analysis is applied for segmentation to extract the region of interest and achieves an accuracy of 95.30%. The ground truth for the validation of segmentation is accomplished by comparing the segmented images with validation data provided with the ISIC dataset.
摘要：
皮肤癌影响所有年龄段的人，是一种常见疾病。皮肤癌的死亡人数随着晚期诊断而上升。需要一种用于早期皮肤癌检测的自动化机制来降低死亡率。通过扫描或影像学筛查进行视觉检查是检测这种疾病的常见机制，但是由于它与其他疾病相似，这种机制显示的准确性最低。本文介绍了一种创新的分割机制，该机制对ISIC数据集进行操作，将皮肤图像分为关键部分和非关键部分。研究的主要目的是从皮肤镜皮肤图像中分割病变。建议的框架分两步完成。第一步是预处理图像;为此，通过应用DCT和颜色系数，我们已经应用了一个底帽滤波器来进行脱毛和图像增强。在下一阶段,采用中点分析的背景减除法进行分割，提取感兴趣区域，准确率达到95.30%。通过将分割图像与ISIC数据集提供的验证数据进行比较，可以实现分割验证的基本事实。
