Low contrast images and blurriness pose challenge in the over-segmentation of image, which increases model complexities. In this work, a novel hybrid dermoscopic skin-lesion segmentation method, namely SLICACO, is proposed incorporating the simple linear iterative clustering (SLIC) and ant colony optimization (ACO) algorithms. The working of proposed method is multifold. First, over-segmentation of preprocessed image is generated using SLIC super-pixel technique. Second, clusters of super-pixels generated by SLIC are used by ACO with the pixels of similar intensity for edge detection and seek for the optimum pathway in a strained zone. Third, lesion area is segmented using the Convex Hull and Thresholding. Fourth, Erosion Filtering is used to obtain the final segmented image. The performance of SLICACO is assessed on five benchmark dermatoscopic datasets and compared with deep learning models to test its generalizing behavior. Promising results are obtained on the PH2 archive data set with an accuracy of 95.9%.

中文翻译：

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SLICACO：一种用于皮肤镜黑色素细胞皮肤病变分割的自动化新型混合方法

低对比度图像和模糊性对图像的过度分割提出了挑战，这增加了模型的复杂性。在这项工作中，提出了一种新的混合皮肤镜皮肤病灶分割方法，即 SLICACO，它结合了简单线性迭代聚类 (SLIC) 和蚁群优化 (ACO) 算法。所提出方法的工作是多方面的。首先，使用 SLIC 超像素技术生成预处理图像的过度分割。其次，ACO 使用由 SLIC 生成的超像素簇与强度相似的像素进行边缘检测，并在应变区域中寻找最佳路径。第三，使用凸包和阈值分割病变区域。第四，使用侵蚀过滤来获得最终的分割图像。SLICACO 的性能在五个基准皮肤镜数据集上进行评估，并与深度学习模型进行比较以测试其泛化行为。在 PH2 存档数据集上获得了有希望的结果，准确率为 95.9%。