IEEE J Biomed Health Inform. 2019 Mar;23(2):481-488. doi: 10.1109/JBHI.2018.2808970. Epub 2018 Feb 23.
Malignant skin lesions are among the most common types of cancer, and automated systems for their early detection are of fundamental importance. We propose SDI+, an unsupervised algorithm for the segmentation of skin lesions in dermoscopic images. It is articulated into three steps, aimed at extracting preliminary information on possible confounding factors, accurately segmenting the lesion, and post-processing the result. The overall method achieves high accuracy on dark skin lesions and can handle several cases where confounding factors could inhibit a clear understanding by a human operator. We present extensive experimental results and comparisons achieved by the SDI+ algorithm on the ISIC 2017 dataset, highlighting the advantages and disadvantages.
恶性皮肤病变是最常见的癌症类型之一，因此开发用于早期检测的自动化系统至关重要。我们提出了 SDI+，这是一种用于皮肤镜图像中皮肤病变分割的无监督算法。它由三个步骤组成，旨在提取可能的混杂因素的初步信息，准确地分割病变，并对结果进行后处理。该方法在深色皮肤病变上取得了很高的准确率，并且可以处理几种可能会干扰人工操作员清晰理解的混杂因素的情况。我们在 ISIC 2017 数据集上展示了 SDI+算法的广泛实验结果和比较，突出了其优缺点。
