IEEE J Biomed Health Inform. 2019 Mar;23(2):489-500. doi: 10.1109/JBHI.2018.2832455. Epub 2018 May 2.
This paper proposes a computer assisted diagnostic (CAD) system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim to validate these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve precisely to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH dermoscopy dataset. An extensive experimental analysis reveals two important findings: 1). The proposed segmentation method mimics the ground truth data accurately, outperforming the other methods that have been used for comparison purposes, and 2). The most significant melanoma characteristics in the lesion actually lie on the lesion periphery.
本文提出了一种用于检测皮肤镜图像中黑色素瘤的计算机辅助诊断（CAD）系统。临床研究得出的结论是，在黑色素瘤的情况下，病变边界表现出与正常皮肤斑点不同的结构，如色素网络和条纹，而正常皮肤斑点的边界则更平滑。我们旨在通过对皮肤病变进行分割，然后提取病变的周边区域，对这些区域进行特征提取和分类，以检测黑色素瘤，从而验证这些发现。对于分割，我们提出了一种新颖的基于主动轮廓的方法，该方法采用初始病变轮廓，然后使用病变和皮肤之间的 Kullback-Leibler 散度来精确拟合曲线到病变边界。在病变分割后，提取其周边区域，使用基于局部二值模式的图像特征来检测黑色素瘤。为了验证我们的算法，我们使用了公开的 PH 皮肤镜数据集。广泛的实验分析揭示了两个重要发现：1）. 所提出的分割方法准确地模拟了真实数据，优于其他用于比较目的的方法；2）. 病变中实际上存在的最显著的黑色素瘤特征位于病变周边。
