Iyatomi Hitoshi, Norton Kerri-Ann, Celebi M, Schaefer Gerald, Tanaka Masaru, Ogawa Koichi
Faculty of Engineering, Hosei University, 3-7-2 Kajino-cho Koganei, 184-8522, Tokyo, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5407-10. doi: 10.1109/IEMBS.2010.5626500.
In this paper, we present a classification method of dermoscopy images between melanocytic skin lesions (MSLs) and non-melanocytic skin lesions (NoMSLs). The motivation of this research is to develop a pre-processor of an automated melanoma screening system. Since NoMSLs have a wide variety of shapes and their border is often ambiguous, we developed a new tumor area extraction algorithm to account for these difficulties. We confirmed that this algorithm is capable of handling different dermoscopy images not only those of NoMSLs but also MSLs as well. We determined the tumor area from the image using this new algorithm, calculated a total 428 features from each image, and built a linear classifier. We found only two image features, "the skewness of bright region in the tumor along its major axis" and "the difference between the average intensity in the peripheral part of the tumor and that in the normal skin area using the blue channel" were very efficient at classifying NoMSLs and MSLs. The detection accuracy of MSLs by our classifier using only the above mentioned image feature has a sensitivity of 98.0% and a specificity of 86.6% in a set of 107 non-melanocytic and 548 melanocytic dermoscopy images using a cross-validation test.
在本文中，我们提出了一种用于区分黑素细胞性皮肤病变（MSLs）和非黑素细胞性皮肤病变（NoMSLs）的皮肤镜图像分类方法。本研究的目的是开发一种自动黑色素瘤筛查系统的预处理程序。由于NoMSLs具有各种各样的形状且其边界通常不清晰，我们开发了一种新的肿瘤区域提取算法来解决这些难题。我们证实该算法不仅能够处理不同的皮肤镜图像，包括NoMSLs的图像，也能处理MSLs的图像。我们使用这种新算法从图像中确定肿瘤区域，从每个图像中计算总共428个特征，并构建了一个线性分类器。我们发现只有两个图像特征，即“肿瘤中明亮区域沿其主轴的偏度”和“使用蓝色通道时肿瘤周边部分的平均强度与正常皮肤区域平均强度之间的差异”在区分NoMSLs和MSLs方面非常有效。在一组107张非黑素细胞性和548张黑素细胞性皮肤镜图像上进行交叉验证测试时，我们的分类器仅使用上述图像特征对MSLs的检测准确率具有98.0%的灵敏度和86.6%的特异性。
