Instituto de Informática, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves 9500, Porto Alegre, RS 91501-970, Brazil.
Comput Med Imaging Graph. 2011 Sep;35(6):481-91. doi: 10.1016/j.compmedimag.2011.02.007. Epub 2011 Apr 12.
This paper describes a new method for classifying pigmented skin lesions as benign or malignant. The skin lesion images are acquired with standard cameras, and our method can be used in telemedicine by non-specialists. Each acquired image undergoes a sequence of processing steps, namely: (1) preprocessing, where shading effects are attenuated; (2) segmentation, where a 3-channel image representation is generated and later used to distinguish between lesion and healthy skin areas; (3) feature extraction, where a quantitative representation for the lesion area is generated; and (4) lesion classification, producing an estimate if the lesion is benign or malignant (melanoma). Our method was tested on two publicly available datasets of pigmented skin lesion images. The preliminary experimental results are promising, and suggest that our method can achieve a classification accuracy of 96.71%, which is significantly better than the accuracy of comparable methods available in the literature.
本文提出了一种新的色素性皮肤病变良性或恶性分类方法。皮肤病变图像由标准相机采集，本方法可由非专业人员在远程医疗中使用。每个采集的图像都要经过一系列处理步骤，即：（1）预处理，衰减阴影效应；（2）分割，生成一个三通道图像表示，然后用于区分病变和健康皮肤区域；（3）特征提取，生成病变区域的定量表示；（4）病变分类，估计病变是良性还是恶性（黑色素瘤）。我们的方法在两个公开的色素性皮肤病变图像数据集上进行了测试。初步的实验结果很有希望，表明我们的方法可以达到 96.71%的分类准确率，明显优于文献中已有可比方法的准确率。
