Handels H, Ross T, Kreusch J, Wolff H H, Pöppl S J
Institute for Medical Informatics, Medical University of Lübeck, Germany.
Artif Intell Med. 1999 Jul;16(3):283-97. doi: 10.1016/s0933-3657(99)00005-6.
In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.
本文提出了一种皮肤病学中计算机辅助皮肤肿瘤诊断的新方法。通过分析高分辨率皮肤表面轮廓来自动识别恶性黑色素瘤和痣细胞痣（痣）。第一步，采用二维图像分析方法提取几种类型的特征，以表征皮肤表面轮廓的结构：基于共生矩阵的纹理特征、傅里叶特征和分形特征。然后，应用特征选择算法来确定识别过程中合适的特征子集。特征选择被描述为一个优化问题，并比较了包括启发式策略、贪婪算法和遗传算法在内的几种方法。作为特征子集的质量度量，使用留一法计算的最近邻分类器的分类率。遗传算法显示出最佳结果。最后，使用选定的特征集训练以误差反向传播为学习范式的神经网络。研究了不同的网络拓扑结构、学习参数和剪枝算法，以优化神经分类器的分类性能。使用优化后的识别系统，分类性能达到了97.7%。
