Wiltgen Marco, Gerger Armin, Wagner Christian, Bergthaler Patrick, Smolle Josef
Institute of Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
Anal Quant Cytol Histol. 2007 Aug;29(4):251-63.
To investigate the applicability of different texture features in automatic discrimination of microscopic views from benign common nevi and malignant melanoma lesions.
In tissue counter analysis (TCA) the images are dissected into square elements used for feature calculation. The first class of features is based on the histogram, the co-occurrence matrix and the texture moments. The second class is derived from spectral properties of the wavelet Daubechie 4 and the Fourier transform. Square elements from images of a training set are classified by Classification and Regression Trees analysis.
Features from the histogram and the co-occurrence matrix enable correct classification of 94.7% of nevi elements and 92.6% of melanoma elements in the training set. Classification results are applied to individual test set cases. Discriminant analysis based on the percentage of "malignant elements" showed correct classification of all nevi cases and 95% of melanoma cases. Features derived from the wavelet and Fourier spectrum showed correct results for 88.8% and 79.3% of nevi and 85.6% and 81.5% of melanoma elements, respectively.
TCA is a potential diagnostic tool in automatic analysis of melanocytic skin tumors. Histogram and co-occurrence matrix features are superior to the wavelet and the Fourier features.
研究不同纹理特征在自动鉴别良性普通痣和恶性黑色素瘤病变显微镜图像中的适用性。
在组织计数器分析（TCA）中，图像被分割成用于特征计算的方形元素。第一类特征基于直方图、共生矩阵和纹理矩。第二类特征源自小波Daubechie 4的光谱特性和傅里叶变换。通过分类与回归树分析对训练集图像中的方形元素进行分类。
直方图和共生矩阵的特征能够正确分类训练集中94.7%的痣元素和92.6%的黑色素瘤元素。分类结果应用于各个测试集病例。基于“恶性元素”百分比的判别分析显示，所有痣病例和95%的黑色素瘤病例分类正确。源自小波和傅里叶光谱的特征分别对88.8%和79.3%的痣元素以及85.6%和81.5%的黑色素瘤元素显示出正确结果。
TCA是黑色素细胞性皮肤肿瘤自动分析中的一种潜在诊断工具。直方图和共生矩阵特征优于小波和傅里叶特征。
