Nielsen Birgitte, Albregtsen Fritz, Danielsen Håvard E
Department of Informatics, University of Oslo, P.O. Box 1080 Blindern, N-03 16 Oslo, Norway.
IEEE Trans Med Imaging. 2004 Jan;23(1):73-84. doi: 10.1109/TMI.2003.819923.
In many popular texture analysis methods, second or higher order statistics on the relation between pixel gray level values are stored in matrices. A high dimensional vector of predefined, nonadaptive features is then extracted from these matrices. Identifying a few consistently valuable features is important, as it improves classification reliability and enhances our understanding of the phenomena that we are modeling. Whatever sophisticated selection algorithm we use, there is a risk of selecting purely coincidental "good" feature sets, especially if we have a large number of features to choose from and the available data set is limited. In a unified approach to statistical texture feature extraction, we have used class distance and class difference matrices to obtain low dimensional adaptive feature vectors for texture classification. We have applied this approach to four relevant texture analysis methods. The new adaptive features outperformed the classical features when applied to the most difficult set of 45 Brodatz texture pairs. Class distance and difference matrices also clearly illustrated the difference in texture between cell nucleus images from two different prognostic classes of early ovarian cancer. For each of the texture analysis methods, one adaptive feature contained most of the discriminatory power of the method.
在许多流行的纹理分析方法中，关于像素灰度值之间关系的二阶或更高阶统计量被存储在矩阵中。然后从这些矩阵中提取一个由预定义的、非自适应特征组成的高维向量。识别出一些始终有价值的特征很重要，因为这可以提高分类的可靠性，并增强我们对所建模现象的理解。无论我们使用多么复杂的选择算法，都存在选择纯粹偶然的“好”特征集的风险，特别是当我们有大量特征可供选择且可用数据集有限时。在一种统一的统计纹理特征提取方法中，我们使用类距离矩阵和类差异矩阵来获得用于纹理分类的低维自适应特征向量。我们已将此方法应用于四种相关的纹理分析方法。当应用于45组最难的布罗达兹纹理对时，新的自适应特征优于经典特征。类距离矩阵和差异矩阵也清楚地说明了来自早期卵巢癌两种不同预后类别的细胞核图像在纹理上的差异。对于每种纹理分析方法，一个自适应特征包含了该方法的大部分判别能力。
