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首页> 外文会议>International Workshop on Digital Mammography(IWDM 2006); 20060618-21; Manchester(GB) >Classifying Masses as Benign or Malignant Based on Co-occurrence Matrix Textures: A Comparison Study of Different Gray Level Quantizations
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Classifying Masses as Benign or Malignant Based on Co-occurrence Matrix Textures: A Comparison Study of Different Gray Level Quantizations

机译：基于共现矩阵纹理将肿块分为良性还是恶性：不同灰度量化的比较研究

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摘要

In this paper, co-occurrence matrix based texture features are used to classify masses as benign or malignant. As (digitized) mammograms have high depth resolution (4096 gray levels in this study) and the size of a co-occurrence matrix depends on Q, the number of gray levels used for image intensity (depth) quantization, computation using co-occurrence matrices derived from mammograms can be expensive. Re-quantization using a lower value of Q is routinely performed but the effect of such procedure has not been sufficiently investigated. This paper investigates the effect of re-quantization using different Q. Four feature pools are formed with features measured on co-occurrence matrices with Q ∈ {400}, Q ∈ {100}, Q ∈ {50} and Q ∈ {400, 100, 50}. Classification results are obtained from each pool separately with the use of a genetic algorithm and the Fisher's linear discriminant classifier. For Q ∈ {400, 100, 50}, the best feature subsets selected by the genetic algorithm and of size k = 6, 7, 8 have a leave-one-out area under the receiver operating characteristic (ROC) curve of 0.92, 0.93 and 0.94, respectively. Pairwise comparisons of the area index show that the differences in classification results for Q ∈ {400, 100, 50} and Q ∈ {50} are significant (p < 0.06) for all k while that for Q ∈ {400, 100, 50} and Q ∈ {400} or Q ∈ {100} are not significant.
机译：在本文中，基于共现矩阵的纹理特征用于将肿块分类为良性或恶性。由于（数字化）乳房X线照片具有较高的深度分辨率（本研究中为4096个灰度级），并且共现矩阵的大小取决于Q，用于图像强度（深度）量化的灰度级数量，使用共现矩阵进行计算从乳房X线照片得出的图像可能很昂贵。通常使用较低的Q值进行重新量化，但是尚未充分研究这种过程的效果。本文研究了使用不同Q进行重新量化的效果。形成了四个特征池，这些特征池通过对Q∈{400}，Q∈{100}，Q∈{50}和Q∈{400， 100，50}。使用遗传算法和Fisher线性判别分类器分别从每个库中获得分类结果。对于Q∈{400，100，50}，由遗传算法选择且大小为k = 6、7、8的最佳特征子集在接收器工作特性（ROC）曲线下的留一法则面积为0.92，分别为0.93和0.94。面积指数的成对比较显示，对于所有k，Q∈{400，100，50}和Q∈{50}的分类结果差异均显着（p <0.06），而Q∈{400，100，50 }和Q∈{400}或Q∈{100}不重要。

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