Tsiaparas Nikolaos N, Golemati Spyretta, Andreadis Ioannis, Stoitsis John S, Valavanis Ioannis, Nikita Konstantina S
Department of Electrical and Computer Engineering, National Technical University of Athens, Athens 15780, Greece.
IEEE Trans Inf Technol Biomed. 2011 Jan;15(1):130-7. doi: 10.1109/TITB.2010.2091511. Epub 2010 Nov 11.
In this paper, a multiresolution approach is suggested for texture classification of atherosclerotic tissue from B-mode ultrasound. Four decomposition schemes, namely, the discrete wavelet transform, the stationary wavelet transform, wavelet packets (WP), and Gabor transform (GT), as well as several basis functions, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. The mean and standard deviation of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included 1) ranking the features in terms of their divergence values and 2) appropriately thresholding by a nonlinear correlation coefficient. The selected features were subsequently input into two classifiers using support vector machines (SVM) and probabilistic neural networks. WP analysis and the coiflet 1 produced the highest overall classification performance (90% for diastole and 75% for systole) using SVM. This might reflect WP's ability to reveal differences in different frequency bands, and therefore, characterize efficiently the atheromatous tissue. An interesting finding was that the dominant texture features exhibited horizontal directionality, suggesting that texture analysis may be affected by biomechanical factors (plaque strains).
本文提出了一种用于从B型超声中对动脉粥样硬化组织进行纹理分类的多分辨率方法。研究了四种分解方案，即离散小波变换、平稳小波变换、小波包（WP）和伽柏变换（GT），以及几种基函数区分有症状和无症状病例的能力。将每种分解方案产生的细节子图像的均值和标准差用作纹理特征。特征选择包括：1）根据特征的散度值对其进行排序；2）通过非线性相关系数进行适当的阈值处理。随后，将所选特征输入到使用支持向量机（SVM）和概率神经网络的两个分类器中。使用SVM时，小波包分析和第1类coiflet小波产生了最高的总体分类性能（舒张期为90%，收缩期为75%）。这可能反映了小波包揭示不同频带差异的能力，因此能够有效地对动脉粥样硬化组织进行特征描述。一个有趣的发现是，主要的纹理特征呈现出水平方向性，这表明纹理分析可能受到生物力学因素（斑块应变）的影响。
