Katouzian Amin, Sathyanarayana Shashidhar, Baseri Babak, Konofagou Elisa E, Carlier Stéphane G
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
IEEE Trans Inf Technol Biomed. 2008 May;12(3):315-27. doi: 10.1109/titb.2007.912352.
In vivo plaque characterization is an important research field in interventional cardiology. We will study the realistic challenges to this goal by deploying 40 MHz single-element, mechanically rotating transducers. The intrinsic variability among the transducers' spectral parameters as well as tissue signals will be demonstrated. Subsequently, we will show that global data normalization is not suited for data calibration, due to the aforementioned variations as well as the stringent characteristics of spectral features. We will describe the sensitivity of an existing feature extraction algorithm based on eight spectral signatures (integrated backscatter coefficient, slope, midband-fit (MBF), intercept, and maximum and minimum powers and their relative frequencies) to a number of factors, such as the window size and order of the autoregressive (AR) model. It will be further demonstrated that the variations in the transducer's spectral parameters (i.e., center frequency and bandwidth) cause inconsistencies among extracted features. In this paper, two fundamental questions are addressed: 1) what is the best reliable way to extract the most informative features? and 2) which classification algorithm is the most appropriate for this problem? We will present a full-spectrum analysis as an alternative to the eight-feature approach. For the first time, different classification algorithms, such as k-nearest neighbors (k-NN) and linear Fisher, will be employed and their performances quantified. Finally, we will explore the reliability of the training dataset and the complexity of the recognition algorithm and illustrate that these two aspects can highly impact the accuracy of the end result, which has not been considered until now.
体内斑块特征分析是介入心脏病学中的一个重要研究领域。我们将通过部署40MHz单元素机械旋转换能器来研究实现这一目标所面临的实际挑战。将展示换能器频谱参数以及组织信号之间的固有变异性。随后，由于上述变化以及频谱特征的严格特性，我们将表明全局数据归一化不适用于数据校准。我们将描述一种基于八个频谱特征（积分背向散射系数、斜率、中频拟合（MBF）、截距以及最大功率和最小功率及其相对频率）的现有特征提取算法对多个因素的敏感性，例如窗口大小和自回归（AR）模型的阶数。进一步证明换能器频谱参数（即中心频率和带宽）的变化会导致提取特征之间的不一致。在本文中，我们解决了两个基本问题：1）提取最具信息性特征的最佳可靠方法是什么？2）哪种分类算法最适合这个问题？我们将提出全频谱分析作为八特征方法的替代方案。首次采用不同的分类算法，如k近邻（k-NN）和线性Fisher算法，并对它们的性能进行量化。最后，我们将探讨训练数据集的可靠性和识别算法的复杂性，并说明这两个方面会对最终结果的准确性产生重大影响，而这一点至今尚未得到考虑。
