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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Organ Analysis and Classification using Principal Component and Linear Discriminant Analysis
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Organ Analysis and Classification using Principal Component and Linear Discriminant Analysis

机译：使用主成分和线性判别分析的器官分析和分类

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Texture analysis and classification of soft tissues in Computed Tomography (CT) images recently advanced with a new approach that disambiguates the checkboard problem where two distinctly different patterns produce identical co-occurrence matrices, but this method quadruples the size of the feature space. The feature space size problem is exacerbated by the use of varying sized texture operators for improving boundary segmentation. Dimensionality reduction motivates this investigation into systematic analysis of the power of feature categories (Haralick descriptors, distance, and direction) to differentiate between soft tissues. The within-organ variance explained by the individual components of feature categories offers a ranking of their potential power for between-organ discrimination. This paper introduces a technique for combining the Principal Component Analysis (PCA) results to compare and visualize the explanatory power of features with varying window sizes. We found that 1) the two Haralick features Cluster Tendency and Contrast contribute the most; 2) as distance increases, its contribution to overall variance decreases; and 3) direction is unimportant. We also evaluated the proposed technique with respect to its classification power. Linear Discriminant Analysis (LDA) and Decision Tree (DT) were used to produce two classification models based on the reduced data set. We found that using PCA either fails to improve or markedly degrades the classification performance of LDA as well as of the DT model. Though feature extraction for classification shows no promise, the proposed technique offers a systematic mechanism to compare feature reduction strategies for varying window sizes as well as other measurement techniques.
机译：计算机断层扫描（CT）图像中的软组织的纹理分析和分类最近通过一种新方法得到了发展，该方法消除了棋盘格问题，其中两个截然不同的模式产生相同的共现矩阵，但是此方法将特征空间的大小增加了四倍。使用大小可变的纹理运算符来改善边界分割会加剧特征空间大小的问题。降维促使本研究进行系统分析，以分析特征类别（Haralick描述符，距离和方向）以区分软组织的能力。由特征类别的各个组成部分解释的器官内差异提供了它们对器官间区分的潜在能力的排名。本文介绍了一种结合主成分分析（PCA）结果以比较和可视化具有不同窗口大小的特征的解释能力的技术。我们发现：1）Haralick的两个特征簇倾向性和对比度贡献最大； 2）随着距离增加，其对总体方差的贡献减小； 3）方向不重要。我们还根据分类能力评估了提出的技术。线性判别分析（LDA）和决策树（DT）用于基于精简数据集生成两个分类模型。我们发现使用PCA不能改善​​LDA以及DT模型的分类性能，或者明显降低其分类性能。尽管用于分类的特征提取没有希望，但所提出的技术提供了一种系统化的机制来比较不同窗口大小的特征缩减策略以及其他测量技术。

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