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首页> 外文会议>Conference on Medical Imaging: Physiology, Function, and Structure from Medical Images >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：纹理分析和在计算机断层摄影（CT）最近有新的方法，所述消歧问题棋盘其中两个明显不同的图案产生相同的共生矩阵，但这种方法四倍特征空间的尺寸先进图像软组织的分类。特征空间大小问题是通过使用不同尺寸的纹理运营商用于提高边界分割的加剧。维数降低激励此调查特征类别的功率的系统的分析（Haralick描述符，距离和方向）的软组织之间进行区分。该内部器官变异按功能类别提供了器官之间的歧视方面的排名他们的潜在力量的各个组成部分解释。本文介绍了一种技术，用于组合主成分分析（PCA）的结果进行比较和可视化的特征的解释力具有不同的窗口大小。我们发现：1）两个Haralick特色集群趋势和对比度的贡献最大; 2）随着距离的增加，其对总方差贡献减小;和3）方向是不重要的。我们也评估所提出的技术相对于它的分类能力。线性判别分析（LDA）和决策树（DT）用于基于缩减数据集合，产生两个分类模型。我们发现，使用PCA要么不能改善或明显降低LDA的分类性能还有DT模型。虽然分类显示没有承诺，所提出的技术提供了一个系统的机制比较功能降低策略改变窗口大小以及其他测量技术特征提取。

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