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首页> 外文会议>18th International Conference on Information Processing in Medical Imaging IPMI 2003 Jul 20-25, 2003 Ambleside, UK >Volumetric Texture Description and Discriminant Feature Selection for MRI
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Volumetric Texture Description and Discriminant Feature Selection for MRI

机译：MRI的体积纹理描述和判别特征选择

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摘要

This paper considers the problem of classification of Magnetic Resonance Images using 2D and 3D texture measures. Joint statistics such as co-occurrence matrices are common for analysing texture in 2D since they are simple and effective to implement. However, the computational complexity can be prohibitive especially in 3D. In this work, we develop a texture classification strategy by a sub-band filtering technique that can be extended to 3D. We further propose a feature selection technique based on the Bhattacharyya distance measure that reduces the number of features required for the classification by selecting a set of discriminant features conditioned on a set training texture samples. We describe and illustrate the methodology by quantitatively analysing a series of images: 2D synthetic phantom, 2D natural textures, and MRI of human knees.
机译：本文考虑了使用2D和3D纹理度量对磁共振图像进行分类的问题。联合统计（例如共现矩阵）在2D中分析纹理很常见，因为它们很容易实现且有效。但是，特别是在3D中，计算复杂性可能会令人望而却步。在这项工作中，我们通过可扩展到3D的子带滤波技术开发了一种纹理分类策略。我们进一步提出了一种基于Bhattacharyya距离度量的特征选择技术，该技术通过选择基于一组训练纹理样本的一组判别特征来减少分类所需的特征数量。我们通过定量分析一系列图像来描述和说明该方法：2D合成体模，2D自然纹理和人膝MRI。

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