论著摘要 |【AI-MR】基于纹理特征的人工神经网络自动脑MR图像去噪(双语版)

2018-01-12 14:52:52 admin 0
标签:   人工智能 MRI 图像去噪 人工神经网络

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

发表日期: 2016.12.07   来源:Biomed Mater Eng. 2015;26 Suppl 1:S1275-82.

作者:

Chang YN1, Chang HH1.

作者介绍:

1. Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, Daan 10617 Taipei, Taiwan.

摘要

噪声是质量恶化的主要原因之一,不仅用于视觉检查,还用于脑部磁共振(MR)图像分析(如组织分类,分割和配准)中的计算机化处理。因此,脑MR图像中的噪声去除对于各种后续处理应用而言是重要的。 然而,大多数现有的去噪算法需要费力地调整对特定图像特征和纹理敏感的参数。通过人工智能技术自动化这些参数将是非常有益的。在本研究中,提出了一种与图像纹理特征分析相关的人工神经网络来建立一个可预测的参数模型,并对去噪过程进行自动化。在所提出的方法中,总共有83个图像属性基于四个类别被提取:1)基本图像统计。2)灰度共生矩阵(GLCM)。3)灰度行程矩阵(GLRLM)和4)田村纹理特征。为了获得这些纹理特征中的区分度的排序,对每个图像中计算的每个单独的图像特征应用配对样本t检验。随后,使用顺序前向选择(SFS)方法根据区分的等级来选择最佳的纹理特征。 所选择的最优特征被进一步并入到反向传播神经网络中以建立可预测的参数模型。 采用各种各样的MR图像来评估所提出的框架的性能。 实验结果表明,这种新型自动化系统能够准确预测双边滤波参数,有效去除多幅MR图像中的噪声。与手动调谐的滤波处理相比,我们的方法不仅产生了更好的去噪效果,而且节省了大量的处理时间。

Abstact

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

关键词:

图像特征,MRI,图像去噪,图像纹理,神经网络

Keywords:

Image feature; MRI; image denoising; image texture; neural network

阅读原文:PMID: 26405887  DOI: 10.3233/BME-151425


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