论著摘要 |【AI-MR】使用多分类器系统的3D脑部MR图像分割(双语版)

2018-02-24 10:28:50 admin
标签:   人工智能 大脑分割 MR 神经网络

3D cerebral MR image segmentation using multiple-classifier system.

发表日期: 2017.03.01   来源:Med Biol Eng Comput. 2017 Mar;55(3):353-364.

作者:

Amiri S1, Movahedi MM2,3, Kazemi K4, Parsaei H1,5.

作者介绍:

1. Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran.

2. Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran. movahedim@sums.ac.ir.

3. Health Technology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. movahedim@sums.ac.ir.

4. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.

5. Health Technology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.

摘要

通过图像分割技术在磁共振(MR)图像中识别出的三个软脑组织白质(WM),灰质(GM)和脑脊液(CSF)可以辅助结构和功能脑分析,大脑解剖结构测量和可视化,神经退行性疾病诊断,手术规划和图像引导干预,但只有获得分割结果是正确的。本文提出了一种基于多分类器的脑部MR图像自动脑组织分割系统。所开发的系统将给定MR图像的每个体素分类为GM,WM和CSF。该算法包括预处理,特征提取和监督分类步骤。在第一步中,对给定MR图像中的强度不均匀性进行校正,然后从图像中去除诸如头骨,眼球和皮肤之类的非脑组织。对于每个体素,计算统计特征和非统计特征,并使用表示体素的特征向量。使用三个不同的数据集训练的三层多感知器(MLP)神经网络被用作多分类器系统的基分类器。 基本分类器的输出使用多数投票方案进行融合。所提出的系统的评估使用具有不同噪声和强度不均匀性的Brainweb模拟MR图像和互联网脑部分割库(IBSR)真实MR图像进行。与单个MLP分类器和现有的方法和工具,如FSL-FAST和SPM相比,使用Dice,Jaccard和一致性系数度量的所提出的方法的定量评估证明在精度方面的改进(CSF的约5%)。由于准确分割MR图像对于成功推广MR图像分割技术的临床应用至关重要,因此使用基于多分类器的系统获得的改善是令人鼓舞的。

Abstact

The three soft brain tissues white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) identified in a magnetic resonance (MR) image via image segmentation techniques can aid in structural and functional brain analysis, brain's anatomical structures measurement and visualization, neurodegenerative disorders diagnosis, and surgical planning and image-guided interventions, but only if obtained segmentation results are correct. This paper presents a multiple-classifier-based system for automatic brain tissue segmentation from cerebral MR images. The developed system categorizes each voxel of a given MR image as GM, WM, and CSF. The algorithm consists of preprocessing, feature extraction, and supervised classification steps. In the first step, intensity non-uniformity in a given MR image is corrected and then non-brain tissues such as skull, eyeballs, and skin are removed from the image. For each voxel, statistical features and non-statistical features were computed and used a feature vector representing the voxel. Three multilayer perceptron (MLP) neural networks trained using three different datasets were used as the base classifiers of the multiple-classifier system. The output of the base classifiers was fused using majority voting scheme. Evaluation of the proposed system was performed using Brainweb simulated MR images with different noise and intensity non-uniformity and internet brain segmentation repository (IBSR) real MR images. The quantitative assessment of the proposed method using Dice, Jaccard, and conformity coefficient metrics demonstrates improvement (around 5 % for CSF) in terms of accuracy as compared to single MLP classifier and the existing methods and tools such FSL-FAST and SPM. As accurately segmenting a MR image is of paramount importance for successfully promoting the clinical application of MR image segmentation techniques, the improvement obtained by using multiple-classifier-based system is encouraging.

关键词:

大脑;影像分割;MRI;多层感知机;多分类系统;神经网络

Keywords:

Brain; Image segmentation; MRI; Multilayer perception; Multiple-classifier system; Neural network

阅读原文:PMID: 27207464  DOI: 10.1007/s11517-016-1483-z


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