Amiri Saba, Movahedi Mohammad Mehdi, Kazemi Kamran, Parsaei Hossein
Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran.
Health Technology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Med Biol Eng Comput. 2017 Mar;55(3):353-364. doi: 10.1007/s11517-016-1483-z. Epub 2016 May 20.
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.
通过图像分割技术在磁共振（MR）图像中识别出的三种脑软组织结构，即白质（WM）、灰质（GM）和脑脊液（CSF），有助于进行脑结构和功能分析、脑解剖结构测量与可视化、神经退行性疾病诊断以及手术规划和图像引导干预，但前提是获得的分割结果必须正确。本文提出了一种基于多分类器的系统，用于从脑部MR图像中自动分割脑组织。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。第一步，校正给定MR图像中的强度不均匀性，然后从图像中去除颅骨、眼球和皮肤等非脑组织。对于每个体素，计算统计特征和非统计特征，并使用一个特征向量来表示该体素。使用三个不同数据集训练的三个多层感知器（MLP）神经网络被用作多分类器系统的基础分类器。基础分类器的输出使用多数投票方案进行融合。使用具有不同噪声和强度不均匀性的Brainweb模拟MR图像以及互联网脑分割库（IBSR）真实MR图像对所提出的系统进行评估。使用骰子系数、杰卡德系数和一致性系数指标对所提出方法进行的定量评估表明，与单MLP分类器以及诸如FSL - FAST和SPM等现有方法和工具相比，在准确性方面有了提高（脑脊液方面提高了约5%）。由于准确分割MR图像对于成功推动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所获得的改进令人鼓舞。
