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Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Segmentation

机译:全自动3D T1加权脑磁共振图像分割方法

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In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brainon-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
机译:在医学成像领域,脑部MR图像的准确分割是许多脑部疾病所需要的。然而,由于诸如噪声,成像伪像,固有组织变化和部分体积效应等多种因素,组织分割仍然是一项艰巨的任务。因此,本文提出了一种全自动的脑部MR图像分割方法。该方法包括四个步骤的分割过程。首先,通过中值滤波去除噪声;使用阈值形态脑提取方法(TMBE)进行脑/非脑组织的第二次分割。然后执行基于灰度直方图分析的初始质心估计。最后,将模糊C均值算法用于MRI组织分割。通过使用模拟和真实MR图像进行的广泛分割实验证明了该方法的有效性。

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