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An automated MRI segmentation by using fuzzy C mean and volumetric analysis

机译:基于模糊C均值和体积分析的MRI自动分割

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Automated MRI segmentation techniques are helpful for a physician for early diagnosis of degenerating diseases in individual patients. Here we are using the T1weighted axial MR images of neuro degenerative diseases. The assessment of the accuracy of the result is done by an expert. FCM an unsupervised clustering technique is implemented in order to classify the brain voxel. The brain voxels are classified into three main tissue types: Gray Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). We hypothesized that extracting volumetric data from patient's MR brain images, relating them to reference data would aid diagnostic readers in classifying neurodegenerative diseases. Volumetric anatomical information extracted from brain images using automatic segmentation can support diagnostic decision making.
机译:自动化的MRI分割技术有助于医生对个别患者的变性疾病进行早期诊断。在这里,我们使用神经退行性疾病的T1加权轴向MR图像。结果准确性的评估由专家进行。 FCM实施了无监督聚类技术,以对脑素进行分类。脑体素分为三种主要组织类型:灰质(GM),白质(WM)和脑脊髓液(CSF)。我们假设从患者的MR脑部图像中提取体积数据,并将其与参考数据相关联,将有助于诊断读者对神经退行性疾病进行分类。使用自动分割从大脑图像中提取的体积解剖信息可以支持诊断决策。

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