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MULTISCALE ACTIVE CONTOUR TRANSFORMATION BASED TOOLBOX FOR THE EXTRACTION OF WHITE MATTER FROM BRAIN FMRI IMAGES

机译:基于MultiScale Active Contout变换的工具箱,用于从脑FMRI图像提取白质

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In this paper, we propose a toolbox for the extraction of white matter from brain fMRI images. The underlying algorithm is based on the multiscale active contour (MSAC) transform that includes adjustable parameters. We semiautomatic ally tune the parameters through a supervised learning approach. To obtain a desirable segmentation with minimal human intervention and hide the complexities involved in the algorithm from our end users (biologists and medical practitioners), we design an interactive toolbox that provides a user friendly interface. The backend of this package is designed to segment the fMRI image along three views viz., axial, coronal and sagittal and perform a majority voting to classify each voxel as white or nonwhite. We find this procedure yields an area similarity of 88% with the hand-segmented ground truth and outperforms the previous approach using active contour based segmentation algorithm.
机译:在本文中,我们提出了一种工具箱,用于从脑FMRI图像提取白质。底层算法基于包括可调参数的多尺度活动轮廓(MSAC)变换。我们通过监督学习方法进行半自动盟友调整参数。为了获得具有最小的人类干预并隐藏来自我们最终用户(生物学家和医疗从业人员)涉及算法中涉及的复杂性的理想分段,我们设计了一个提供用户友好界面的交互式工具箱。此包的后端旨在沿三个视图沿三个视图段段。,轴向,冠状和矢状物,并执行大多数投票,以将每个体素分类为白色或非白色或非白色的voxel。我们发现此过程与手工分割的地面真理产生88%的区域相似度,并使用基于主动轮廓的分段算法优于先前的方法。

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