基于图集质心力梯度矢量流活动轮廓的磁共振图像骨分割

T. K. Chuah, C. W. Lim, C. Poh, K. Sheah
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引用次数: 1

摘要

本文提出了一种利用基于图谱的质心力与梯度矢量流(GVF)参数化活动轮廓相结合的股骨松质骨分割技术。在我们的研究中使用的地图集提供了先验信息,以约束轮廓的区域,其中基于边缘的力缺失和初始化活动轮廓。由地图集导出的质心力填充GVF外力场。在我们的实现中,一旦地图集与待分割的目标图像注册,分割过程是全自动的。矢状面切片在髁间位置的21片分割精度分析,灵敏度为97.4±1.9%;特异性为99.6±0.1%,Dice相似系数为96.7±1.1%。从外力场检测和精度结果来看,质心力公式可以有效地逼近梯度矢量流场中缺失的边界,便于自动初始化。
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Bone Segmentation of Magnetic Resonance Images by Gradient Vector Flow Active Contour with Atlas Based Centroid Forces
This paper presents a segmentation technique which utilizes atlas based centroid forces coupled with Gradient Vector Flow (GVF) parametric active contour for the segmentation of femoral cancellous bone. The atlas used in our study provides prior information to constraint contours at regions where edge based forces are missing and to initialize the active contours. GVF external force field is padded with the centroid force derived from the atlas. In our implementation, once the atlas is registered with the target image to be segmented, the segmentation process is fully automatic. Analysis of segmentation accuracy of twenty one slices at the intercondylar location of sagittal slices provides sensitivity of 97.4±1.9%; specificity of 99.6±0.1%, Dice similarity coefficient of 96.7±1.1%. From the inspection of external force fields and the accuracy results, the study suggests that the centroid force formulation is effective in approximating missing boundaries in GVF and in facilitating automatic initialization.
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