Although promising for studying the microstructure of in vivo tissues, the performance and the potentiality of diffusion tensor magnetic resonance imaging are hampered by the presence of high-level noise in diffusion weighted (DW) images. This paper proposes a novel smoothing approach, called the nonstationarity adaptive filtering, which estimates the intensity of a pixel by averaging intensities in its adaptive homogeneous neighborhood. The latter is determined according to five constraints and spatiodirectional nonstationarity measure maps. The proposed approach is compared with an anisotropic diffusion method used in DW image smoothing. Experimental results on both synthetic and real human DW images show that the proposed method achieves a better compromise between the smoothness of homogeneous regions and the preservation of desirable features such as boundaries, even for highly noisy data, thus leading to homogeneously consistent tensor fields and consequently more coherent fibers.

译文

:尽管对于研究体内组织的微结构很有希望,但是由于扩散加权(DW)图像中存在高水平的噪声,因此扩散张量磁共振成像的性能和潜力受到了限制。本文提出了一种新的平滑方法,称为非平稳自适应滤波,该方法通过对像素的自适应均质邻域中的强度进行平均来估计像素的强度。后者是根据五个约束条件和空间定向非平稳性测度图确定的。将该方法与DW图像平滑中使用的各向异性扩散方法进行了比较。在合成和真实人类DW图像上的实验结果表明,即使对于嘈杂的数据,所提出的方法在均匀区域的平滑度和所需特征(如边界)的保留之间也取得了较好的折衷,从而导致张量场均匀一致,因此更紧密的纤维。

+1
+2
100研值 100研值 ¥99课程
检索文献一次
下载文献一次

去下载>

成功解锁2个技能,为你点赞

《SCI写作十大必备语法》
解决你的SCI语法难题!

技能熟练度+1

视频课《玩转文献检索》
让你成为检索达人!

恭喜完成新手挑战

手机微信扫一扫,添加好友领取

免费领《Endnote文献管理工具+教程》

微信扫码, 免费领取

手机登录

获取验证码
登录