论文阅读:Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level se
2017-05-11 22:10
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【论文信息】
Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set
Int J CARS (2008) 3:257–265
DOI 10.1007/s11548-008-0230-9
【背景】
一直到2008年,大多数研究都是针对的X光图像,对于三维数据的研究还比较少。
综述里面提到了一些以前的方法。有借鉴价值。比如积分投影。
作者之前做过一个水平集牙齿分割
【方法】
![](https://oscdn.geek-share.com/Uploads/Images/Content/201705/8740f99da95bed2065e792e73b703c72)
![](https://oscdn.geek-share.com/Uploads/Images/Content/201705/90c7927c4b06aaab185097fd2197b104)
先用阈值分割和水平集得到牙齿区域,生成全景图。
在全景图上用考虑角度的两次积分投影来得到牙齿包围盒。然后以包围盒边界为初始化进行水平集分割。水平集模型很古老,最简单的边界长度面积驱动的模型。
还用巴特沃斯低通滤波减轻了CT伪影。
最后的实验部分几个测度都比较古老。
【讨论&启发】
08年的文章还是很单纯的,方法的细节都讲得很仔细;不像现在的已经不行了,比较水。
Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set
Int J CARS (2008) 3:257–265
DOI 10.1007/s11548-008-0230-9
【背景】
一直到2008年,大多数研究都是针对的X光图像,对于三维数据的研究还比较少。
综述里面提到了一些以前的方法。有借鉴价值。比如积分投影。
作者之前做过一个水平集牙齿分割
【方法】
先用阈值分割和水平集得到牙齿区域,生成全景图。
在全景图上用考虑角度的两次积分投影来得到牙齿包围盒。然后以包围盒边界为初始化进行水平集分割。水平集模型很古老,最简单的边界长度面积驱动的模型。
还用巴特沃斯低通滤波减轻了CT伪影。
最后的实验部分几个测度都比较古老。
【讨论&启发】
08年的文章还是很单纯的,方法的细节都讲得很仔细;不像现在的已经不行了,比较水。
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