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利用先验知识从单层、多层或全容积磁共振扫描中进行自动脑部分割。

Automated brain segmentation from single slice, multislice, or whole-volume MR scans using prior knowledge.

作者信息

Saeed N, Hajnal J V, Oatridge A

机构信息

Picker Research Laboratory, GEC Hirst Research Centre, Borehamwood, England.

出版信息

J Comput Assist Tomogr. 1997 Mar-Apr;21(2):192-201. doi: 10.1097/00004728-199703000-00005.

Abstract

PURPOSE

An automated procedure has been developed to isolate the brain in single/multislice or whole-volume MR images obtained from various sequences.

METHOD

T1-weighted, T2-weighted, and inversion recovery images were acquired. The brain segmentation procedure employed (A) a knowledge base that held generic information about the brain in the three orthogonal views and (B) a texture definition and intensity characteristics of features within the head. The brain was segmented by selectively blurring scans using components of B; contour following with region growing was initiated until the isolated feature satisfied the measurements in A.

RESULTS

The brain was segmented automatically from 210 subjects (whole volume) and 52 subjects (multi/single slice). Detailed analysis of seven segmented brains showed that < 0.8% of the contour pixels were erroneously identified. Whole-volume head scans consisting of 140 x 256 x 256 pixels were segmented in < 30 min.

CONCLUSION

A robust, fast, and efficient procedure has been developed to segment the brain from MR images.

摘要

目的

已开发出一种自动化程序,用于从各种序列获取的单/多层或全容积磁共振成像(MR)图像中分离出大脑。

方法

采集了T1加权、T2加权和反转恢复图像。大脑分割程序采用(A)一个知识库,该知识库保存了大脑在三个正交视图中的一般信息,以及(B)头部内特征的纹理定义和强度特征。通过使用B的组件选择性地模糊扫描来分割大脑;开始进行轮廓跟踪和区域生长,直到分离出的特征满足A中的测量要求。

结果

从210名受试者(全容积)和52名受试者(多层/单层)中自动分割出大脑。对七个分割后的大脑进行详细分析表明,轮廓像素的错误识别率<0.8%。由140×256×256像素组成的全容积头部扫描在<30分钟内完成分割。

结论

已开发出一种强大、快速且高效的程序,用于从MR图像中分割大脑。

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