基于先验形状和局部统计的血管影像图像分割方法

田沄; 刘子丰; 赵世凤

doi:10.1631/FITEE.1800129

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基于先验形状和局部统计的血管影像图像分割方法

常规文章 | Updated：2022-05-19

- 基于先验形状和局部统计的血管影像图像分割方法
- Vascular segmentation of neuroimages based on a prior shape and local statistics
- 信息与电子工程前沿（英文） 2019年20卷第8期页码：1099-1108
- Affiliations：
  
  College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
  Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing 100875, China
- Author bio：
  
  Shi-feng ZHAO, E-mail: zhao_shifeng@bnu.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61472042 and 61802020), the Beijing Natural Science Foundation, China (No. 4174094), and the Fundamental Research Funds for the Central Universities, China (No. 2015KJJCB25)
- DOI：10.1631/FITEE.1800129
  中图分类号： TP391.4
- 收稿：2018-03-02，
  
  修回：2019-;8-15，
  
  纸质出版：2019-08
- Accepted：
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田沄, 刘子丰, 赵世凤. 基于先验形状和局部统计的血管影像图像分割方法[J]. 信息与电子工程前沿（英文）, 2019,20(8):1099-1108.

Yun TIAN, Zi-feng LIU, Shi-feng ZHAO. Vascular segmentation of neuroimages based on a prior shape and local statistics[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1099-1108.
田沄, 刘子丰, 赵世凤. 基于先验形状和局部统计的血管影像图像分割方法[J]. 信息与电子工程前沿（英文）, 2019,20(8):1099-1108. DOI： 10.1631/FITEE.1800129.

Yun TIAN, Zi-feng LIU, Shi-feng ZHAO. Vascular segmentation of neuroimages based on a prior shape and local statistics[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1099-1108. DOI： 10.1631/FITEE.1800129.

摘要

快速准确地从医学图像中提取血管结构是许多临床医疗的基础。然而，大多数血管分割方法忽略了分割结果中孤立点和冗余点的存在。本文提出一种基于先验形状和局部统计的血管分割方法，能有效消除异常值并精确分割粗细血管。首先，定义了一种改进的血管滤波器，用于量化每个体素属于管状结构的可能性；其次，执行匹配和连接操作以获得血管掩模；最后，在血管掩模基础上实现基于局部统计的区域生长方法，得到较为完整的无外围值的血管树。与Frangi方法以及Yang方法在实际血管造影图像上的实验和比较，证明该方法在保持血管分支连通的同时，可以有效去除异常值。

Abstract

Fast and accurate extraction of vascular structures from medical images is fundamental for many clinical procedures. However

most of the vessel segmentation techniques ignore the existence of the isolated and redundant points in the segmentation results. In this study

we propose a vascular segmentation method based on a prior shape and local statistics. It could efficiently eliminate outliers and accurately segment thick and thin vessels. First

an improved vesselness filter is defined. This quantifies the likelihood of each voxel belonging to a bright tubular-shaped structure. A matching and connection process is then performed to obtain a blood-vessel mask. Finally

the region-growing method based on local statistics is implemented on the vessel mask to obtain the whole vascular tree without outliers. Experiments and comparisons with Frangi's and Yang's models on real magneticresonance-angiography images demonstrate that the proposed method can remove outliers while preserving the connectivity of vessel branches.

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