北京航空航天大学学报 ›› 2015, Vol. 41 ›› Issue (6): 982-988.doi: 10.13700/j.bh.1001-5965.2014.0502

• 论文 • 上一篇    下一篇

CTA影像头部骨骼组织提取算法

曹春红1,2, 艾亮3, 许光星1,2   

  1. 1. 东北大学 信息科学与工程学院, 沈阳 110819;
    2. 东北大学 医学影像计算教育部重点实验室, 沈阳 110819;
    3. 沈阳铁路局 信息技术所, 沈阳 110001
  • 收稿日期:2014-08-11 出版日期:2015-06-20 发布日期:2015-07-30
  • 通讯作者: 曹春红(1976—),女,吉林四平人,副教授,caochunhong@ise.neu.edu.cn,主要研究方向为计算机图形学、计算机图像处理. E-mail:caochunhong@ise.neu.edu.cn
  • 基金资助:
    国家自然科学基金(61300096); 中央高校基本科研业务费专项资金(N130404013)

Head bone tissue extraction algorithm based on CTA image

CAO Chunhong1,2, AI Liang3, XU Guangxing1,2   

  1. 1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
    2. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang 110819, China;
    3. Information Computing Institute, Shenyang Railway Bureau, Shenyang 110001, China
  • Received:2014-08-11 Online:2015-06-20 Published:2015-07-30

摘要: 计算机断层血管造影(CTA)影像单纯根据灰度信息无法良好地分离血管组织和骨骼组织.结合CTA影像的灰度特点,提出基于改进的三维区域生长算法的骨骼组织外轮廓提取和基于改进的Snake模型的骨骼提取算法.首先结合概率论的相关知识改进区域生长判定条件的准确性,提出三维区域生长的快速的骨骼区域种子点提取方法,使得它可以获得比较准确的骨骼组织区域.之后选取Snake模型并对其进行改进,增加了影像能量信息项,使得该模型可以更好地解决当前的问题.最后给出了实验结果并和传统算法进行对比,证实所提出的骨骼组织分割提取算法效果良好.

关键词: 骨骼提取, 三维分割, 计算机断层血管造影(CTA), 区域增长, 医学影像分割

Abstract: Vascular tissue and bone tissue cannot be clearly separated based solely on grayscale information in images of computed tomography angiography (CTA). The algorithm based on the growth of three-dimensional (3D) region improved bone tissue outside the bone contour extraction and the extraction algorithm based on improved Snake model combined with the characteristics of CTA grayscale images were proposed. Combining the knowledge of probability theory to improve the accuracy of determining condition of the region growing, fast skeletal regional seed extraction method of 3D region growing was proposed. It made it possible to obtain more accurate bone tissue area. After the Snake model was selected and some improvements were made to the model, energy image information items were increased, so that the model can better solve the current problems. Finally, the experimental results were given and compared with results from the traditional algorithm. It is confirmed the proposed segmentation of bone tissue extraction algorithm works well.

Key words: bone extraction, 3D segmentation, computed tomography angiography (CTA), region growth, medical image segmentation

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