电子测量与仪器学报

王霞,王光磊,李艳,王洪瑞.改进的基于特征融合 MOSSE 冠脉目标追踪算法[J].电子测量与仪器学报,2021,35(9):112-118

改进的基于特征融合 MOSSE 冠脉目标追踪算法

Improved MOSSE coronary target tracking algorithm based on feature fusion

DOI：

英文关键词:feature-fusion MOSSE coronary artery target tracking CTA

基金项目:国家自然科学基金(61473112)、河北省自然科学重点基金(F2017201222)、河北省自然科学基金(F2015201196)项目资助

作者	单位
王霞	1.河北大学电子信息工程学院
王光磊	1.河北大学电子信息工程学院
李艳	1.河北大学电子信息工程学院
王洪瑞	1.河北大学电子信息工程学院

Author	Institution
Wang Xia	1.College of Electronic and Information Engineering, Hebei University
Wang Guanglei	1.College of Electronic and Information Engineering, Hebei University
Li Yan	1.College of Electronic and Information Engineering, Hebei University
Wang Hongrui	1.College of Electronic and Information Engineering, Hebei University

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中文摘要:

CT 血管造影(computed tomography angiography,CTA)作为一种无创、检测精确较高的辅助诊断方法,尚急需能有效消除冠脉目标附近干扰噪声并寻求可全自动快速准确追踪目标的新算法以大幅减轻医生阅片压力、辅助其进行可靠诊断与治疗。提出了一种特征融合的误差最小平方和(minimum output sum of squared error,MOSSE)冠脉目标追踪新算法,通过提取冠脉血管多个特征,将其融合加入现有的 MOSSE 追踪方法,实现全自动准确快速追踪冠脉目标。使用河北大学附属医院 9 位患者(5 男 4 女,均龄 65 岁,其中 6 位有冠心病史)的 CTA 数据进行了算法验证,并与文献已报道基于中心线提取和基于区域生长的现有冠脉目标提取算法进行了处理结果对比分析。结果表明,新算法处理追踪一例患者切片数据仅需耗时 0. 02 s,多个病例的平均准确度达 94. 30%,性能优于上述现有冠脉目标提取算法,能实现全自动准确高效追踪到形态变化剧烈的冠脉目标,可为冠心病的临床诊治起到更为高效的辅助作用。

英文摘要:

Computed tomography angiography(CTA), as a non-invasive detection with higher accuracy auxiliary diagnostic method, now is urgently needed to effectively eliminate the interference noise near the coronary artery target and to find a new algorithm that can fully automatic, fast and accurate tracking the target, so as to greatly reduce the pressure on doctors to read the film and assist them in reliable diagnosis and treatment. A new minimum output sum of squared error (MOSSE) algorithm was proposed to achieve automatic accurate and fast tracking of coronary targets by extracting multiple features of coronary arteries and incorporating them into the existing MOSSE tracking method. CTA data from 9 patients (5 males and 4 females, average age 65, 6 with atherosclerosis) in Affiliated Hospital of Hebei University were used to verify the algorithm, and the results were compared with existing coronary target extraction algorithms based on centerline and regional growth. Results show that the new algorithm processing track patient frame data only takes 0. 02 s, the average accuracy of multiple cases was 94. 30%, and the performance is better than the existing coronary target extraction algorithm, it realizes automatic accurate efficient tracking to form severe coronary target change, and provides more efficient assistance to the clinical diagnosis and treatment of coronary heart disease (CHD).

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