冠狀動脈CTA圖像的序列分割算法
發(fā)布時間：2018-07-21 18:37
【摘要】：隨著經(jīng)濟不斷發(fā)展,人們的生活方式發(fā)生了巨大變化。心腦血管疾病由于高患病率和高死亡率對人類健康構成嚴重威脅,每年因心腦血管疾病導致的死亡率居高不下。所以對于心血管疾病早期的診斷和預防顯得尤其重要。作為冠心病診斷的有效無創(chuàng)手段,多層螺旋CTA近些年發(fā)展迅速,而基于CTA圖像的血管分割可以準確提取冠脈輪廓,是冠狀動脈狹窄診斷的重要臨床輔助工具,并可以提供鈣化程度、斑塊負擔以及狹窄程度的定量分析,因此,成為醫(yī)學圖像處理領域研究的熱點。而針對冠脈CTA序列圖像的自動或半自動分割算法就有著重要的臨床意義和實際價值。本文針對冠脈血管的序列分割,研究工作主要分為一下三部分:第一,主動脈血管的序列分割。傳統(tǒng)的基于質(zhì)心的方式雖然能夠?qū)崿F(xiàn)序列分割,卻無法解決分裂的問題;本文提出的基于ISODATA和區(qū)域生長的序列分割新算法,通過對主動脈的目標進行分割,然后對結果利用ISODATA算法進行聚類,將聚類后得到的目標區(qū)域的聚類中心作為下一幅CT圖像新的種子點再進行區(qū)域生長。該算法很好地解決了目標分裂的問題。第二,冠狀動脈血管的序列分割。冠狀動脈血管在CTA圖像中目標區(qū)域較小,結構復雜,使自動分割有一定難度。于是,本文提出了基于特征匹配的追蹤分割算法。通過對所有數(shù)據(jù)閾值化,然后利用區(qū)域特征對目標進行匹配,最后實現(xiàn)冠脈的序列分割。該算法能較好地適應小區(qū)域血管的識別與追蹤。第三,冠狀動脈序列分割算法的改進。針對第二部分冠脈序列分割算法中出現(xiàn)的遺漏冠脈細小血管的不足,提出了基于卡爾曼濾波的改進算法。改進主要包括兩部分:一是取代全局閾值的粗分割,在上一幀數(shù)據(jù)先驗知識的指導下計算最優(yōu)局部閾值。二是考慮到目標血管運動幅度過大導致不在ROI區(qū)域內(nèi)的問題,引入了卡爾曼濾波,以預測位置點作為ROI區(qū)域中心。改進算法明顯可以識別和追蹤到更多的冠脈細小血管,提高了追蹤精確度。
[Abstract]:With the development of economy, people's way of life has changed greatly. Cardio-cerebrovascular diseases (CVDs) pose a serious threat to human health due to high morbidity and mortality, and the mortality caused by cardiovascular and cerebrovascular diseases (CVDs) remains high every year. Therefore, early diagnosis and prevention of cardiovascular disease is particularly important. As an effective and noninvasive method for the diagnosis of coronary heart disease, multilayer spiral CTA has developed rapidly in recent years. The segmentation of coronary artery based on CTA image can accurately extract the coronary artery profile, and it is an important clinical assistant tool for the diagnosis of coronary artery stenosis. It can provide quantitative analysis of calcification degree, plaque burden and stenosis degree, so it has become a hot spot in the field of medical image processing. The automatic or semi-automatic segmentation algorithm for coronary CTA images has important clinical significance and practical value. In this paper, the sequence segmentation of coronary artery is divided into three parts: first, the sequence segmentation of aortic vessels. Although the traditional centroid method can achieve sequence segmentation, it can not solve the problem of fragmentation. A new algorithm based on ISODATA and region growth is proposed in this paper. Then the result is clustered by ISODATA algorithm, and the cluster center of the target region is used as the new seed point of the next CT image to grow the region. The algorithm solves the problem of target splitting well. Second, the sequence segmentation of coronary artery. The target area of coronary artery in CTA image is small and the structure is complex, which makes automatic segmentation difficult. Therefore, a tracking segmentation algorithm based on feature matching is proposed in this paper. By thresholding all the data and matching the target with regional features, the coronary artery segmentation is realized. The algorithm is suitable for small area vascular recognition and tracking. Third, the improvement of coronary artery sequence segmentation algorithm. In the second part, an improved algorithm based on Kalman filter is proposed to solve the problem of missing small coronary vessels in the segmentation algorithm of coronary artery sequence. The improvement mainly includes two parts: one is to replace the rough segmentation of global threshold and to calculate the optimal local threshold under the guidance of prior knowledge of the previous frame data. Secondly, considering the problem that the target vessel movement amplitude is too large, the Kalman filter is introduced to predict the location point as the center of ROI region. The improved algorithm can identify and track more small coronary vessels and improve the tracking accuracy.
【學位授予單位】：河北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R816.2;TP391.41
本文編號：2136463
[Abstract]:With the development of economy, people's way of life has changed greatly. Cardio-cerebrovascular diseases (CVDs) pose a serious threat to human health due to high morbidity and mortality, and the mortality caused by cardiovascular and cerebrovascular diseases (CVDs) remains high every year. Therefore, early diagnosis and prevention of cardiovascular disease is particularly important. As an effective and noninvasive method for the diagnosis of coronary heart disease, multilayer spiral CTA has developed rapidly in recent years. The segmentation of coronary artery based on CTA image can accurately extract the coronary artery profile, and it is an important clinical assistant tool for the diagnosis of coronary artery stenosis. It can provide quantitative analysis of calcification degree, plaque burden and stenosis degree, so it has become a hot spot in the field of medical image processing. The automatic or semi-automatic segmentation algorithm for coronary CTA images has important clinical significance and practical value. In this paper, the sequence segmentation of coronary artery is divided into three parts: first, the sequence segmentation of aortic vessels. Although the traditional centroid method can achieve sequence segmentation, it can not solve the problem of fragmentation. A new algorithm based on ISODATA and region growth is proposed in this paper. Then the result is clustered by ISODATA algorithm, and the cluster center of the target region is used as the new seed point of the next CT image to grow the region. The algorithm solves the problem of target splitting well. Second, the sequence segmentation of coronary artery. The target area of coronary artery in CTA image is small and the structure is complex, which makes automatic segmentation difficult. Therefore, a tracking segmentation algorithm based on feature matching is proposed in this paper. By thresholding all the data and matching the target with regional features, the coronary artery segmentation is realized. The algorithm is suitable for small area vascular recognition and tracking. Third, the improvement of coronary artery sequence segmentation algorithm. In the second part, an improved algorithm based on Kalman filter is proposed to solve the problem of missing small coronary vessels in the segmentation algorithm of coronary artery sequence. The improvement mainly includes two parts: one is to replace the rough segmentation of global threshold and to calculate the optimal local threshold under the guidance of prior knowledge of the previous frame data. Secondly, considering the problem that the target vessel movement amplitude is too large, the Kalman filter is introduced to predict the location point as the center of ROI region. The improved algorithm can identify and track more small coronary vessels and improve the tracking accuracy.
【學位授予單位】：河北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R816.2;TP391.41
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