肝臟血管中心線及分級數(shù)據(jù)的提取方法
發(fā)布時間：2018-11-23 20:28
【摘要】：隨著肝臟部分切除術(shù)特別是活體肝移植的發(fā)展,肝臟分段解剖在手術(shù)中越來越重要。中心線及分級數(shù)據(jù)作為血管的結(jié)構(gòu)化描述方法,不僅是肝臟分段的重要依據(jù),而且描述了血管的解剖位置和級數(shù),也體現(xiàn)了供血關(guān)系,因此準確提取肝臟血管的中心線及分級數(shù)據(jù)是肝臟臨床診斷和分析過程中一個重要環(huán)節(jié)。由于肝臟血管分支復(fù)雜,并且肝臟血管的分割結(jié)果不平滑,使用傳統(tǒng)方法提取血管中心線和分級數(shù)據(jù)提取方法時,存在錯誤分支多、血管分叉處像素冗余和中心線不連續(xù)等缺陷。在提取中心線和分級數(shù)據(jù)過程中通過剪枝策略去除細小分支,并且引入一種冗余像素判斷方法去除中心線中的多余像素就能提高傳統(tǒng)方法的魯棒性,最終提取準確的血管中心線和分級數(shù)據(jù)。中心線提取算法的基礎(chǔ)是快速步進法(FMM),計算過程中根據(jù)FMM速度函數(shù)的不同,計算不同速度的場。首先設(shè)定FMM速度函數(shù)為常函數(shù),計算血管中每一個像素到邊緣的最短距離,獲得低速場,自動選取距離場中值最大的像素作為血管源點Ps。然后設(shè)定FMM的速度函數(shù)是以低速場值為自變量的開方函數(shù),計算血管每一個像素點到源點Ps的最短距離,獲得中速場,將中速場取整分塊,通過寬度優(yōu)先遍歷的方法獲得血管端點。最后設(shè)定FMM的速度函數(shù)是以距離場值為自變量的線性函數(shù),計算血管每一個像素到源點Ps的最短距離,獲得高速場,在高速場中利用梯度下降法從血管端點回溯到源點Ps,獲得血管中心線。在提取分級數(shù)據(jù)的過程中,首先根據(jù)中心線上點的幾何特征,標記出分叉點,然后從血管主干點開始遍歷中心線,構(gòu)建血管結(jié)構(gòu)字典樹,為了準確表示血管的結(jié)構(gòu)特征,利用修改局部點的方法去除字典樹中的噪聲節(jié)點,合并相鄰的分支節(jié)點,最后根據(jù)血管特征提取準確的分級數(shù)據(jù)。實驗證明,對于大部分病例數(shù)據(jù),以上可以快速、有效地提取血管中心線和分級數(shù)據(jù)。
[Abstract]:With the development of partial hepatectomy, especially in vivo liver transplantation, segmental anatomy of liver is becoming more and more important. Centerline and grading data are not only the important basis of liver segmentation, but also describe the anatomical position and progression of blood vessels, and reflect the relationship between blood supply and blood supply. Therefore, accurate extraction of liver blood vessel centerline and grading data is an important link in the process of liver clinical diagnosis and analysis. Because the branches of hepatic blood vessels are complex and the segmentation results of hepatic blood vessels are not smooth, there are many wrong branches when traditional methods are used to extract blood vessel centerline and classification data. Defects such as pixel redundancy and discontinuity of the center line at the vascular bifurcation. In the process of extracting centerline and grading data, the small branches are removed by pruning strategy, and the robustness of traditional methods is improved by introducing a redundant pixel judgment method to remove redundant pixels from the centerline. Finally, accurate blood vessel centerline and grading data were extracted. The basis of centerline extraction algorithm is to calculate the field of different velocities according to the difference of FMM velocity function in the process of (FMM), calculation by fast step method. First, the FMM velocity function is set as a constant function, the shortest distance from each pixel to the edge of the vessel is calculated, and the low velocity field is obtained. The pixel with the largest median distance field is automatically selected as the vascular source point Ps.. Then the velocity function of FMM is an open-square function with the low velocity field value as the independent variable. The shortest distance from each pixel point to the source point Ps is calculated, the medium velocity field is obtained, and the intermediate velocity field is divided into blocks. The vascular endpoints are obtained by width-first traversal. Finally, the velocity function of FMM is a linear function with the distance field value as the independent variable. The shortest distance from each pixel to the source point Ps is calculated, and the high-speed field is obtained. In the high-speed field, the gradient descent method is used to trace back to the source point Ps, from the vascular endpoint. The central line of the blood vessel was obtained. In the process of extracting the grading data, the bifurcation points are first marked according to the geometric characteristics of the points on the center line, and then the center line is traversed from the main point of the blood vessel to construct a dictionary tree of the blood vessel structure, in order to accurately represent the structural characteristics of the blood vessel. The noise nodes in the dictionary tree are removed by modifying the local points, the adjacent branch nodes are merged, and the accurate classification data are extracted according to the vascular features. Experimental results show that the above method can extract the blood vessel centerline and grading data quickly and effectively for most case data.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R657.3;TP391.41
本文編號：2352603
[Abstract]:With the development of partial hepatectomy, especially in vivo liver transplantation, segmental anatomy of liver is becoming more and more important. Centerline and grading data are not only the important basis of liver segmentation, but also describe the anatomical position and progression of blood vessels, and reflect the relationship between blood supply and blood supply. Therefore, accurate extraction of liver blood vessel centerline and grading data is an important link in the process of liver clinical diagnosis and analysis. Because the branches of hepatic blood vessels are complex and the segmentation results of hepatic blood vessels are not smooth, there are many wrong branches when traditional methods are used to extract blood vessel centerline and classification data. Defects such as pixel redundancy and discontinuity of the center line at the vascular bifurcation. In the process of extracting centerline and grading data, the small branches are removed by pruning strategy, and the robustness of traditional methods is improved by introducing a redundant pixel judgment method to remove redundant pixels from the centerline. Finally, accurate blood vessel centerline and grading data were extracted. The basis of centerline extraction algorithm is to calculate the field of different velocities according to the difference of FMM velocity function in the process of (FMM), calculation by fast step method. First, the FMM velocity function is set as a constant function, the shortest distance from each pixel to the edge of the vessel is calculated, and the low velocity field is obtained. The pixel with the largest median distance field is automatically selected as the vascular source point Ps.. Then the velocity function of FMM is an open-square function with the low velocity field value as the independent variable. The shortest distance from each pixel point to the source point Ps is calculated, the medium velocity field is obtained, and the intermediate velocity field is divided into blocks. The vascular endpoints are obtained by width-first traversal. Finally, the velocity function of FMM is a linear function with the distance field value as the independent variable. The shortest distance from each pixel to the source point Ps is calculated, and the high-speed field is obtained. In the high-speed field, the gradient descent method is used to trace back to the source point Ps, from the vascular endpoint. The central line of the blood vessel was obtained. In the process of extracting the grading data, the bifurcation points are first marked according to the geometric characteristics of the points on the center line, and then the center line is traversed from the main point of the blood vessel to construct a dictionary tree of the blood vessel structure, in order to accurately represent the structural characteristics of the blood vessel. The noise nodes in the dictionary tree are removed by modifying the local points, the adjacent branch nodes are merged, and the accurate classification data are extracted according to the vascular features. Experimental results show that the above method can extract the blood vessel centerline and grading data quickly and effectively for most case data.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R657.3;TP391.41
【參考文獻】
相關(guān)期刊論文 前9條
1 郭云霞;溫海瑞;;Eikonal型方程粘性解的表達式[J];應(yīng)用泛函分析學(xué)報;2014年02期
2 沈柏用;施源;;肝臟分段解剖的新認識[J];世界華人消化雜志;2008年09期
3 郭惠;符紅光;羅東輝;;三維動態(tài)幾何中直線消隱的實現(xiàn)[J];計算機應(yīng)用;2007年03期
4 朱貴冬;沈理;;利用等值線跟蹤的快速步進法[J];計算機輔助設(shè)計與圖形學(xué)學(xué)報;2006年12期
5 柳稼航,楊建峰,單新建,尹京苑;一種基于優(yōu)先搜索方向的邊界跟蹤算法[J];遙感技術(shù)與應(yīng)用;2004年03期
6 朱付平,田捷,林瑤,葛行飛;基于Level Set方法的醫(yī)學(xué)圖像分割[J];軟件學(xué)報;2002年09期
7 趙明旺;基于遺傳算法和最速下降法的函數(shù)優(yōu)化混合數(shù)值算法[J];系統(tǒng)工程理論與實踐;1997年07期
8 滑炎卿，，張國楨，葛云明，鄭海寧;肝八段分區(qū)的CT與斷面解剖對照研究[J];臨床醫(yī)學(xué)影像雜志;1996年04期
9 董家鴻;黃志強;;精準肝切除——21世紀肝臟外科新理念[J];中華外科雜志;2009年21期
相關(guān)碩士學(xué)位論文 前4條
1 王鵬;基于CT圖像的肝臟血管樹三維拓撲模型的構(gòu)建及應(yīng)用[D];重慶大學(xué);2013年
2 董娜娜;提取血管與血管中心線的算法研究[D];長春工業(yè)大學(xué);2012年
3 高耀宗;全自動肝臟門靜脈分割算法的研究與實現(xiàn)[D];浙江大學(xué);2011年
4 劉志遠;基于VTK的CT圖像三維可視化方法研究[D];山東科技大學(xué);2009年
本文編號：2352603
本文鏈接：http://sikaile.net/yixuelunwen/waikelunwen/2352603.html
最近更新
教材專著
