CT圖像肺腫瘤分割系統(tǒng)的設(shè)計與實現(xiàn)
發(fā)布時間：2018-07-29 06:51
【摘要】：眾所周知,癌癥已成為人類死亡的一大重要原因,而肺癌更是位列惡性腫瘤死亡原因之首。肺腫瘤的分割是計算機輔助診斷的重要組成部分。目前,臨床上主要通過醫(yī)生手工勾勒的方式對肺腫瘤進行分割,然而這種方式不僅效率低下,且具有一定的主觀性。近年來,人們對肺腫瘤的分割進行了大量研究,由于腫瘤的多樣性,使得現(xiàn)階段仍然不能使用一種算法將所有類型的肺腫瘤準確、自動地分割出來。因此,本文通過對肺腫瘤分割算法的研究,利用VTK和Qt工具包設(shè)計并實現(xiàn)了一個完整的肺腫瘤分割系統(tǒng)。本文主要工作內(nèi)容如下:(1)采用邊界約束的區(qū)域生長算法對肺腫瘤進行交互分割,實現(xiàn)了粘連型肺腫瘤的二維分割,解決了區(qū)域生長算法的過分割問題,通過大量的分割實驗與醫(yī)生手工分割結(jié)果進行對比,驗證了分割結(jié)果的準確性。(2)根據(jù)肺腫瘤分割系統(tǒng)的實際需求,將系統(tǒng)分為讀入保存模塊、顯示模塊、交互模塊和分割模塊。其中讀入保存模塊通過VTK接口實現(xiàn)了圖像數(shù)據(jù)的統(tǒng)一管理;顯示模塊在實現(xiàn)圖像二維及三維可視化的同時,還實現(xiàn)了縮放等交互顯示功能;交互事件響應(yīng)模塊實現(xiàn)了系統(tǒng)外部事件及內(nèi)部事件的檢測與響應(yīng);分割模塊通過人機交互實現(xiàn)了肺腫瘤的二維分割和孤立型肺腫瘤的三維分割及三維重建。(3)在系統(tǒng)中通過VOI的提取和多種子點的選擇,解決了區(qū)域生長算法在三維CT圖像的數(shù)據(jù)量較大時速度過慢的問題,實現(xiàn)了孤立型肺腫瘤的三維分割和三維重建,通過等值面閾值調(diào)節(jié)工具給用戶提供了最佳的三維重建顯示效果。本文最后完成了系統(tǒng)的功能測試,并使用系統(tǒng)進行分割實驗。結(jié)果表明,本文所設(shè)計的系統(tǒng)可以達到預(yù)期的設(shè)計目標(biāo),不僅對肺腫瘤具有良好的分割效率,而且有一定的應(yīng)用價值。
[Abstract]:As we all know, cancer has become an important cause of human death, and lung cancer is the leading cause of death from malignant tumors. Lung tumor segmentation is an important part of computer aided diagnosis. At present, lung tumors are segmented by doctors' manual drawing. However, this method is not only inefficient, but also subjective. In recent years, people have done a lot of research on the segmentation of lung tumors. Due to the diversity of tumors, it is still not possible to use an algorithm to segment all types of lung tumors accurately and automatically. Therefore, through the research of lung tumor segmentation algorithm, a complete lung tumor segmentation system is designed and implemented by using VTK and QT toolkits. The main work of this paper is as follows: (1) the boundary constrained region growth algorithm is used to segment lung tumor interactively. The two-dimensional segmentation of adherent lung tumor is realized, and the over-segmentation problem of region growth algorithm is solved. The accuracy of the segmentation results is verified by comparing a large number of segmentation experiments with manual segmentation results by doctors. (2) according to the actual needs of lung tumor segmentation system, the system is divided into read and save module, display module, interactive module and segmentation module. The reading and saving module realizes the unified management of image data through the VTK interface, and the display module realizes the interactive display function such as zooming while realizing the visualization of 2D and 3D images. The interactive event response module realizes the detection and response of the external and internal events of the system. The segmentation module realizes the two-dimensional segmentation of lung tumor and three-dimensional reconstruction of solitary lung tumor through human-computer interaction. (3) extraction of VOI and selection of multiple seed points in the system. It solves the problem that the region growth algorithm is too slow when the data of 3D CT image is large, and realizes the 3D segmentation and reconstruction of solitary lung tumor. The optimal display effect of 3D reconstruction is provided by the equal-surface threshold adjustment tool. Finally, the function test of the system is completed, and the system is used for the segmentation experiment. The results show that the system designed in this paper can achieve the desired design goal, not only has a good segmentation efficiency for lung tumors, but also has certain application value.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R734.2;TP391.41
本文編號：2151875
[Abstract]:As we all know, cancer has become an important cause of human death, and lung cancer is the leading cause of death from malignant tumors. Lung tumor segmentation is an important part of computer aided diagnosis. At present, lung tumors are segmented by doctors' manual drawing. However, this method is not only inefficient, but also subjective. In recent years, people have done a lot of research on the segmentation of lung tumors. Due to the diversity of tumors, it is still not possible to use an algorithm to segment all types of lung tumors accurately and automatically. Therefore, through the research of lung tumor segmentation algorithm, a complete lung tumor segmentation system is designed and implemented by using VTK and QT toolkits. The main work of this paper is as follows: (1) the boundary constrained region growth algorithm is used to segment lung tumor interactively. The two-dimensional segmentation of adherent lung tumor is realized, and the over-segmentation problem of region growth algorithm is solved. The accuracy of the segmentation results is verified by comparing a large number of segmentation experiments with manual segmentation results by doctors. (2) according to the actual needs of lung tumor segmentation system, the system is divided into read and save module, display module, interactive module and segmentation module. The reading and saving module realizes the unified management of image data through the VTK interface, and the display module realizes the interactive display function such as zooming while realizing the visualization of 2D and 3D images. The interactive event response module realizes the detection and response of the external and internal events of the system. The segmentation module realizes the two-dimensional segmentation of lung tumor and three-dimensional reconstruction of solitary lung tumor through human-computer interaction. (3) extraction of VOI and selection of multiple seed points in the system. It solves the problem that the region growth algorithm is too slow when the data of 3D CT image is large, and realizes the 3D segmentation and reconstruction of solitary lung tumor. The optimal display effect of 3D reconstruction is provided by the equal-surface threshold adjustment tool. Finally, the function test of the system is completed, and the system is used for the segmentation experiment. The results show that the system designed in this paper can achieve the desired design goal, not only has a good segmentation efficiency for lung tumors, but also has certain application value.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R734.2;TP391.41
【參考文獻】
相關(guān)期刊論文 前8條
1 江貴平;秦文健;周壽軍;王昌淼;;醫(yī)學(xué)圖像分割及其發(fā)展現(xiàn)狀[J];計算機學(xué)報;2015年06期
2 韓成虎;韓成龍;丘文峰;;CT圖像三維重建系統(tǒng)的設(shè)計與實現(xiàn)[J];現(xiàn)代計算機(專業(yè)版);2013年02期
3 蘭紅;張璐;;分水嶺優(yōu)化的Snake模型肝臟圖像分割[J];中國圖象圖形學(xué)報;2012年07期
4 張婧;李彬;田聯(lián)房;陳萍;;應(yīng)用規(guī)則的肺結(jié)節(jié)識別系統(tǒng)[J];計算機工程與應(yīng)用;2011年17期
5 劉淑琴;;肺癌影像醫(yī)學(xué)診斷進展[J];河北醫(yī)藥;2008年06期
6 袁杲;楊玲;;Observer與Command模式在VTK類庫設(shè)計中的應(yīng)用研究[J];西南民族大學(xué)學(xué)報(自然科學(xué)版);2007年04期
7 魏娜,王玨,劉明宇;基于Visualization Toolkit的腦模型三維重建方法研究[J];中國康復(fù)理論與實踐;2005年03期
8 劉懷軍,楊樺,馮平勇,李淑芝,王永生;窗口技術(shù)在CT診斷中的價值[J];實用放射學(xué)雜志;1992年02期
相關(guān)碩士學(xué)位論文 前1條
1 宋海友;基于VTK的醫(yī)學(xué)圖像三維重建及其可視化技術(shù)研究[D];成都理工大學(xué);2006年
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