北京生物醫(yī)學(xué)工程

___________圖論在腦腫瘤分割及提取中的應(yīng)用研究_________

Application of graph theory in brain tumor segmentation and extraction

作者：李鵬吳水才高宏建盛磊

單位：           北京工業(yè)大學(xué)生命科學(xué)與生物工程學(xué)院(北京100124)    

關(guān)鍵詞：圖論；腦腫瘤；圖像分割

分類號：

出版年·卷·期（頁碼）：2013·32·3（243-247）

摘要：

目的基于Matlab和VC++混合編程，實現(xiàn)了圖論在腦腫瘤分割及提取中的應(yīng)用，為之后腦腫瘤三維重建提供準確的分割結(jié)果。方法在Matlab和VC++開發(fā)平臺下，首先讀取含腦腫瘤的MRI圖像，經(jīng)過一定的預(yù)處理后，調(diào)用C++編寫的圖論分割函數(shù)，實現(xiàn)MRI圖像的全局分割，然后通過腫瘤區(qū)域的顏色信息進行區(qū)域二值化和輪廓提取等后處理，很好地完成了腦腫瘤的分割提取。結(jié)果通過與專家手動分割的腦腫瘤區(qū)域進行比較以及對算法各模塊運行時間的監(jiān)測，顯示腦腫瘤分割準確度高，且算法運行穩(wěn)定。結(jié)論基于圖論的分割算法能夠反映圖像全局特性，且運行穩(wěn)定，是一種值得推廣的腦腫瘤分割方法。

Objective Based on Matlab and VC++mixed programming，this paper realizes the application of graph theory in the brain tumor segmentation and extraction，providing accurate segmentation results for subsequent brain tumor three-dimensional reconstruction. Methods On Matlab and VC++development platform，the MR images with brain tumors are read firstly，after certain preprocessing，the graph theory segmentation functions written in C++are called to realize the global segmentation of MR images. Then some postprocessing including region binarization and contour extraction according to color information of tumor regions are done to complete the brain tumor segmentation and extraction. Results Compared with the manual segmentation of brain tumor region by expert，and with the monitoring on the running time of each module in the algorithm，the results are highly accurate in brain tumor segmentation and the segmentation algorithm runs stably. Conclusions The image segmentation algorithm based on graph theory reflects the global image properties，runs stably，and is worthy of popularization in brain tumor segmentation.

參考文獻：

［1］黃峰茜. 腦部腫瘤圖像分割技術(shù)的研究［D］. 南京：南京航空航天大學(xué)，2008.

Huang Fengqian. Research on division technology of brain tumor image［D］. Nanjing：Nanjing University of Aeronautics and Astronautics，2008.

［2］Felzenszwalb PE，Huttenlocher DP. Efficient Graph-Based Image Segmentation ［J］. International Journal of Computer Vision， 2004，59(2)：167-181.

［3］李素瑩. 基于圖論的乳腺腫瘤超聲圖像的分割和識別方法［D］. 廣州：華南理工大學(xué)，2011.

Li Suying. A graph-based segmentation and recognition method for breast tumors in ultrasound images［D］. Guangzhou：South China University of Technology，2011.

［4］艾海明，吳水才，高宏建，等. 基于圖論的肝腫瘤CT圖像自動分割方法［J］. 北京工業(yè)大學(xué)學(xué)報， 2010，36 (4)：572-576.

Ai Haiming，Wu Shuicai，Gao Hongjian，et al. Graph-based method for liver tumor CT image auto-segmentation［J］. Journal of Beijing University of Technology，2010，36(4)：572-576.

［5］黃茜，楊文亮，顧杰峰. 一種改進的基于圖論的圖像分割方法［J］. 科學(xué)技術(shù)與工程，2009，9 (13)：3652-3656.

Huang Qian，Yang Wenliang，Gu Jiefeng. Improved image segmentation method based on graph theory［J］. Science Technology and Engineering，2009，9(13)：3652-3656.

［6］Deepthi Narayan，Srikanta MK，Kumar GH. Image Segmentation Based on Graph Theoretical Approach to Improve the Quality of Image Segmentation ［C］. World Academy of Science，Engineering and Technology， 2008，（42）：35-38.

［7］Ming Zhang，Reda Alhajj. Improving the Graph-Based Image Segmentation Method［C］. Proceedings of the 18th IEEE international Conference on Tools with Artificial Intelligence，2006：617-624.

［8］李偉，陳武凡. 基于圖論的交互式腦部腫瘤MRI自動三維分割［J］. 南方醫(yī)科大學(xué)學(xué)報，2009，29 (1)：140-143.

Li Wei，Chen Wufan. Graph-based interactive three-dimensional segmentation of magnetic resonance images of brain tumors ［J］. Journal South Medical University， 2009，29(1)：140-143.

［9］田麗霞. 基于圖論的復(fù)雜腦網(wǎng)絡(luò)分析［J］.北京生物醫(yī)學(xué)工程，2010,29(1):96-100.

Tian Lixia. Analysis of complex brain networks based on graph theory［J］.Beijing Biomedical Engineering，2010,29(1):96-100.

［10］王海英. 圖論算法及其MATLAB實現(xiàn)［M］. 北京：北京航空航天大學(xué)出版社，2010：1-2.

Wang Haiying. Graph theory algorithm and MATLAB realization［M］. Beijing：Beijng University of Aeronautics and Astronautics Press，2010：1-2.

［11］譚志明. 基于圖論的圖像分割及其嵌入式應(yīng)用研究［D］. 上海：上海交通大學(xué)圖像通信與信息處理研究所，2008.

Tan Zhiming. Research on graph theory based image segmentation and its embedded application［D］. Shanghai：Institute of Image Communication & Information Processing，Shanghai Jiao Tong University，2008.

服務(wù)與反饋：

【文章下載】【加入收藏】

提示：您還未登錄，請登錄！點此登錄

51黑料吃瓜在线观看,51黑料官网|51黑料捷克街头搭讪_51黑料入口最新视频