Head and Neck Tumor Segmentation Based on Augmented Gradient Level Set Method_Journal of Biomedical Engineering

Authors：

ZHANGQiongmin ¹ ,  ZHANGJing ¹ , WANGMintang ¹ , HELing ¹ , MENYi ² , WEIJun ¹ , HUANGHua ¹

1. Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China;
2. Head and Neck Tumor Surgery, Hospital of Stomatology, Sichuan University, Chengdu 610041, China;

Corresponding author：

ZHANGJing, Email: jing_zhang@scu.edu.cn

Keywords：

head and neck tumor segmentation; tumor volume measurement; level set; edge indicator function; computer-aided diagnosis

DOI：

10.7507/1001-5515.20150158

Video：

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To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume. In large volume tumor segmentation, the proposed level set method can reduce manual intervention and enhance the segmentation accuracy. Tumor volume calculation results are close to the gold standard. From the experiment results, the augmented gradient based level set method has achieved accurate head and neck tumor segmentation. It can provide useful information to computer aided diagnosis.

Citation： ZHANGQiongmin, ZHANGJing, WANGMintang, HELing, MENYi, WEIJun, HUANGHua. Head and Neck Tumor Segmentation Based on Augmented Gradient Level Set Method. Journal of Biomedical Engineering, 2015, 32(4): 887-891,904. doi: 10.7507/1001-5515.20150158 Copy

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11.	何传江,田巧玉.几何活动轮廓模型中停止速度函数的尺度变换[J].计算机工程与应用,2008,44(8):82-84.
12.	王辉,王来生,钟萍. 基于边缘检测函数尺度变换的水平集图像分割. 计算机工程[J],2009,35(24):202-204.
13.	LIU J, UDUPA J K, SAHA P K, et al. Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis[J]. Medical Physics, 2008, 35(8):3637-3649.
14.	UDUPA J K, LEBLANC V R, ZHUGE Y, et al. A framework for evaluating image segmentation algorithms[J]. Comput Med Imaging Graph, 2006, 30(2):75-87.
15.	高玮玮,沈建新,王玉亮. 眼底图像中硬性渗出自动检测方法的对比[J]. 南京航空航天大学学报,2013,45(1):55-61.
16.	Mayr N A, Taoka T, Yuh W T, et al. Method and timing of tumor volume measurement for outcome prediction in cervical cancer using magnetic resonance imaging[J]. Int J Radiat Oncol Biol Phys, 2002, 52(1):14-22.
17.	Chong V F, Zhou J, Khoo J B, et al. Tongue carcinoma:tumor volume measurement[J]. Int J Radiation Oncol Biol Phys, 2004, 59(1):59-66.

1. SRIDHAR P,MERCIER G,TAN J,et al.FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors[J].Am J Roentgenol,2014,202(5):1114-1119.
2. 葛莹,董越,张丽芝,等.颌面部肌上皮瘤的MSCT诊断及文献复习[J].中国医学影像技术,2012,28(1):69-72.
3. 常亮,王长福,王玉强,等.多层螺旋CT增强扫描诊断口底恶性肿瘤[J].中国医学影像技术,2011,27(6):1134-1136.
4. 李玲,余后强.基于CT图像的肝癌计算机辅助诊断[J].甘肃科学学报,2012,(1):60-64.
5. EL-BAZ A,BEACHE G M,GIMEL’FARB G,et al.Computer-aided diagnosis systems for lung cancer:challenges and methodologies[EB/OL][2012-12-20].http://dx.doi.org/10.1155/2013/942353.
6. EI-DAHSHAN E S A,MOHSEN H M,REVET K,et al.Computer-aided diagnosis of human brain tumor through MRI:A survey and a new algorithm[J].Expert Systems with Applications,2014,41(11):5526-5545.
7. 屠规益,徐震纲,刘绍严.头颈肿瘤学发展中的是与非(一)[J].医学争鸣,2011(3):17-21.
8. 李传龙.基于水平集和模糊聚类方法的图像分割技术研究[D].大连:大连海事大学,2012.
9. LI C,XU C,GUI C,et al.Level set evolution without re-initialization:a new variational formulation[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington DC,USA:2005:430-436.
10. LI C,XU C,GUI C,et al.Distance regularized level set evolution and its application to image segmentation[J].IEEE Transactions on Image Processing,2010,19(12):3243-3254.
11. 何传江,田巧玉.几何活动轮廓模型中停止速度函数的尺度变换[J].计算机工程与应用,2008,44(8):82-84.
12. 王辉,王来生,钟萍. 基于边缘检测函数尺度变换的水平集图像分割. 计算机工程[J],2009,35(24):202-204.
13. LIU J, UDUPA J K, SAHA P K, et al. Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis[J]. Medical Physics, 2008, 35(8):3637-3649.
14. UDUPA J K, LEBLANC V R, ZHUGE Y, et al. A framework for evaluating image segmentation algorithms[J]. Comput Med Imaging Graph, 2006, 30(2):75-87.
15. 高玮玮,沈建新,王玉亮. 眼底图像中硬性渗出自动检测方法的对比[J]. 南京航空航天大学学报,2013,45(1):55-61.
16. Mayr N A, Taoka T, Yuh W T, et al. Method and timing of tumor volume measurement for outcome prediction in cervical cancer using magnetic resonance imaging[J]. Int J Radiat Oncol Biol Phys, 2002, 52(1):14-22.
17. Chong V F, Zhou J, Khoo J B, et al. Tongue carcinoma:tumor volume measurement[J]. Int J Radiation Oncol Biol Phys, 2004, 59(1):59-66.

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