A PET-CT Lung Tumor Segmentation Method Based on Active Contour Model

Jingjing ZONG; Tianshuang QIU; Guangwen ZHU

doi:10.11999/JEIT200891

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3496-3504

Jingjing ZONG, Tianshuang QIU, Guangwen ZHU. A PET-CT Lung Tumor Segmentation Method Based on Active Contour Model[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3496-3504. doi: 10.11999/JEIT200891

Citation:

Jingjing ZONG, Tianshuang QIU, Guangwen ZHU. A PET-CT Lung Tumor Segmentation Method Based on Active Contour Model[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3496-3504. doi: 10.11999/JEIT200891

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A PET-CT Lung Tumor Segmentation Method Based on Active Contour Model

doi: 10.11999/JEIT200891

1.
School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian 116028, China
2.
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
3.
Department of Nuclear Medicine, First Affiliated Hospital, Dalian Medical University, Dalian 116011, China

Funds: The National Natural Science Foundation of China (61671105), The Scientific Research Project of Department of Education, Liaoning Province (JDL2020029)

Received Date: 2020-10-16
Rev Recd Date: 2021-09-21

Available Online: 2021-10-25

Publish Date: 2021-12-21

Abstract

Abstract

To solve the problem that the doctors' clinical experience is not fully integrated into the algorithm design in PET-CT lung tumor segmentation, a hybrid active contour model named RSF_ML based on variational level set is proposed by combining with the PET Gaussian distribution prior, Region Scalable Fitting (RSF) model and Maximum Likelihood ratio Classification (MLC) criterion. Furthermore, referring to the important value of fusion image in the process of lung tumor manual delineation, a segmentation method for PET-CT lung tumor fusion image based on RSF_ML is proposed. Experiments show that the proposed method can achieve accurate segmentation of representative Non-Small Cell Lung Cancer (NSCLC), and the subjective and objective results are better than the comparison method, which can provide effective computer-aided segmentation results for clinic.
- Active contour model,
- Lung tumor segmentation,
- Variational level set,
- Maximum Likelihood ratio Classification (MLC)

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References(17)

References

[1]	COSMA L, SOLLAKU S, FRANTELLIZZI V, et al. Early ¹⁸F-FDG PET/CT in COVID-19[J]. Journal of Medical Imaging and Radiation Oncology, 2020, 64(5): 671–673. doi: 10.1111/1754-9485.13099
[2]	SONG Yang, CAI Weidong, and FENG D D. Global context inference for adaptive abnormality detection in PET-CT images[C]. Proceedings of the 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2012: 482–485. doi: 10.1109/ISBI.2012.6235589.
[3]	DONG Yunyun, YANG Wenkai, WANG Jiawen, et al. An improved supervoxel 3D region growing method based on PET/CT multimodal data for segmentation and reconstruction of GGNs[J]. Multimedia Tools and Applications, 2020, 79(3): 2309–2338. doi: 10.1007/s11042-019-08250-4
[4]	JU Wei, XIANG Deihui, ZHANG Bin, et al. Random walk and graph cut for Co-segmentation of lung tumor on PET-CT images[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5854–5867. doi: 10.1109/TIP.2015.2488902
[5]	LIAN Chunfeng, RUAN Su, DENŒUX T, et al. Joint tumor segmentation in PET-CT images using co-clustering and fusion based on belief functions[J]. IEEE Transactions on Image Processing, 2019, 28(2): 755–766. doi: 10.1109/TIP.2018.2872908
[6]	LI Laquan, LU Wei, TAN Yihua, et al. Variational PET/CT tumor co-segmentation integrated with PET restoration[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2020, 4(1): 37–49. doi: 10.1109/TRPMS.2019.2911597
[7]	FOSTER B, BAĞCI U, MANSOOR A, et al. A review on segmentation of positron emission tomography images[J]. Computers in Biology and Medicine, 2014, 50: 76–96. doi: 10.1016/j.compbiomed.2014.04.014
[8]	LI Laquan, ZHAO Xiangming, LU Wei, et al. Deep learning for variational multimodality tumor segmentation in PET/CT[J]. Neurocomputing, 2020, 392: 277–295. doi: 10.1016/j.neucom.2018.10.099
[9]	ZHAO Xiangming, LI Laquan, LU Wei, et al. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network[J]. Physics in Medicine & Biology, 2019, 64(1): 015011. doi: 10.1088/1361-6560/aaf44b
[10]	叶锋, 李婉茹, 陈家祯, 等. 基于显著性区域检测和水平集的图像快速分割算法[J]. 电子与信息学报, 2017, 39(11): 2661–2668. doi: 10.11999/JEIT170214 YE Feng, LI Wanru, CHEN Jiazhen, et al. Image fast segmentation algorithm based on saliency region detection and level set[J]. Journal of Electronics &Information Technology, 2017, 39(11): 2661–2668. doi: 10.11999/JEIT170214
[11]	SONG Yangyang, PENG Guohua, SUN Dongwei, et al. Active contours driven by Gaussian function and adaptive-scale local correntropy-based K-means clustering for fast image segmentation[J]. Signal Processing, 2020, 174: 107625. doi: 10.1016/j.sigpro.2020.107625
[12]	JIN Ri and WENG Guirong. Active contour model based on improved fuzzy c-means algorithm and adaptive functions[J]. Computers & Mathematics with Applications, 2019, 78(11): 3678–3691. doi: 10.1016/j.camwa.2019.06.010
[13]	GUO Lu, SHEN Shuming, HARRIS E, et al. A tri-modality image fusion method for target delineation of brain tumors in radiotherapy[J]. PLoS One, 2014, 9(11): e112187. doi: 10.1371/journal.pone.0112187
[14]	BALLANGAN C, WANG Xiuying, FULHAM M, et al. Lung tumor delineation in PET-CT images using a downhill region growing and a Gaussian mixture model[C]. Proceedings of the 18th IEEE International Conference on Image Processing, Brussels, Belgium, 2011: 2173–2176. doi: 10.1109/ICIP.2011.6116042.
[15]	LI Chunming, KAO C Y, GORE J C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Transactions on Image Processing, 2008, 17(10): 1940–1949. doi: 10.1109/TIP.2008.2002304
[16]	ZONG Jingjing, QIU Tianshuang, LI Weidong, et al. Automatic ultrasound image segmentation based on local entropy and active contour model[J]. Computers & Mathematics with Applications, 2019, 78(3): 929–943. doi: 10.1016/j.camwa.2019.03.022
[17]	DING Keyan, XIAO Linfang, and WENG Guirong. Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation[J]. Signal Processing, 2017, 134: 224–233. doi: 10.1016/j.sigpro.2016.12.021