基于随机游走算法的CT图像肺肿瘤分割

doi:10.3969/j.issn.1000-1565.2019.03.013

河北大学学报(自然科学版) ›› 2019, Vol. 39 ›› Issue (3): 311-315.DOI: 10.3969/j.issn.1000-1565.2019.03.013

基于随机游走算法的CT图像肺肿瘤分割

张欣¹,王洁¹,孔航²,温誉童²,刘雯²,王兵²

收稿日期:2018-10-13 出版日期:2019-05-25 发布日期:2019-05-25
通讯作者: 王兵(1966—),女,河北承德人,河北大学教授,主要从事模式识别、图像处理方向的研究.E-mail: 2206351447@qq.com
作者简介:张欣(1966—),男,河北承德人,河北大学教授,博士,主要从事机器视觉方向的研究. E-mail: zhangxin@hbu.edu.cn
基金资助:
河北省自然科学基金资助项目(F2017201172);河北省教育厅重点项目(ZD2018210);河北大学大学生创新创业训练计划项目(201810075013);河北大学实验室开放项目(sy201876)

Large lung tumor segmentation in CT images based on random walk algorithm

ZHANG Xin¹, WANG Jie¹, KONG Hang², WEN Yutong², LIU Wen², WANG Bing²

1. College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; 2. College of Mathematics and Information Science, Hebei University, Baoding 071002, China

Received:2018-10-13 Online:2019-05-25 Published:2019-05-25

摘要/Abstract

摘要： 针对CT图像肺肿瘤分割中复杂大肿瘤分割的准确性和自适应问题,提出了一种基于随机游走算法的分割方法.首先,根据图像灰度信息提取肺实质;针对大肿瘤与周围肺组织粘连的复杂情况,先提取有凹陷的肺实质,再根据肺实质先验轮廓,用曲线段形变模型修补肺实质的凹陷边界.然后,用区域生长法自动确定目标种子点和背景种子点;对于大血管与肿瘤粘连的情况,需要少量交互修改个别背景种子点.最后,用随机游走算法完成大肿瘤的分割.实验结果表明,该方法的准确性高,分割结果能够满足临床治疗效果分析和病理学研究的要求.

关键词: 肺肿瘤分割, 随机游走算法, 曲线段形变模型

Abstract: To the segmentation accuracy and adaption of large lung tumors with complex structures in CT images, a segmentation method based on random walk algorithm is proposed. First, the lung parenchyma was extracted according to the gray level information. If the large tumors adhered to normal tissues surrounding lungs, the lung parenchymas with concave edges are first extracted, and then the lung parenchyma with concave edges is refined by the curve section deformation model according to the prior shape of lung. Then a random walk algorithm is implemented, where the object and background seeds are automatically found by region growing method,and interaction are needed to modify a few background seed points if vasa adhere to tumors. The experiment results show that this method is accurate, which can meet the requirements of clinical treatment and pathological analysis.

Key words: lung tumor segmentation, random walk algorithm, curve section deformation model

中图分类号:

TP391

张欣,王洁,孔航,温誉童,刘雯,王兵. 基于随机游走算法的CT图像肺肿瘤分割[J]. 河北大学学报(自然科学版), 2019, 39(3): 311-315.

ZHANG Xin, WANG Jie, KONG Hang, WEN Yutong, LIU Wen, WANG Bing. Large lung tumor segmentation in CT images based on random walk algorithm[J]. Journal of Hebei University (Natural Science Edition), 2019, 39(3): 311-315.

参考文献

[1] ALLEMANI C, MATSUDA T, CARLO V D, et al. Global surveillance of trends in cancer survival 2000-14(CONCORD-3):analysis of individual records for 37513025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries[J]. Lancet, 2018, 391(10125): 1023-1075. DOI: 10.1016/S0140-6736(17)33326-3.
[2] HOSSEINIASL E, ZURADA J, GIMEL F G, et al. 3D Lung segmentation by incremental constrained nonnegative matrix factorization[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(5):952-963. DOI: 10.1109/TBME.2015.2482387.
[3] JOHNSON P B, YOUNG L A, LAMICHHANE N, et al. Quantitative imaging: correlating image features with the segmentation accuracy of PET based tumor contours in the lung[J]. Radiotherapy & Oncology, 2017, 123(2):257-262. DOI: 10.1016/j.radonc.2017.03.008.
[4] PULAGAM A R, KANDE G B, EDE V K R, et al. Automated lung segmentation from HRCT scans with diffuse parenchymal lung diseases[J]. Journal of Digital Imaging, 2016, 29(4):507-519. DOI: 10.1007/s10278-016-9875-z.
[5] CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277. DOI: 10.1109/83.902291.
[6] NIAN Y, LI M, CUI H, et al. Graph-based unsupervised segmentation for lung tumor CT images[Z]. IEEE International Conference on Computer & Communications, Chengdu,China,2018. DOI: 10.1109/CompComm.2017.8322865.
[7] GRADY L. Multilabel random walker image segmentation using prior models[Z]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition, San Diego,USA, 2005. DOI: 10.1109/CVPR.2005.239.
[8] 顾潇蒙. 基于随机游走算法的CT图像肺区域和肺肿瘤的分割研究[D]. 保定: 河北大学, 2016.
[9] TAN Y, SCHWARTZ L H, ZHAO B. Segmentation of lung lesions on CT scans using watershed, active contours, and markov random field[J]. Medical Physics, 2013, 40(4):043502. DOI: 10.1118/1.4793409.
[10] PERONA P, MALIK J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12( 7):629-639. DOI: 10.1109/34.56205.
[11] GU Y, KUMAR V, HALL L O, et al. Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach[J]. Pattern Recognition, 2013, 46(3):692-702. DOI: 10.1016/j.patcog.2012.10.005.

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基于随机游走算法的CT图像肺肿瘤分割

Large lung tumor segmentation in CT images based on random walk algorithm

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