Robust Pulmonary Nodule Segmentation in CT Image for Juxta-pleural and Juxta-vascular Case

Author(s): Zhang Yang*, Xie Yingying, Guo Li, Zhang Zewei, Ding Weifeng, Pan Zhifang, Qin Jing.

Journal Name: Current Bioinformatics

Volume 14 , Issue 2 , 2019

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Graphical Abstract:


Background: Lung cancer is a greatest threat to people's health and life. CT image leads to unclear boundary segmentation. Segmentation of irregular nodules and complex structure, boundary information is not well considered and lung nodules have always been a hot topic.

Objective: In this study, the pulmonary nodule segmentation is accomplished with the new graph cut algorithm. The problem of segmenting the juxta-pleural and juxta-vascular nodules was investigated which is based on graph cut algorithm.

Methods: Firstly, the inflection points by the curvature was decided. Secondly, we used kernel graph cut to segment the nodules for the initial edge. Thirdly, the seeds points based on cast raying method is performed; lastly, a novel geodesic distance function is proposed to improve the graph cut algorithm and applied in lung nodules segmentation.

Results: The new algorithm has been tested on total 258 nodules. Table 1 summarizes the morphologic features of all the nodules and given the results between the successful segmentation group and the poor/failed segmentation group. Figure 1 to Fig. (12) shows segmentation effect of Juxta-vascular nodules, Juxta-pleural nodules, and comparted with the other interactive segmentation methods.

Conclusion: The experimental verification shows better results with our algorithm, the results will measure the volume numerical approach to nodule volume. The results of lung nodules segmentation in this study are as good as the results obtained by the other methods.

Keywords: Pulmonary nodule segmentation, CT image, juxta-vascular, graph cut problem, segmentation of the nodules, curvature information.

Ye X, Beddoe G, Slabaugh G. Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans Biomed Eng 2009; 56: 1810-20.
Cha J, Farhangi MM, Dunlap N, Amini AA. Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4D CT. Med Phys 2018; 45: 297-306.
Rios Velazquez E, Aerts HJ, Gu Y, et al. A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen. Radiother Oncol 2012; 105: 167-73.
Jirapatnakul AC, Mulman YD, Reeves AP, Yankelevitz DF, Henschke CI. Segmentation of juxta-pleural pulmonary nodules using a robust surface estimate. Int J Biomed Imaging 2011; 11: 1-14.
Liu B, Raj A. Discriminative random field segmentation of lung nodules in CT studies. Comput Math Methods Med 2013; 8: 1-9.
Lermé N, Malgouyres F, Rocchisani JM. Fast and memory efficient segmentation of lung tumors using graph cuts. Comput Sci 2010; 6: 1-12.
Hollensen C, Cannon G, Cannon D, et al. Lung tumor segmentation using electric flow lines for graph cuts. Image Anal Recog 2012; 7: 206-13.
Ballangan C, Wang X, Fulham M, Eberl S, Feng DD. Lung tumor segmentation in PET images using graph cuts. Comput Methods Programs Biomed 2013; 109: 260-8.
Schildkraut JS, Prosser N, Savakis A, et al. Level-set segmentation of pulmonary nodules in megavolt electronic portal images using a CT prior. Med Phys 2010; 37: 5703-10.
Meng L, Zhao H. A novel approach of lung segmentation on chest CT images using graph cuts. Neurocomputing 2015; 168: 799-807.
Gu Y, Kumar V, Hall LO, et al. Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recognit 2013; 46: 692-702.
Iwano S, Okada T, Koike W, et al. Semi-automatic volumetric measurement of lung cancer using multi-detector CT effects of nodule characteristics. Acad Radiol 2009; 16: 1179-86.
Lavanya M, Kannan PM. Lung Lesion detection in CT scan images using the fuzzy local information cluster means (FLICM) automatic segmentation algorithm and back propagation network classification. Asian Pac J Cancer Prev 2017; 18: 3395-9.
Farag AA, El Munim HE, Graham JH, Farag AA. A novel approach for lung nodules segmentation in chest CT using level sets. IEEE Trans Image Process 2012; 13: 5202-13.
Li B, Chen K, Tian L, Yeboah Y, Ou S. Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model. Comput Math Methods Med 2013; 2013: 515386.
Zou Q, Li J, Song L, Zeng X, Wang G. Similarity computation strategies in the microRNA-disease network: a survey. Brief Funct Genomics 2016; 15: 55-64.
Moltz JH, Kuhnigk JM, Bornemann L, Peitgen HO. Segmentation of juxta-pleural lung nodules in CT scans based on ellipsoid approximation. Pulmonary Image Anal 2008; 1: 25-32.
Mina Z, Jina R, Song E, Liu H, Hung CC, Wang X. 3-D segmentation of lung nodules in CT images based on improved level set method. Int J Inform 2011; 14: 1411-8.
Yi YF, Gao LQ, Guo L. Pulmonary nodules segmentation method based on improved random walker algorithm. J Northeastern University 2012; 33: 318-22.
Xu N, Ahuja N, Bansal R. Object segmentation using graph cuts based active contours. Comput Vis Image Underst 2007; 107: 210-24.
Holuša M, Sojka E. Image segmentation using iterated graph cuts with residual graph. Intl Symp Vis Computing 2013; 1: 228-37.
Zou Q, Zeng J, Cao L, Ji R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 2016; 173: 346-54.
Ben Salah M, Mitiche A, Ben Ayed I. Multiregion image segmentation by parametric kernel graph cuts. IEEE Trans Image Process 2011; 20: 545-57.
McNitt-Gray MF, Armato SG III, Meyer CR, et al. The lung image database consortium (LIDC) data collection process for nodule detection and annotation. Acad Radiol 2007; 14: 1464-74.
Armato SG III, McLennan G, Bidaut L, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 2011; 38: 915-31.
Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M. Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. IEEE Trans Biomed Eng 2011; 58: 3418-28.
Sun S, Guo Y, Guan Y, Ren H, Fan L, Kang Y. Juxta-vascular nodule segmentation based on flow entropy and geodesic distance. IEEE J Biomed Health Inform 2014; 4: 1355-62.
Dehmeshki J, Amin H, Valdivieso M, Ye X. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans Med Imaging 2008; 27: 467-80.
Armato SG III, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H. Computerized detection of pulmonary nodules on CT scans. Radiographics 1999; 19: 1303-11.
Grady L. Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 2006; 28: 1768-83.
Mortensen EN, Barrett WA. Interactive segmentation with intelligent scissors. Graph Models Image Proc 1998; 60: 349-84.
Leyi W, Jijun T, Quan Z. Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information. Inf Sci 2017; 384: 135-44.
Boykov YY, Jolly MP. Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: VL Patel, R Rogers, R Haux. MEDINFO 117. Proceedings of the Eighth International Conference on Computer Vision; 2001 July 7-14. British Columbia, Canada. Vancouver, BC, Canada. 2001; pp. 105- 112

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Article Details

Year: 2019
Page: [139 - 147]
Pages: 9
DOI: 10.2174/1574893613666181029100249
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