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Automatic approach for lung segmentation with juxta-pleural nodules from thoracic CT based on contour tracing and correction. (English) Zbl 1423.92184
Summary: This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) \(11.15 \pm 69.63\) cm, volume overlap error (VOE) \(3.5057 \pm 1.3719\)%, average surface distance (ASD) \(0.7917 \pm 0.2741\) mm, root mean square distance (RMSD) \(1.6957 \pm 0.6568\) mm, maximum symmetric absolute surface distance (MSD) \(21.3430 \pm 8.1743\) mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.
MSC:
92C55 Biomedical imaging and signal processing
Software:
MITK; VTK
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[1] Sun, X.; Zhang, H.; Duan, H., 3D computerized segmentation of lung volume with computed tomography, Academic Radiology, 13, 6, 670-677, (2006)
[2] Brown, M. S.; McNitt-Gray, M. F.; Mankovich, N. J.; Goldin, J. G.; Hiller, J.; Wilson, L. S.; Aberle, D. R., Method for segmenting chest CT image data using an anatomical model: Preliminary results, IEEE Transactions on Medical Imaging, 16, 6, 828-839, (1997)
[3] Brown, M. S.; Goldin, J. G.; McNitt-Gray, M. F.; Greaser, L. E.; Sapra, A.; Li, K.-T.; Sayre, J. W.; Martin, K.; Aberle, D. R., Knowledge-based segmentation of thoracic computed tomography images for assessment of split lung function, Medical Physics, 27, 3, 592-598, (2000)
[4] Sun, S.; Bauer, C.; Beichel, R., Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach, IEEE Transactions on Medical Imaging, 31, 2, 449-460, (2012)
[5] Ukil, S.; Reinhardt, J. M., Smoothing lung segmentation surfaces in three-dimensional X-ray CT images using anatomic guidance, Academic Radiology, 12, 12, 1502-1511, (2005)
[6] Van Rikxoort, E. M.; De Hoop, B.; Viergever, M. A.; Prokop, M.; Van Ginneken, B., Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection, Medical Physics, 36, 7, 2934-2947, (2009)
[7] Hasegawa, A.; Lo, S.-C. B.; Lin, J.-S.; Freedman, M. T.; Mun, S. K., A shift-invariant neural network for the lung field segmentation in chest radiography, Journal of Signal Processing Systems, 18, 3, 241-250, (1998)
[8] Leader, J. K.; Zheng, B.; Rogers, R. M.; Sciurba, F. C.; Perez, A.; Chapman, B. E.; Patel, S.; Fuhrman, C. R.; Gur, D., Automated lung segmentation in X-Ray computed tomography: development and evaluation of a heuristic threshold-based scheme, Academic Radiology, 10, 11, 1224-1236, (2003)
[9] Yang, Y.; Zhou, S.; Shang, P.; Qi, E.; Wu, S.; Xie, Y., Contour propagation using feature-based deformable registration for lung cancer, BioMed Research International, 2013, (2013)
[10] Sofka, M.; Wetzl, J.; Birkbeck, N.; Zhang, J.; Kohlberger, T.; Kaftan, J.; Declerck, J.; Zhou, S. K., Multi-stage learning for robust lung segmentation in challenging ct volumes, Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011: 14th International Conference, Toronto, Canada, September 18–22, 2011, Proceedings, Part III. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011: 14th International Conference, Toronto, Canada, September 18–22, 2011, Proceedings, Part III, Lecture Notes in Computer Science, 6893, 667-674, (2011), Berlin, Germany: Springer, Berlin, Germany
[11] Korfiatis, P.; Kalogeropoulou, C.; Karahaliou, A.; Kazantzi, A.; Skiadopoulos, S.; Costaridou, L., Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT, Medical Physics, 35, 12, 5290-5302, (2008)
[12] Yim, Y.; Hong, H., Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images, Computers in Biology and Medicine, 38, 8, 845-857, (2008)
[13] Pu, J.; Roos, J.; Yi, C. A.; Napel, S.; Rubin, G. D.; Paik, D. S., Adaptive border marching algorithm: automatic lung segmentation on chest CT images, Computerized Medical Imaging and Graphics, 32, 6, 452-462, (2008)
[14] Zhou, S.; Cheng, Y.; Tamura, S., Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images, Biomedical Signal Processing and Control, 13, 1, 62-70, (2014)
[15] Prasad, M. N.; Brown, M. S.; Ahmad, S.; Abtin, F.; Allen, J.; da Costa, I.; Kim, H. J.; McNitt-Gray, M. F.; Goldin, J. G., Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs, Academic Radiology, 15, 9, 1173-1180, (2008)
[16] Sluimer, I.; Prokop, M.; Van Ginneken, B., Toward automated segmentation of the pathological lung in CT, IEEE Transactions on Medical Imaging, 24, 8, 1025-1038, (2005)
[17] Kirschner, M., The probabilistic active shape model: from model construction to flexible medical image segmentation [Ph.D. thesis], (2013), TU Darmstadt
[18] Jolliffe, I. T., Principal Component Analysis, (2002), Wiley Online Library · Zbl 1011.62064
[19] Otsu, N., A threshold selection method from gray-level histograms, Automatica, 11, 285–296, 23-27, (1975)
[20] Kalender, W. A.; Fichte, H.; Bautz, W.; Skalej, M., Semiautomatic evaluation procedures for quantitative ct of the lung, Journal of Computer Assisted Tomography, 15, 2, 248-255, (1991)
[21] Hedlund, L. W.; Anderson, R. F.; Goulding, P. L.; Beck, J. W.; Effmann, E. L.; Putman, C. E., Two methods for isolating the lung area of a CT scan for density information, Radiology, 144, 2, 353-357, (1982)
[22] Hu, S.; Hoffman, E. A.; Reinhardt, J. M., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images, IEEE Transactions on Medical Imaging, 20, 6, 490-498, (2001)
[23] Späth, H., Spline Algorithms for Curves and Surfaces, (1974), Utilitas Mathematica Publishing
[24] Hinton, E.; Campbell, J. S., Local and global smoothing of discontinuous finite element functions using a least squares method, International Journal for Numerical Methods in Engineering, 8, 3, 461-480, (1974) · Zbl 0286.73066
[25] Bose, P.; Morin, P.; Stojmenović, I.; Urrutia, J., Routing with guaranteed delivery in ad hoc wireless networks, Wireless Networks, 7, 6, 609-616, (2001) · Zbl 0996.68012
[26] Wolf, I.; Vetter, M.; Wegner, I.; Nolden, M.; Bottger, T.; Hastenteufel, M.; Schobinger, M.; Kunert, T.; Meinzer, H.-P., The medical imaging interaction toolkit (MITK): a toolkit facilitating the creation of interactive software by extending VTK and ITK, Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, 16
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