Username   Password       Forgot your password?  Forgot your username? 

A 3D Segmentation Method for Pulmonary Nodule Image Sequences based on Supervoxels and Multimodal Data

Volume 13, Number 5, September 2017 - Paper 12  - pp. 682-696
DOI: 10.23940/ijpe.17.05.p12.682696

Qiang Cuia, Zinlin Qianga, Juanjuan Zhaoa,* , Yan Qianga, Xiaolei Liaoa

aSchool of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China

(Submitted on April 20, 2017; Revised on June 13, 2017; Accepted on August 20, 2017)


Three-dimensional reconstruction can reflect the dynamic relationship between lung lesions and surrounding tissues. It is easy to obtain an intuitive understanding of the shape, size, appearance and surroundings of pulmonary nodules, such as pleura or blood vessels. Three-dimensional reconstruction greatly improves the quality of surgery and reduces risk. This technique can help doctors to understand disease better and can guide operations in complex anatomical areas; therefore, it is worth recommending its clinical use. Therefore, our paper proposes a 3D segmentation method for use with pulmonary nodule image sequences based on supervoxels and multimodal data. First, we segment the lung parenchyma into superpixels. Then, we register PET/CT images using mutual information to roughly locate pulmonary nodule areas, matching the accurate pulmonary nodule areas using a multi-scale circular template matching algorithm. Finally, an improved three-dimensional supervoxel region-growing algorithm is proposed to reconstruct three-dimensional pulmonary nodules. The experimental results show that compared with the 3D region-growing algorithm, our algorithm can reconstruct complex pulmonary nodules more accurately and reduce time complexity.


References: 35

    1. F. Behnia, S. Elojeimy, M. Matesan, and D. C. Fajgenbaum, “Potential Value of FDG PET-CT in Diagnosis and Follow-up of TAFRO Syndrome,” Annals of Hematology, vol. 96, no. 3, pp. 497–500, March 2017
    2. W. D. Bidgood, S. C. Horii, F. W. Prior, and D. E. V. Syckle, “Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging,” Journal of the American Medical Informatics Association Jamia, vol. 4, no. 3, pp. 199-212, 1997
    3. R. Brunelli, “Template Matching Techniques in Computer Vision: Theory and Practice”, Wiley Publishing Co., Inc., Hoboken, 2009
    4. A. K. Buck and M. Schwaiger, “Positron Emission Tomography,” Springer Berlin Heidelberg Co., Inc., Berlin, 2017
    5. Y. Chen, A. Furukawa, A. Taniguchi, T. Tateyama, and S. Kanasaki, “Automated Assessment of Small Bowel Motility Function based on Simple Linear Iterative Clustering (SLIC),” in Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp.1737-1740, Zhangjiajie, China, August 2015
    6. J. H. Chu, H. Min, L. Liu, and W. Lu, “A Novel Computer Aided Breast Mass Detection Scheme based on Morphological Enhancement and SLIC Superpixel Segmentation,” Medical Physics, vol. 42, no. 7, pp. 3859-3869, July 2015
    7. S. Diederich, M. Lentschig, T. Overbeck, D. Wormanns, and W. Heindel, “Detection of Pulmonary Nodules at Spiral CT: Comparison of Maximum Intensity Projection Sliding Slabs and Single-image Reporting,” European Radiology, vol. 11, no. 8, pp. 1345–1350, August 2001
    8. A. Fabijańska, M. Janaszewski, M. Postolski, L. Babout, “Airway Tree Segmentation from CT Scans Using Gradient-Guided 3D Region Growing,” in Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP), pp. 247-254, Guadalajara, Jalisco, Mexico, November 2009
    9. H. P. Fan, G. H. Zeng, M. Body, and M. S. Hacid, “Seeded Region Growing: an Extensive and Comparative Study,” Pattern Recognition Letters, vol. 26, no. 8, pp. 1139-1156, June 2005
    10. A. A. Farag, J. Graham, S. Elshazly, and A. Farag, “Data-driven Lung Nodule Models for Robust Nodule Detection in Chest CT,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR), pp. 2588-2591, Istanbul, Turkey, August 2010
    11. G. H. Gu and Y. Zhao, “Scene Classification based on Spatial Pyramid Representation by Superpixel Lattices and Contextual Visual Features,” Optical Engineering, vol. 51, no. 1, pp. 7201-7209, February 2012
    12. A. A. Hammoudi, F. Li, L. Gao, Z. Wang, M. J. Thrall, and Y. Massoud, “Automated Nuclear Segmentation of Coherent Anti-stokes Raman Scattering Microscopy Images by Coupling Superpixel Context Information with Artificial Neural Networks,” in Proceedings of the Second International Conference on Machine Learning in Medical Imaging (MLMI), pp. 317-325, Toronto, Canada, September 2011
    13. D. M. Hansell, A. A. Bankier, H. MacMahon, T. C. McLoud, N. L. Müller, and J. Remy, “Fleischner Society: Glossary of Terms for Thoracic Imaging,” Radiology, vol. 246, no. 3, pp. 697-722, March 2008
    14. T. Heimann, B. van Ginneken, M. A. Styner, Y. Arzhaeva, V. Aurich, and C. Bauer, “Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets,” IEEE Transactions on Medical Imaging, vol. 28, no. 8, pp. 1251-1265, August 2009
    15. B. Irving, A. Cifor, B. W. Papiez, J. Franklin, E. M. Anderson, S. M. Brady, and J. A. Schnabel, “Automated Colorectal Tumour Segmentation in DCE-MRI Using Supervoxel Neighbourhood Contrast Characteristics,” in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 609–616, Boston, USA, September 2014
    16. S. Kobashi, N. Kamiura, Y. Hata, and F. Miyawaki, “Volume-quantization-based Neural Network Approach to 3D MR Angiography Image Segmentation,” Image & Vision Computing, vol. 19, no. 4, pp. 185-93, March 2001
    17. W. J. Kostis, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke, “Three-dimensional Segmentation and Growth-rate Estimation of Small Pulmonary Nodules in Helical CT Images,” IEEE Transactions on Medical Imaging, vol. 22, no. 10, pp. 1259-1274, October 2003
    18. X. L. Liao, J. J. Zhao, C. Jiao, L. Lei, Y. Qiang, and Q. Cui, “A Segmentation Method for Lung Parenchyma Image Sequences based on Superpixels and a Self-Generating Neural Forest,” Plos One, vol. 11, no. 8, pp. e0160556, August 2016
    19. Y. Lin and C. Xiu, “Template Matching Algorithm Based on Edge Detection,” in Proceedings of the International Symposium on Computer Science and Society (ISCCS), pp. 7-9, Kota Kinabalu, Malaysia, July 2011
    20. M. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, “Entropy Rate Superpixel Segmentation,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097-2104, Colorado Springs, USA, June 2011
    21. H. Luan, F. Qi, Z. Xue, L. Chen, and D. Shen, “Multimodality Image Registration by Maximization of Quantitative-qualitative Measure of Mutual Information,” Pattern Recognition, vol. 41, no. 1, pp. 285-98, January 2008
    22. L. B. Lusted, “Logical Analysis in Roentgen Diagnosis,” Radiology, vol. 74, no. 2, pp. 178–193, February 1960
    23. F. Maes, A. Collignon, D. Vandermeulen, and G. Marchal, “Multi-modality Image Registration by Maximization of Mutual Information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 187-198, May 1997
    24. P. Masa-Ah and S. Soongsathitanon, “A Novel Standardized Uptake Value (SUV) Calculation of PET DICOM Files Using MATLAB,” in Proceedings of the 10th WSEAS International Conference on Applied Informatics and Communications, and 3rd WSEAS International Conference on Biomedical Electronics and Biomedical Informatics (AIC/BEBI), pp. 413-416, Taipei, Taiwan, August 2010
    25. A. Moore, S. Prince, J. Warrell, U. Mohammed, and G. Jones, “Superpixel Lattices,” in Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, Anchorage, USA, June 2008
    26. M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, “A Brain Tumor Segmentation Framework based on Outlier Detection,” Medical Image Analysis, vol. 8, no. 3, pp. 275-283, September 2004
    27. X. Ren and J. Malik, “Learning a Classification Model for Segmentation,” in Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV), pp. 10, Washington DC, USA, October 2003
    28. C. Revol-Muller, F. Peyrin, Y. Carrillon, and C. Odet, “Automated 3D Region Growing Algorithm based on an Assessment Function,” Pattern Recognition Letters, vol. 23, no. 1-3, pp. 137–150, January 2002
    29. R. Siegel, D. Naishadham, and A. Jemal, “Cancer statistics, 2013,” CA: A Cancer Journal for Clinicians, vol. 63, no. 1, pp. 11–30, January 2013
    30. S. S. Sun, Y. Guo, Y. B. Guan, H. Z. Ren, L. N. Fan, and Y. Kang, “Juxta-vascular Nodule Segmentation based on Flow Entropy and Geodesic Distance,” IEEE Journal of Biomedical & Health Informatics, vol. 18, no. 4, pp. 1355–1362, July 2014
    31. W. J. Tuddenham, “Visual Search, Image Organization, and Reader Error in Roentgen Diagnosis. Studies of the Psycho-physiology of Roentgen Image Perception,” Radiology, vol. 78, no. 5, pp. 694–704, May 1962
    32. P. Viola and W. M. W. Iii, “Alignment by Maximization of Mutual Information,” International Journal of Computer Vision, vol. 24, no. 2, pp. 137-54, Septemper 1997
    33. T. W. Way, L. M. Hadjiiski, B. Sahiner, H. P. Chan, and P. N. Cascade, “Computer-aided Diagnosis of Pulmonary Nodules on CT Scans: Segmentation and Classification Using 3D Active Contours,” Medical Physics, vol. 33, no. 7, pp. 2323-2337, July 2006
    34. S. Wu and J. Wang, “Pulmonary Nodules 3D Detection on Serial CT Scans,” in Proceedings of the Second WRI Global Congress on Intelligent Systems (GCIS), pp. 257-260, Wuhan, China, November 2012
    35. W. Wu, Z. Zhou, S. Wu, and Y. Zhang, “Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-based Graph Cuts,” Computational and Mathematical Methods in Medicine, vol. 2016, no. 2016-4-5, pp. 2016:9093721, April 2016



      Click here to download the paper.

      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      This site uses encryption for transmitting your passwords.