Advertisement

Wireless Personal Communications

, Volume 109, Issue 3, pp 1987–2010 | Cite as

A Novel Automatic Liver Segmentation by Level Set Method Over Real-Time Sensory Computed Tomography

  • G. Ignisha RajathiEmail author
  • G. Wiselin Jiji
Article
  • 31 Downloads

Abstract

The issues based on gastroenterology are the most distressing diseases in a human anatomy with the largest solid organ, liver that requires serious attention in its diagnosis. The challenging task of Liver segmentation is the main motive, proposed with the exploring of level set methodology with signed pressure force combined with masking and other Morphological operations. Next, the segmented liver multi-atlases are condensed together as a complete 3D projection of Human Liver in 360° shape angulation. The huge set of abdominal slices with 0.16 mm spacing of about 250 real time images of each subject, is treated as input with this proposed method and nearly 2550 CT images are used to get segmented using this methodology. The Evaluation Metrics given as 5 Error Metrics and 4 Performance Metrics obtained with the averaging of 10 atlases to each point, of the segmented atlases, outperforms the comparative methods of the Radiologist’s inference of liver boundary detection and an additional existing Local Fuzzy thresholding methodology. The overall visual outcome, evaluation metrical values and graphical data shows the peculiar performance of proposed methodology. This outcome with the electronic evolution of computed tomography appends to the wireless sensory waves to mark the CT images for our input. The doctors will be greatly helped by our process, to serve the global cause of saving lives by diagnosing accurately the complications of liver with appropriate segments, at early stage itself.

Keywords

Liver segmentation Liver window Level set Volumetric measures 

Notes

Acknowledgements

The Abdominal CT slices were collected from TVMCH, Medall Diagnostics, Tirunelveli and a few more from Arthi Scans, Tirunelveli. Our special thanks to Dr.Arun Kumar MD.,RD, Managing Director, Arthi Scans-Tirunelveli for rendering his support in marking of referential images.

References

  1. 1.
    Maton, A., Hopkins, J., Johnson, S., McLaughlin, D. L. C. W., Warner, M. Q., & Wright, J. D. (1993). Human biology and health (Print book.). Englewood Cliffs : Prentice Hall. Retrieved from http://www.worldcat.org/title/human-biology-and-health/oclc/32308337.
  2. 2.
    Tucker, M. E. (2013). Global burden of liver disease substantial. Medscape Medical News, The Liver Meeting 2013: American Association for the Study of Liver Diseases (AASLD). https://www.medscape.com/viewarticle/813788.
  3. 3.
    Sheron, N. (2013). Facts-about-liver-disease. Retrieved from https://www.britishlivertrust.org.uk/about-us/media-centre/facts-about-liver-disease/. Accessed 16 Aug 2017.
  4. 4.
    Campbell, A. (2017). Alcohol-related deaths in the UK: Registered in 2015. Office for National Statistics. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/causesofdeath/bulletins/alcoholrelateddeathsintheunitedkingdom/registeredin2015. Accessed 16 Feb 2017.
  5. 5.
    Liver cancer statistics. (2012). World cancer research fund international. http://www.wcrf.org/int/cancer-facts-figures/data-specific-cancers/liver-cancer-statistics. Accessed 3 Aug 2017.
  6. 6.
    World Health Rankings. (2017). WHO 2013. http://www.worldlifeexpectancy.com/cause-of-death/liver-cancer/by-country/. Accessed 12 July 2017.
  7. 7.
    Chowdhury, A. (2004). Liver diseases in adults. Liver Foundation India. Retrieved from http://www.theliverfoundationindia.org/liver-disease-in-adults.html. Accessed 5 May 2017.
  8. 8.
    Dvořák, P., Bartušek, K., Kropatsch, W. G., & Smékal, Z. (2015). Automated multi-contrast brain pathological area extraction from 2D MR images. Journal of Applied Research and Technology,13(1), 58–69.  https://doi.org/10.1016/S1665-6423(15)30005-5.CrossRefGoogle Scholar
  9. 9.
    Kim, J., Lee, S., Lee, G., Park, Y., & Hong, Y. (2016). Using a method based on a modified k-means clustering and mean shift segmentation to reduce file sizes and detect brain tumors from magnetic resonance (MRI) images. Wireless Personal Communications,89(3), 993–1008.  https://doi.org/10.1007/s11277-016-3420-8.CrossRefGoogle Scholar
  10. 10.
    Cootes, T., Hill, A., Taylor, C., & Haslam, J. (1994). Use of active shape models for locating structures in medical images. Image and Vision Computing,12(6), 355–365.  https://doi.org/10.1016/0262-8856(94)90060-4.CrossRefGoogle Scholar
  11. 11.
    Berlin, I., Lamecker, H., Lange, T., Seebass, M., & Berlin, I. (2004). Segmentation of the liver using a 3D statistical shape model, April 9. Retrieved from https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/784.
  12. 12.
    Kainmüller, D., Lange, T., & Lamecker, H. (2007). Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In MICCAI workshop on 3D segmentation in the clinic: A grand challenge, (pp. 109–116). http://mbi.dkfz-heidelberg.de/grandchallenge2007/sites/proceed.htm.
  13. 13.
    Rodriguez, R., Mexicano, A., Bila, J., Cervantes, S., & Ponce, R. (2015). Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. Journal of Applied Research and Technology,13(2), 261–269.  https://doi.org/10.1016/j.jart.2015.06.008.CrossRefGoogle Scholar
  14. 14.
    Zhang, Xing, Tian, Jie, Deng, Kexin, Yongfang, Wu, & Li, Xiuli. (2010). Automatic liver segmentation using a statistical shape model with optimal surface detection. IEEE Transactions on Biomedical Engineering,57(10), 2622–2626.  https://doi.org/10.1109/TBME.2010.2056369.CrossRefGoogle Scholar
  15. 15.
    Heimann, T., Meinzer, H., & Wolf, I. (2007). A statistical deformable model for the segmentation of liver CT volumes using extended training data. In Proceeding on MICCAI workshop 3-D segmentat. Clinic: A grand, challenge, (pp. 161–166). https://scholar.google.com/scholar?q=Heimann%2C%20T.%2C%20Meinzer%2C%20H.P.%2C%20Wolf%2C%20I.%3A%20A%20Statistical%20Deformable%20Model%20for%20the%20Segmentation%20of%20Liver%20CT%20Volumes.%20In%3A%203D%20Segmentation%20in%20the%20Clinic%20-%20A%20Grand%20Challenge%2C%20pp.%20161%E2%80%93166%20%282007%290.
  16. 16.
    Bellman, R. (1966). Dynamic programming. Science,153(3731), 34–37.  https://doi.org/10.1126/science.153.3731.34.CrossRefzbMATHGoogle Scholar
  17. 17.
    Seghers, D., Slagmolen, P., Lambelin, Y., Hermans, J., Loeckx, D., Maes, F., & Suetens, P. (2007). Landmark based liver segmentation using local shape and local intensity models. In Proceedings of workshop of the 10th international conference on MICCAI, workshop on 3D segmentation in the clinic: A grand challenge, (pp. 135–142).Google Scholar
  18. 18.
    Saddi, K. A., Rousson, M., Chefd’hotel, C., & Cheriet, F. (2007). Global-to-local shape matching for liver segmentation in CT imaging. In Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, (pp. 207–214).Google Scholar
  19. 19.
    Schmidt, G., Athelogou, M., Schönmeyer, R., Korn, R., & Binnig, G. (2007). Cognition network technology for a fully automated 3d segmentation of liver. In MICCAI workshop on 3D segmentation in the clinic: A grand challenge (pp. 125–133). http://mbi.dkfzheidelberg.de/grand-challenge2007/sites/proceed.htm. http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p125.pdf.
  20. 20.
    Chi, Y., Cashman, P. M. M., Bello, F., & Kitney, R. I. (2007). A discussion on the evaluation of a new automatic liver volume segmentation method for specified CT image datasets. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge (pp. 167–175). https://scholar.google.com/scholar?q=Chi%20Y%2C%20Cashman%20PMM%2C%20Bello%20F%2C%20Kitney%20RI%2C%282007%29%20A%20discussion%20on%20the%20evaluation%20of%20a%20new%20automatic%20liver%20volume%20segmentation%20method%20for%20specified%20CT%20image%20datasets.%20In%3A%20Proceedings%20of%20MICCAI%20workshop%20on%203D%20segmentation%20in%20the%20clinic%3A%20a%20grand%20challenge%2C%20pp%20167%E2%80%93175.
  21. 21.
    Susomboon, R., Raicu, D. S., & Furst, J. (2007). A hybrid approach for liver segmentation. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge (pp. 151–160). https://scholar.google.com/scholar?q=Susomboon%20R%2C%20Raicu%20DS%2C%20Furst%20J%20%282007%29%20A%20hybrid%20approach%20for%20liver%20segmentation.%20In%3A%20Proceedings%20of%20MICCAI%20workshop%20on%203D%20segmentation%20in%20the%20clinic%3A%20a%20grand%20challenge%2C%20pp%20151%E2%80%93160.
  22. 22.
    McLachlan, G. J., & Krishnan, T. (2008). The EM algorithm and extensions, 2E. Hoboken: Wiley.  https://doi.org/10.1002/9780470191613.CrossRefzbMATHGoogle Scholar
  23. 23.
    Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC,3(6), 610–621.  https://doi.org/10.1109/tsmc.1973.4309314.CrossRefGoogle Scholar
  24. 24.
    Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed.). New York: Wiley.zbMATHGoogle Scholar
  25. 25.
    Beichel, R., Bauer, C., Bornik, A., Sorantin, E., & Bischof, H. (2007). Liver segmentation in CT data : A segmentation refinement approach. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand Challenge (pp 235–245). https://scholar.google.com/scholar?q=Beichel%20R%2C%20Bauer%20C%2C%20Bornik%20A%2C%20Sorantin%20E%2C%20Bischof%20H%20%282007%29%20Liver%20segmentation%20in%20CT%20data%3A%20a%20segmentation%20refinement%20approach.%20In%3A%20Proceedings%20of%20MICCAI%20workshop%20on%203D%20segmentation%20in%20the%20clinic%3A%20a%20grand%20Challenge%2C%20pp%20235%E2%80%93245.
  26. 26.
    Boykov, Y., & Funka-Lea, G. (2006). Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision,70(2), 109–131.  https://doi.org/10.1007/s11263-006-7934-5.CrossRefGoogle Scholar
  27. 27.
    Beck, A., & Aurich, V. (2007). HepaTux—A semiautomatic liver segmentation system. In: Proceedings of MICCAI workshop on 3Dsegmentation in the clinic: a grand challenge (pp. 225–233). https://scholar.google.com/scholar?q=Beck%20A%2C%20Aurich%20V%20%282007%29%20HepaTuxa%20semiautomatic%20liver%20segmentation%20system.%20In%3A%20Proceedings%20of%20MICCAI%20workshop%20on%203D%20segme ntation%20in%20the%20clinic%3A%20a%20grand%20challenge%2C%20pp%20225%96234.
  28. 28.
    Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999). Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging,18(8), 712–721.  https://doi.org/10.1109/42.796284.CrossRefGoogle Scholar
  29. 29.
    Slagmolen, P., Elen, A., Seghers, D., Loeckx, D., Maes, F., & Haustermans, K. (2007). Atlas based liver segmentation using nonrigid registration with a B-spline transformation model. In Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge (pp.197–206). https://scholar.google.com/scholar?q=Slagmolen%20P%2C%20Elen%20A%2C%20Seghers%20D%2C%20Loeckx%20D%2C%20Maes%20F%2C%20Haustermans%2C%20K%20%282007%29%20Atlas%20based%20liver%20segmentation%20using%20nonrigid%20registration%20with%20a%20Bspline%20transformation%20model.%20In%3A%20Proceedings%20of%20MICCAI%20workshop%20on%203D%20segmentation%20in%20the%20clinic%3A%20a%20grand%20challenge%2C%20pp%20197%E2%80%93206.
  30. 30.
    Sonka, M., Hlavac, V., & Boyle, R. (2007). Image processing, analysis, and machine vision (3rd ed.). Thomson-Engineering.Google Scholar
  31. 31.
    Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence,16(6), 641–647.  https://doi.org/10.1109/34.295913.CrossRefGoogle Scholar
  32. 32.
    Yuqian Zhao, Yunlong Zan, Xiaofang Wang, & Guiyuan Li. (2010). Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation. In 2010 Chinese control and decision conference (pp. 3423–3427). IEEE.  https://doi.org/10.1109/ccdc.2010.5498558.
  33. 33.
    Foruzan, A. H., & Chen, Y.-W. (2013). Segmentation of liver in low-contrast images using k-means clustering and geodesic active contour algorithms. IEICE Transactions on Information and Systems,96(4), 798–807.CrossRefGoogle Scholar
  34. 34.
    Huang, W., Tan, Z. M., Lin, Z., Huang, G., Zhou, J., Chui, C. K., Su, Y. & Chang, S. (2012). A semi-automatic approach to the segmentation of liver parenchyma from 3D CT images with extreme learning machine. In 2012 annual international conference of the IEEE engineering in medicine and biology society (pp. 3752–3755). IEEE.  https://doi.org/10.1109/embc.2012.6346783.
  35. 35.
    Wu, W., Zhou, Z., Wu, S., & Zhang, Y. (2016). Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts. Computational and Mathematical Methods in Medicine,2016, 1–14.  https://doi.org/10.1155/2016/9093721.MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton–Jacobi formulations. Journal of Computational Physics,79(1), 12–49.  https://doi.org/10.1016/0021-9991(88)90002-2.MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
  38. 38.
    Tomoshige, S., Oost, E., Shimizu, A., Watanabe, H., & Nawano, S. (2014). A conditional statistical shape model with integrated error estimation of the conditions; Application to liver segmentation in non-contrast CT images. Medical Image Analysis,18(1), 130–143.  https://doi.org/10.1016/j.media.2013.10.003.CrossRefGoogle Scholar
  39. 39.
    Sethian, J. A. (1999). Level set methods and fast marching methods: Evolving interfaces in computational geometry, fluid mechanics, computer vision and material science. Cambridge University Press.Google Scholar
  40. 40.
    Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence,17(2), 158–175.  https://doi.org/10.1109/34.368173.CrossRefGoogle Scholar
  41. 41.
    Lefohn, A. E., Kniss, J. M., Hansen, C. D., & Whitaker, R. T. (2004). A streaming narrow-band algorithm: interactive computation and visualization of level sets. IEEE Transactions on Visualization and Computer Graphics,10(4), 422–433.  https://doi.org/10.1109/TVCG.2004.2.CrossRefGoogle Scholar
  42. 42.
    Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision,22(1), 61–79.  https://doi.org/10.1023/A:1007979827043.CrossRefzbMATHGoogle Scholar
  43. 43.
    Dawant, B. M., Li, R., Lennon, B., & Li, S. (2007). Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge (pp. 215–221). https://scholar.google.com/scholar?q=Dawant%20BM%2C%20Li%20R%2C%20Lennon%20B%2C%20Li%20S%20%282007%29%20Semiautomatic%20segmentation%20of%20the%20liver%20and%20its%20evaluation%20on%20the%20MICCAI%202007%20grand%20challenge%20data%20set.%20In%3A%20Proceedings%20of%20MICCAI%20workshop%20on%203D%20segmentation%20in%20the%20clinic%3A%20a%20grand%20challenge%2C%20pp%20215%E2%80%93221.
  44. 44.
    Lee, J., Kim, N., Lee, H., Seo, J. B., & Won, H. J. (2007). Efficient liver segmentation exploiting level-set speed images with 2. Shape Propagation,5D, 189–196.Google Scholar
  45. 45.
    Wimmer, A., Soza, G., & Hornegger, J. (2007). Two-stage semi-automatic organ segmentation framework using radial basis functions and level sets. In Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge (pp. 179–188). https://scholar.google.com/scholar?q=Wimmer%20A%2C%20Soza%20G%2C%20Hornegger%20J%20%282007%29%20Two-stage%20semiautomatic%20organ%20segmentation%20framework%20using%20radial%20basis%20functions%20and%20level%20sets%3A%20In%3A%20Proceedings%20of%20MICCAI%20workshop%20on%203D%20segmentation%20in%20the%20clinic%3A%20a%20grand%20challenge%2C%20pp%20179%E2%80%93188.
  46. 46.
    Masoodhu Banu, N. M., & Sujatha, S. (2015). Improved tampering detection for image authentication based on image partitioning. Wireless Personal Communications,84(1), 69–85.  https://doi.org/10.1007/s11277-015-2594-9.CrossRefGoogle Scholar
  47. 47.
    Lee, S., Lee, G., Hong, Y., & Kim, J. (2016). A study on the improved normalized cut algorithm using a bilateral filter for efficient object extraction from image. Wireless Personal Communications,86(1), 77–90.  https://doi.org/10.1007/s11277-015-3033-7.CrossRefGoogle Scholar
  48. 48.
    Hongzhe Y., Yongtian W., Jian Y., & Yue L. (2010). A novel graph cuts based liver segmentation method. In 2010 International conference of medical image analysis and clinical application (pp. 50–53). IEEE.  https://doi.org/10.1109/miaca.2010.5528409.
  49. 49.
    Massoptier, L., & Casciaro, S. (2007). Fully automatic liver segmentation through graph-cut technique. In 2007 29th Annual international conference of the IEEE engineering in medicine and biology society (pp. 5243–5246). IEEE.  https://doi.org/10.1109/iembs.2007.4353524.
  50. 50.
    Mittal, A., Kundu, C., Bose, R., & Shevgaonkar, R. K. (2017). Entropy based image segmentation for energy efficient LTE system with cloud. Wireless Personal Communications,92(3), 1145–1162.  https://doi.org/10.1007/s11277-016-3598-9.CrossRefGoogle Scholar
  51. 51.
    Luo, S., Li, X., & Li, J. (2014). Review on the methods of automatic liver segmentation from abdominal images. Journal of Computer and Communications,2(2), 1–7.  https://doi.org/10.4236/jcc.2014.22001.MathSciNetCrossRefGoogle Scholar
  52. 52.
    Campadelli, P., Casiraghi, E., & Esposito, A. (2009). Liver segmentation from computed tomography scans: A survey and a new algorithm. Artificial Intelligence in Medicine,45(2–3), 185–196.  https://doi.org/10.1016/j.artmed.2008.07.020.CrossRefGoogle Scholar
  53. 53.
    Rathore, S., Iftikhar, M. A., Hussain, M., & Jalil, A. (2011). Texture analysis for liver segmentation and classification: A survey. Frontiers of Information Technology,2011, 121–126.  https://doi.org/10.1109/FIT.2011.30.CrossRefGoogle Scholar
  54. 54.
    Mharib, A. M., Ramli, A. R., Mashohor, S., & Mahmood, R. B. (2012). Survey on liver CT image segmentation methods. Artificial Intelligence Review,37(2), 83–95.  https://doi.org/10.1007/s10462-011-9220-3.CrossRefGoogle Scholar
  55. 55.
    Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing,10(2), 266–277.  https://doi.org/10.1109/83.902291.CrossRefzbMATHGoogle Scholar
  56. 56.
    Pomerantz, S. M., White, C. S., Krebs, T. L., Daly, B., Sukumar, S. A., Hooper, F., et al. (2000). Liver and bone window settings for soft-copy interpretation of chest and abdominal CT. American Journal of Roentgenology,174(2), 311–314.  https://doi.org/10.2214/ajr.174.2.1740311.CrossRefGoogle Scholar
  57. 57.
    Patten, R. M., Gunberg, S. R., Brandenburger, D. K., & Richardson, M. L. (2000). CT detection of hepatic and splenic injuries. American Journal of Roentgenology,175(4), 1107–1110.  https://doi.org/10.2214/ajr.175.4.1751107.CrossRefGoogle Scholar
  58. 58.
    Dang, T., & Mandarano, G. (2006). A review of the benefits and rationale of viewing liver window settings for abdominal computed tomography scans. The Radiographer,53(1), 12–19.CrossRefGoogle Scholar
  59. 59.
    Woodward, A., Chan, Y. H., Gong, R., Nguyen, M., Gee, T., Delmas, P., et al. (2017). A low cost framework for real-time marker based 3-D human expression modeling. Journal of Applied Research and Technology,15(1), 61–77.  https://doi.org/10.1016/j.jart.2017.01.002.CrossRefGoogle Scholar
  60. 60.
    Yang, S.-C., Yu, C.-Y., Lin, C.-J., Lin, H.-Y., & Lin, C.-Y. (2015). Reconstruction of three-dimensional breast-tumor model using multispectral gradient vector flow snake method. Journal of Applied Research and Technology,13(2), 279–290.  https://doi.org/10.1016/j.jart.2015.06.014.CrossRefGoogle Scholar
  61. 61.
    Zhang, R., Zhu, S., & Zhou, Q. (2016). A novel gradient vector flow snake model based on convex function for infrared image segmentation. Sensors,16(10), 1756.  https://doi.org/10.3390/s16101756.CrossRefGoogle Scholar
  62. 62.
    Zhu, S., & Gao, R. (2016). A novel generalized gradient vector flow snake model using minimal surface and component-normalized method for medical image segmentation. Biomedical Signal Processing and Control.  https://doi.org/10.1016/j.bspc.2015.12.004.CrossRefGoogle Scholar
  63. 63.
    Singh, A. K., Dave, M., & Mohan, A. (2015). Multilevel encrypted text watermarking on medical images using spread-spectrum in DWT domain. Wireless Personal Communications,83(3), 2133–2150.  https://doi.org/10.1007/s11277-015-2505-0.CrossRefGoogle Scholar
  64. 64.
    Jiji, G. W., & DuraiRaj, P. J. (2015). Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique. Applied Soft Computing,30, 650–662.  https://doi.org/10.1016/j.asoc.2015.01.058.CrossRefGoogle Scholar
  65. 65.
    Gimel, G. (2013). Part 3: Image processing. Basics of Mathematical Morphology. https://www.cs.auckland.ac.nz/courses/compsci373s1c/PatricesLectures/2013/CS373-IP-02.pdf.
  66. 66.
    Efford, N. (2002). Digital image processing: A practical introduction using JavaTM. Pearson Education. Retrieved from https://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm.
  67. 67.
    Ronse, C., & Devijve, P. A. (1984). Connected components in binary images: The detection problem. Research Studies (pp 91-102). https://scholar.google.com/scholar?as_q=%22Connected+Components+in+Binary+Images%3A+The+Detection+Problem%2C%22&as_occt=title&hl=en&as_sdt=0%2C31.
  68. 68.
    Yang, X. D. (1989). An improved algorithm for labeling connected components in a binary image. TR 89-981, March. https://apps.dtic.mil/dtic/tr/fulltext/u2/a210100.pdf.
  69. 69.
    Park, J.-M., Looney, C.G., & Chen, H.-C. (2000) Fast connected component labeling algorithm using a divide and conquer technique. In S.Y. Shin (Ed.), Computers and Their Applications: Proceedings of the ISCA 15th International Conference on Computers and Their Applications, March 29-31, 2000. ISCA 2000 (pp. 373–376). New Orleans: Louisiana USA.Google Scholar
  70. 70.
    Zhang, K., Zhang, L., Song, H., & Zhou, W. (2010). Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing,28(4), 668–676.  https://doi.org/10.1016/j.imavis.2009.10.009.CrossRefGoogle Scholar
  71. 71.
    Jiang, H., Feng, R., & Gao, X. (2011). Level set based on signed pressure force function and its application in liver image segmentation. Wuhan University Journal of Natural Sciences..  https://doi.org/10.1007/s11859-011-0748-5.CrossRefGoogle Scholar
  72. 72.
    Aja-Fernández, S., Curiale, A. H., & Vegas-Sánchez-Ferrero, G. (2015). A local fuzzy thresholding methodology for multiregion image segmentation. Knowledge-Based Systems,83, 1–12.  https://doi.org/10.1016/j.knosys.2015.02.029.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyFrancis Xavier Engineering CollegeTirunelveliIndia
  2. 2.Department of Computer Science & EngineeringDr. Sivanthi Aditanar College of EngineeringTiruchendurIndia

Personalised recommendations