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Automatic Segmentation of the Ribs, the Vertebral Column, and the Spinal Canal in Pediatric Computed Tomographic Images

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Abstract

We propose methods to perform automatic identification of the rib structure, the vertebral column, and the spinal canal in computed tomographic (CT) images of pediatric patients. The segmentation processes for the rib structure and the vertebral column are initiated using multilevel thresholding and the results are refined using morphological image processing techniques with features based on radiological and anatomical prior knowledge. The Hough transform for the detection of circles is applied to a cropped edge map that includes the thoracic vertebral structure. The centers of the detected circles are used to derive the information required for the opening-by-reconstruction algorithm used to segment the spinal canal. The methods were tested on 39 CT exams of 13 patients; the results of segmentation of the vertebral column and the spinal canal were assessed quantitatively and qualitatively by comparing with segmentation performed independently by a radiologist. Using 13 CT exams of six patients, including a total of 458 slices with the vertebra from different sections of the vertebral column, the average Hausdorff distance was determined to be 3.2 mm with a standard deviation (SD) of 2.4 mm; the average mean distance to the closest point (MDCP) was 0.7 mm with SD = 0.6 mm. Quantitative analysis was also performed for the segmented spinal canal with three CT exams of three patients, including 21 slices with the spinal canal from different sections of the vertebral column; the average Hausdorff distance was 1.6 mm with SD = 0.5 mm, and the average MDCP was 0.6 mm with SD = 0.1 mm.

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References

  1. Yao J, O’Connor SD, Summers RM: Automated spinal column extraction and partitioning. In Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, Arlington, VA, 2006, pp 390–393

  2. Hahn M, Beth T: Balloon based vertebra separation in CT images. In Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, Los Alamitos, CA, 2004, pp 310–315

  3. McCracken TO (Ed): New Atlas of Human Anatomy. London, UK: Constable, 2001

  4. Delorme S, Petit Y, de Guise JA, Labelle H, Aubin C-É, Dansereau J: Assessment of the 3-D reconstruction and high-resolution geometrical modeling of the human skeletal trunk from 2-D radiographic images. IEEE Trans Biomed Eng 50(8):989–998, 2003

    Article  CAS  PubMed  Google Scholar 

  5. Kadoury S, Cheriet F, Laporte C, Labelle H: A versatile 3D reconstruction system of the spine and pelvis for clinical assessment of spinal deformities. Med Biol Eng Comput 45(6):591–602, 2007

    Article  PubMed  Google Scholar 

  6. Boisvert J, Cheriet F, Pennec X, Labelle H, Ayache N: Geometric variability of the scoliotic spine using statistics on articulated shape models. IEEE Trans Med Imag 27(4):557–568, 2008

    Article  CAS  Google Scholar 

  7. Archip N, Erard PJ, Petersen ME, Haefliger JM, Germond JF: A knowledge-based approach to automatic detection of the spinal cord in CT images. IEEE Trans Med Imag 21(12):1504–1516, 2002

    Article  Google Scholar 

  8. Karangelis G, Zimeras S: 3D segmentation method of the spinal cord applied on CT data. Computer Graphics Topics 14(1/2002):28–29, 2002

    Google Scholar 

  9. Lee CC, Chung PC: Recognizing abdominal organs in CT images using contextual neural network and fuzzy rules. In Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, 2000, pp 1745–1748

  10. Rangayyan RM, Deglint HJ, Boag GS: Method for the automatic detection and segmentation of the spinal canal in computed tomographic images. J Electronic Imag 15(3):033007, 2006

    Article  Google Scholar 

  11. Qatarneh SM, Crafoord J, Kramer EL, Maguire Jr, GQ, Brahme A, Noz ME, Hyödynmaa S: A whole body atlas for segmentation and delineation of organs for radiation therapy planning. Nucl Instrum Methods Phys Res A471(1):160–164, 2001

    Google Scholar 

  12. Qatarneh SM, Noz ME, Hyödynmaa S, Maguire GQ, Kramer EL, Crafoord J: Evaluation of a segmentation procedure to delineate organs for use in construction of a radiation therapy planning atlas. Int J Med Inform 69(1):39–55, 2003

    Article  PubMed  Google Scholar 

  13. Ehrhardt J, Handels H, Malina T, Strathmann B, Plötz W, Pöppl SJ: Atlas-based segmentation of bone structures to support the virtual planning of hip operations. Int J Med Inform 64(2):439–447, 2001

    Article  CAS  PubMed  Google Scholar 

  14. Ehrhardt J, Handels H, Plötz W, Pöppl SJ: Atlas-based recognition of the anatomical structures and landmarks and the automatic computation of the orthopedic parameters. Methods Inf Med 43(4):391–397, 2004

    CAS  PubMed  Google Scholar 

  15. D'Haese P, Niermann KJ, Cmelak AJ, Joshi P, Dawant B: Automatic segmentation of the human spinal canal using an intelligent digital atlas. Int J Radiat Oncol Biol Phys 60:S579–S580, 2004

    Article  Google Scholar 

  16. Wang H, Bai J, Zhang Y: A relative thoracic cage coordinate system for localizing the thoracic organs in chest CT volume data. In: Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China, 2005, pp 3257–3260

  17. Staal J, Ginneken BV, Viergever MA: Automatic rib segmentation in CT data. In: Sonka M, Kakadiaris IA, Kybic J Eds. Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, volume 3117/2004. Berlin: Springer, 2004, pp 193–204

    Google Scholar 

  18. Lee CC, Chung PC, Tsai HM: Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules. IEEE Trans Inf Technol Biomed 7(3):208–217, 2003

    Article  PubMed  Google Scholar 

  19. Rangayyan RM, Vu RH, Boag GS: Automatic delineation of the diaphragm in computed tomographic images. J Digit Imaging 21:S134–S147, 2008

    Article  PubMed  Google Scholar 

  20. Banik S, Rangayyan RM, Boag GS: Delineation of the pelvic girdle in computed tomographic images. In Proceedings of the 21st IEEE Canadian Conference on Electrical and Computer Engineering, Niagara Falls, Ontario, Canada, 2008, pp 179–182

  21. Deglint HJ, Rangayyan RM, Ayres FJ, Boag GS, Zuffo MK: Three-dimensional segmentation of the tumor in computed tomographic images of neuroblastoma. J Digit Imaging 20(1):72–87, 2007

    Article  Google Scholar 

  22. Vu RH, Rangayyan RM, Deglint HJ, Boag GS: Segmentation and analysis of neuroblastoma. J Franklin Inst 344(3–4):257–284, 2007

    Article  Google Scholar 

  23. Banik S, Rangayyan RM, Boag GS. Landmarking of computed tomographic images to assist in segmentation of abdominal tumors caused by neuroblastoma. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 2008, pp 3126–3129

  24. Rangayyan RM, Banik S, Boag GS: Automatic segmentation of the ribs and the vertebral column in computed tomographic images of pediatric patients. In Proceedings of CARS 22nd International Congress and Exhibition: Computer Assisted Radiology and Surgery, volume 3(1), Barcelona, Spain, 2008, pp S42–S44

  25. Rangayyan RM: Biomedical Image Analysis, Boca Raton, FL: CRC, 2005

    Google Scholar 

  26. Zadeh LA: Fuzzy sets. Inf Control 8:338–353, 1965

    Article  Google Scholar 

  27. Bezdek JC: Fuzzy Models for Pattern Recognition: Methods that search for structures in data, New York, NY: IEEE, 1992

    Google Scholar 

  28. Udupa JK, Samarasekera S: Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation. Graph Models Image Process 58(3):246–261, 1996

    Article  Google Scholar 

  29. Rosenfeld A: The fuzzy geometry of image subsets. Pattern Recogn Lett 2(5):311–317, 1984

    Article  Google Scholar 

  30. Vincent L: Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Trans Image Process 2(2):176–201, 1993

    Article  CAS  PubMed  Google Scholar 

  31. Dawant BM, Zijdenbos AP: Image segmentation. In: Sonka M, Fitzpatrick JM Eds. Handbook of Medical Imaging, Volume 2: Medical Image Processing and Analysis. Bellingham, WA: SPIE, 2000, pp 71–127

    Chapter  Google Scholar 

  32. Goutsias J, Batman S: Morphological methods for biomedical image analysis. In: Sonka M, Fitzpatrick JM Eds. Handbook of Medical Imaging, Volume 2: Medical Image Processing and Analysis. Bellingham, WA: SPIE, 2000, pp 175–272

    Chapter  Google Scholar 

  33. Bloch I: Fuzzy connectivity and mathematical morphology. Pattern Recogn Lett 14:483–488, 1993

    Article  Google Scholar 

  34. Hough PVC: A method and means for recognizing complex patterns. US Patent 3,069,654, 1962

  35. Duda RO, Hart PE: Use of the Hough transform to detect lines and curves in pictures. Commun ACM 15(1):11–15, 1972

    Article  Google Scholar 

  36. Rote G: Computing the minimum Hausdorff distance between two point sets on a line under translation. Inf Process Lett 38:123–127, 1991

    Article  Google Scholar 

  37. Xu J, Chutatape O, Chew P: Automated optic disk boundary detection by modified active contour model. IEEE Trans Biomed Eng 54(3):473–482, 2007

    Article  PubMed  Google Scholar 

  38. Huttenlocher DP, Klanderman GA, Rucklidge WJ: Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–853, 1993

    Article  Google Scholar 

  39. Saha PK, Chaudhuri BB, Majumder DD: A new shape preserving parallel thinning algorithm for 3D digital images. Pattern Recogn 30(12):1939–1955, 1997

    Article  Google Scholar 

  40. Mategrano VC, Petasnick J, Clark J, Bin AC, Weinstein R: Attenuation values in computed tomography of the abdomen. Radiology 125:135–140, 1977

    CAS  PubMed  Google Scholar 

  41. Phelps ME, Hoffman EJ, Ter-Pogossian MM: Attenuation coefficients of various body tissues, fluids and lesions at photon energies of 18 to 136 keV. Radiology 117:573–583, 1975

    CAS  PubMed  Google Scholar 

  42. Michalski JM: Neuroblastoma. In: Perez CA, Brady LW, Halperin EC, Schmidt-Ullrich RK Eds. Principles and Practice of Radiation Oncology. 4th edition. Philadelphia, PA: Lippincott Williams and Wilkins, 2004, pp 2247–2260

    Google Scholar 

  43. Canny J: A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698, 1986

    Article  Google Scholar 

  44. Ware JH, Mosteller F, Delgado F, Donnelly C, Ingelfinger JA: P values. In: Bailar III, JC, Mosteller F Eds. Medical uses of statistics, 2nd edition. Boston, MA: NEJM Books, 1992, pp 181–200

    Google Scholar 

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Acknowledgment

This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.

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Correspondence to Rangaraj M. Rangayyan.

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Banik, S., Rangayyan, R.M. & Boag, G.S. Automatic Segmentation of the Ribs, the Vertebral Column, and the Spinal Canal in Pediatric Computed Tomographic Images. J Digit Imaging 23, 301–322 (2010). https://doi.org/10.1007/s10278-009-9176-x

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  • DOI: https://doi.org/10.1007/s10278-009-9176-x

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