Computerized Medical Imaging and Graphics
Volume 33, Issue 1 , Pages 72-82 , January 2009

Automatic segmentation and recognition of lungs and lesion from CT scans of thorax

  • Manish Kakar

      Affiliations

    • Department of Radiation Biology, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical centre, Oslo, Norway
    • Corresponding Author InformationCorresponding author. Tel.: +47 22781225; fax: +47 22781207.
    • ,
  • Dag Rune Olsen

      Affiliations

    • Department of Radiation Biology, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical centre, Oslo, Norway
    • Department of Medical Physics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical centre, Oslo, Norway
    • Department of Physics, University of Oslo, Oslo, Norway

Received 12 February 2008 ,Revised 3 October 2008 ,Accepted 30 October 2008.

References 

  1. Liu HH, Koch N, Starkschall G, Jacobson M, Forster K, Liao Z, et al. Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI. Part II. Margin reduction of internal target volume. Int J Radiat Oncol Biol Phys. 2004;60(Dec (5)):1473–1483
  2. Koch N, Liu HH, Starkschall G, Jacobson M, Forster K, Liao Z, et al. Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI. Part I. Correlating internal lung motion with skin fiducial motion. Int J Radiat Oncol Biol Phys. 2004;60(Dec (5)):1459–1472
  3. Julesz B. Nonlinear and cooperative processes in texture perception. In: Theoretical approaches in neurobiology. Cambridge, MA: MIT press; 1981;p. 93–108
  4. Serre T, Wolf L, Bileschi S, Riesenhuber M, Poggio T. Robust object recognition with cortex-like mechanisms. IEEE Trans Pattern Anal Mach Intell. 2007;29(Mar (3)):411–426
  5. Reeves AP, Kostis WJ. Computer aided diagnosis for lung cancer. Radiol Clin North Am. 2000;38:497–509
  6. Armato SG, Giger ML, MacMohan H. Automated detection of lung nodules in CT scans: preliminary results. Med Phys. 2001;28:1552–1561
  7. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging. 2001;20:595–604
  8. McNitt-Gray MF, Har EM, Wyckoff N, Sayre JW, Goldin JG. A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys. 1999;26:880–888
  9. Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based upon CT evaluation. Radiology. 2000;217:251–256
  10. Zagers H, Vrooman HA, Aarts NJM, Stolk J, Kool LJS, Dijkman JH, et al. Assessment of the progression of emphysema by quantitative analysis of spirometrically gated computed tomography images. Invest Radiol. 1996;31:761–767
  11. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, Thelen M. Automatic detection and quantification of ground-glass opacities on high resolution CT using multiple neural networks: comparison with a density mask. Am J Roentgenol. 2000;175:1329–1334
  12. Uppaluri R, Hoffman EA, Sonka M, Hartley PG, Hunninghake GW, McLennan G. Computer recognition of regional lung disease patterns. Am J Respir Crit Care Med. 1999;160:648–654
  13. Delorme S, Keller-Reichenbecher MA, Zuna I, Schlegel W, van Kaik G. Usual interstitial pneumonia—quantitative assessment of high-resolution computed tomography findings by computer-assisted texture based image analysis. Invest Radiol. 1997;32:566–574
  14. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, et al. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys. 2003;30:2440–2454
  15. Shyu CR, Brodley CE, Kak AC, Kosaka A, Aisen AM, Broderick LS. ASSERT: a physician-in-the-loop content based retrieval system for HRCT image databases. Comput Vis Image Underst. 1999;75:111–132
  16. Udupa JK, Saha PK. Fuzzy connectedness and image segmentation. Proc IEEE. 2003;91(10):1649–1669
  17. Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition. Med Phys. 1993;20(Jul (4)):1033–1048
  18. Farrokhnia F, Jain AK. A multi-channel filtering approach to texture segmentation. In: Proceedings CVPR ‘91, IEEE conference on computer vision and pattern recognition. Maui, HI, USA. 1991;p. 364–370
  19. Cohen I, Cohen LD, Ayache NJ. Using deformable surfaces to segment 3-d images and infer differential structures. CVGIP: Image Underst. 1992;56(2):135–263
  20. Worring M, Smeulders AWM, Staib LH, Duncan JS. Parameterized feasible boundaries in gradient vector fields. Comput Vis Image Underst. 1996;63(Jan (1)):135–144
  21. Kakar M, Nystrom H, Nøttrup TJ, Bruland OS, Olsen DR. Automated texture based CT segmentation by Gabor filtering and Fuzzy Clustering. Med Phys. 2006;3(6):2170
  22. Tuceryan M, Jain AK. Texture analysis. In:  Chen CH,  Pau LF,  Wang PSP editor. Handbook of pattern recognition and computer vision. 2nd edn.. World Scientific Publishing Co.; 1998;p. 207–248
  23. Field DJ. Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am A. 1987;4(12):2379
  24. Turner MR. Texture segmentation by Gabor functions. Biol Cybern. 1986;55:71–82
  25. Jain AK, Farrokhnia F. Unsupervised texture segmentation using Gabor filters. In: International conference on systems, man and cybernetics. Los Angeles, CA, USA. 1990;p. 14–19
  26. De Valois RL, Albrecht DG, Thorell LG. Spatial frequency selectivity of cells in macaque visual cortex. Vision Res. 1982;22(5):545–559
  27. Farrokhnia F, Jain AK. A multi-channel filtering approach to texture segmentation. In: Computer Vision and Pattern Recognition, 1991. Proceedings CVPR ‘91, IEEE Computer Society Conference. 1991;p. 364–370
  28. Bezdek J, Keller J, Krishnapuram R, Pal NR. Fuzzy models and algorithms for pattern recognition and image processing. Boston: Kluwer Academy Publishers; 1999;
  29. Pal NR, Bezdek JC. On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst. 1995;3(3):370–379
  30. Goldberg DE. Genetic Algorithms in search, optimization and machine learning. New York: Addison-Wesley; 1989;
  31. Hall LO, Ozyurt IB, Bezdek JC. Clustering with a genetically optimized approach. IEEE Trans Evol Comput. 1999;3(2):103–112
  32. Hathaway RJ, Bezdek JC. Optimization of clustering criteria by reformulation. IEEE Trans Fuzzy Syst. 1995;3(2):241–245
  33. Back T, Kursawe F. Evolutionary algorithms for fuzzy logic: a brief overview. In: Proceedings of information processing and management of uncertainity in knowledge-based systems, vol. 2. 1994;p. 659–664
  34. Whitley D. The GENITOR algorithm and selection pressure: why ranking based allocation of reproductive trials is best. Proc IGGA. 1989;3:116–121
  35. Baker JE. Reducing bias and inefficiency in the selection algorithm. Proc ICGA. 1987;2:14–21
  36. Schaffer JD, Caruna RA, Eshelman LJ, Das R. A study of control parameters affecting online performance of Genetic Algorithms for function optimization. In: Proceedings of the third international conference on Genetic Algorithms. 1989;p. 51–60
  37. Linda G, Shapiro , George C, Stockman . Computer vision. Upper Saddle River, NJ: Prentice Hall; 2001;
  38. Gozalez RC, Woods RE. Digital image processing. MA: Addison-Wesley Publishing Company; 1993;p. 505
  39. Weiss Gary M. Mining with rarity: a unifying framework. SIGKDD Explor Newsl. 2004;7–9
  40. Weiss GM, Provost F. Learning when training data are costly: the effect of class distribution on tree induction. J Artif Intell Res. 2003;19(Oct):315–354
  41. Prati RC, Batista GEAPA, Monard MC. Class imbalances versus class overlapping: an analysis of a learning system behavior. Lect Notes Comp Sci. 2004;2972:312–321
  42. Chawla NV, Boywer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;(Jun (16)):321
  43. Aksoy S, Haralick RM. Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recogn Lett. 2001;22(Apr (5)):563–582
  44. Vishawanathan SVM, Smola AJ, Murty MN. In:  Fawcett T,  Mishra N editor. SimpleSVM. Washington DC, USA: AAAI Press; 2003;p. 760–767
  45. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2(2):121–167
  46. Udupa JK, LeBlanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, et al. A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph. 2006;30(Mar (2)):75–87
  47. Huang J, Ling CX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng. 2005;17(Mar (3)):299–310
  48. Kohavi R, Provost F. Applications of data mining to electronic commerce. Data Min Knowl Discov. 2001;5(1–2):5–10

PII: S0895-6111(08)00118-3

doi: 10.1016/j.compmedimag.2008.10.009

Computerized Medical Imaging and Graphics
Volume 33, Issue 1 , Pages 72-82 , January 2009

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