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Effective object segmentation based on physical theory in an MR image

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Abstract

Object recognition is usually processed based on region segmentation algorithm. Region segmentation in the IT field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective object segmentation method based on R2 information within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the liver region and by setting up feature points of R2 map as seed points for region growing to enable region segmentation even when the border line was not clear. As a result, an average area difference of 7.5 %, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.

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References

  1. Arie Kaufman (1991) “Introduction to Volume Visualization.,” A. Kaufman(ed.), Computer Society Press

  2. Baba N, Ichse N, Tanaka T (1996) Image area extraction of biological objects from a thin section image by statistical texture analysis. Electron Microse 45:298–306

    Article  Google Scholar 

  3. Bhalla M, Naidich DP, McGuinness G, Gruden JF, Leitman BS, McCauley DI (1996) Diffuse lung disease : assessment with helical CT -preliminary observations of the role of maximum and minimum intensity projection images. Radiology 200:341–347

    Article  Google Scholar 

  4. Calhoun PS, Kuszyk BS, Heath DG, Carley JC, Fishman EK (1999) Three-dimensional volume rendering of spiral CT data : theory and method. RadioGraphics 19:745–764

    Article  Google Scholar 

  5. Catmull E, Rom R (1974) “A class of local interpolating splines,” Computer Aided Geometric Design, pp.317–326

  6. Chande B, Dutta Majumder D (1988) “A note on the graylevel co-occurrence matrix in threshold selection”, Signal Processing, 15(2)

    Google Scholar 

  7. Damadian RV (1971) Science 171:1151–1153

    Article  Google Scholar 

  8. de Graaf RA, Brown PB, McIntyre S, Nixon TW, Behar KL, Rothman DL (2006) Magn Reson Med 56:386–394

    Article  Google Scholar 

  9. Drebin RA, Carpenter L, Hanrahan P (1988) Volume rendering. Comput Graph 22(4):65–74

    Article  Google Scholar 

  10. Eddie Y, Ng K, Chen Y (2006) Segmentation of the breast thermogram: improved boundary detection with the modified snake algorithm. J Mech Med Biol 6(2):123–136

    Article  Google Scholar 

  11. Fernandez-Seara MA, Techawiboonwong A, Detre JA et al (2006) MR susceptometry for measuring global brain oxygen extraction. Magn Reson Med 55:967.73

    Article  Google Scholar 

  12. Gavrila DM, Daimler-Benz AG (1998) “Multi-feature Hierarchical Template Matching Using Distance Transforms”,IEEE International Conference on Pattern Recognition

  13. Haacke EM, Ayaz M, Khan A et al (2007) Establishing a baseline phase behavior in magnetic resonance imaging to determine normal vs abnormal iron content in the brain. J Magn Reson Imaging 26:256.64

    Article  Google Scholar 

  14. Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999) Magnetic resonance imaging:physical principles and sequence design. Wiley, USA, pp 129–133

    Google Scholar 

  15. Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999) Magnetic resonance imaging:physical principles and sequence design. Wiley, USA, pp 118–123

    Google Scholar 

  16. Haacke EM, Lai S, Reichenbach JR et al (1997) In vivo measurement of blood oxygen saturation using magnetic resonance imaging: a direct validation of the blood oxygen level-dependent concept in functional brain imaging. Human Brain Mapp 5:341–46

    Article  Google Scholar 

  17. Hanrahan P (1990) Three-pass affine transforms for volume rendering. Computer Graph 24(5):71–78

    Article  Google Scholar 

  18. Hemachande S, Verma A, Arora S, Panigrahi PK (2007) Locally adaptive block thresholding method with continuity constraint. Pattern Recogn Lett 28:119–124

    Article  Google Scholar 

  19. Hu J, Yu Y, Juhasz C et al (2008) MRsusceptibility weighted imaging (SWI) complements conventional contrast enhanced T1 weighted MRI in characterizing brain abnormalities of Sturge-Weber syndrome. J Magn Reson Imaging 28:300.07

    Google Scholar 

  20. Kang DJ, In Kweon S (1999) A fast and stable snake algorithm for medical images. Pattern Recogn Lett 20(10):1069

    Article  Google Scholar 

  21. Kang CC, Wang WJ (2007) A novel edge detection method based on maximization of the objective function. Pattern Recogn 40(2):609–618

    Article  MathSciNet  Google Scholar 

  22. Kass M, Witkin A (1988) Demetri terzopoulos active contour models. Int J Comput Vis 1:321–331

    Article  Google Scholar 

  23. Kingsley PB (1999) Concepts in Magn Reson 11:29–49

    Article  Google Scholar 

  24. Koopmans PJ, Manniesing R, Niessen WJ, et al. (2008) MR venography of the human brain using susceptibility weighted imaging at very high field strength.MAGMA 21:149 .58. Epub 2008 Jan 11

  25. Levoy M (1988) Volume rendering, display of surface from volume data. IEEE Comput Graph Appl 8(5):29–37

    Article  Google Scholar 

  26. Li W, Zhou C, Zhang Z (2004) Segmentation of the body of the tongue based on the improved snake algorithm in traditional Chinese medicine. In Proc. of the 5th World Congress on Intelligent Control and Automation, pp. 15–19

  27. Muerle JL, Allen DC (1968) Experimental evaluation of a technique for automatic segmentation of objects in complex scenes. IPPR, Thopmson

    Google Scholar 

  28. Otsu N (1979) A thresholding selection method from gray-scale histogram. In IEEE Transactions on System, Man, and Cybernetics 9(1):62–66

    Article  MathSciNet  Google Scholar 

  29. Pippa Storey, PhD, Alexis A. Thompson, Christine L. Carqueville, BA, John C. Wood, R. Andrew de Freitas, and Cynthia K. Rigsby (2007) R2* Imaging of Transfusional Iron Burden at 3T and Comparison with 1.5T, J Magn Reson Imaging 25, pp.540–547

  30. Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE (1990) “Contrast-limited adaptive histogram equalization: speed and effectiveness,” Visualization in Biomedical Computing, pp.337-345

  31. Rafael C. Gonzalez and Paul Wintz (1993) Digital Image Processing, 3rd Ed., Addison-Wesley

  32. Reichenbach JR, Venkatesan R, Schillinger DJ et al (1997) Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology 204:272–77

    Article  Google Scholar 

  33. Remy-Jardin M, Remy J, Artaud D, Deschildre F, Duhamel A (1996) Diffuse infiltrative lung disease : Clinical value of sliding-thin-slab maximum intensity projection CT scans in the detection of mild micronodular patterns. Radiology 200:333–339

    Article  Google Scholar 

  34. Rose J-L, Revol-Muller C, Almajdub M, Chereul E, Odet C (2007) “Shape prior integrated in an automated 3d region growing method,” in Image Processing, 2007. ICIP 2007. IEEE International Conference on, vol. 1, pp. 53–56

  35. Rother C, Kolmogorov V, Blake A (2004) GrabCut: Interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314

    Article  Google Scholar 

  36. Shrager RI, Weiss GH, Spence RGS (1998) NMR Biomed 11:297–305

    Article  Google Scholar 

  37. Thomas M. Murphy, Mark Math, and Leif H. Finkel (2003) "Curvature Covariation as a Factor of Perceptual Salience," International IEEE EMBS CNECI, pp. 16–19

  38. Umut Orguner, Fredrik Gustafsson (2007) “Statistical Characteristics of Harris Corner Detector”, IEEE/SP 14th Workshop, pp.571-575

  39. Unser M (1995) Texture classification and segmentation for using wavelet frames. IEEE Trans 4(11):1549–1560

    Google Scholar 

  40. Williams D, Shah M (1992) A fast algorithm for active contours and curvature estimation. Comput Vis Graph Image Process: Image Underst 55:14–25

    MATH  Google Scholar 

  41. Zabih R, Kolmogorov V (2004) Spatially coherent clustering using graph cuts. In Proc Comput Vision Pattern Recognit 2:437–444

    Google Scholar 

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Acknowledgments

This research was supported by MSIP (the Ministry of Science, ICT and Future Planning), Korea, under the IT-CRSP (IT Convergence Research Support Program) (NIPA-2013-H0401-13-1001), supervised by the NIPA (National IT Industry Promotion Agency); and by a grant from the Korea Healthcare Technology R&D Project of the Ministry of Health and Wealth of the Republic of Korea (A080369).

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Authors and Affiliations

  1. Department of Computer Science, Gachon University, Sujung-Gu, Seongnam, Gyunggi-Do, South Korea

    Sung-Jong Eun & Taeg-Keun Whangbo

  2. Department of Medical Sciences, Seoul National University, Jongno-Gu, Seoul, South Korea

    Hyeonjin Kim

  3. Department of Computer Science, Yonsei University, Seoul, South Korea

    Jung-Wook Park

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  1. Sung-Jong Eun
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  2. Hyeonjin Kim
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  4. Taeg-Keun Whangbo
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Correspondence to Taeg-Keun Whangbo.

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Eun, SJ., Kim, H., Park, JW. et al. Effective object segmentation based on physical theory in an MR image. Multimed Tools Appl 74, 6273–6286 (2015). https://doi.org/10.1007/s11042-014-2089-9

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