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Statistically Inhomogeneity Correction and Image Segmentation using Active Contours

Affiliations

  • Department of Electronics and Communication Engineering, C S Patel Institute of Technology, Charotar University of Science and Technology, Changa − 388421, Gujarat, India
  • Department of Electronics and Communication Engineering, Babaria Institute of Technology, Varnama, Vadodara − 391240, Gujarat, India

Abstract


Objective: To improve results on noisy image. Method: We have proposed Gaussian distribution density function based inhomogeneity correction method with active contour. We have implemented proposed method in MATLAB. Findings: It is shown through stimulated results that the noise and inhomogeneity both are reduced in segmented images. Experimental work is carried out for gray scale images only and it is proven that noise has been significantly reduced for different gray scale images. Applications: Proposed method can be applied for the segmentation and analysis of the various images.

Keywords

Active Contour, Gaussian Distribution, Gray Scale Image, Inhomogeneity, Segmentation.

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References


  • Vovk U, Pernus F, Likar B. A Review Method for Correction of intensity Inhomogeneity in MRI, IEEE Transactions on Medical Imaging. 2007 Mar; 26(3):405–21.
  • Simmons A, Tofts PS, Barker GJ, Arridge SR. Sources of Intensity Nonuniformity in Spin Echo Images at 1.5 T, Magnetic Resonancein Medicine. 1994; 32(1): 121–28. https://doi.org/10.1002/mrm.1910320117
  • Axel L, Costantini J, Listerud J. Intensity Correction in Surface Coil MR Imaging, AJR American Journal of Roentgenology. 1987; 148(3):418–20. https://doi.org/10.2214/ajr.148.2.418
  • Brey WW, Narayana PA. Correction for Intensity Fall off in Surface Coilmagnetic Resonance Imaging. Med. Phys.1988; 15(2):241–45. https://doi.org/10.1118/1.596255
  • Narayana PA, Brey WW, Kulkarni MV, Sievenpiper CL. Compensation For Surface Coil Sensitivity Variation in Magnetic Resonance Imaging, Magnetic Resoning Image. 1988; 6(3):271–74. https://doi.org/10.1016/0730725X(88)90401-8
  • Brinkmann BH, Manduca A, Robb RA. Optimized Homomorphic Unsharpmasking for MR Gray Scale Inhomogeneity Correction, IEEE Transaction Medical Imaging. 1998 Apr; 17(2):161–71. https://doi.org/10.1109/42.700729
  • Zhou LQ, Zhu YM, Bergot C, Laval-Jeantet AM, Bousson V, Laredo JD, Laval-Jeantet M. A Method of RadioFrequency Inhomogeneity Correction for Brain Tissue segmentation in MRI, Computerized Medical Imaging Graphics. 2001; 25(2):379–89. https://doi.org/10.1016/ S0895-6111(01)00006-4
  • Tomazevic D, Likar B, Pernus F. Comparative Evaluation of Retrospective Shading Correction Methods, Journal Microscology. 2002 Mar; 208(1):212–23. https://doi.org/10.1046/j.1365-2818.2002.01079.x
  • Dawant BM, Zijdenbos AP, Margolin RA. Correction of Intensity Variations in MR Images for Computer-Aided Tissues Classification, IEEE Transaction Medical Imaging.
  • Dec; 12(4):770–81. https://doi.org/10.1109/42.251128
  • Zhuge Y, Udupa JK, Liu J, Saha PK, Iwanage T. Scale Based Method For Correcting Background Intensity Variation in Acquired Images. In: Proceedings SPIE Medical Imaging. 2002; 4686(1):1103–11. https://doi.org/10.1117/12.467067
  • Bansal R, Staib LH, Peterson BS. Correcting Nonuniformities in MRI Intensities using Entropy Minimization based on an Elastic Model, Medical Image Computing and ComputerAssisted Intervention (MICCAI 2004); Saint-Malo: France, 2004. p. 78–86.
  • Pham DL, Prince JL. An Adaptive Fuzzy C-Means Algorithm for the Image Segmentation in the Presence of Intensity Inhomogeneities, Pattern Recognition Letter. 1998 Apr; 20(1):57–68. https://doi.org/10.1016/S01678655(98)00121–4
  • Sled JG, Zijdenbos AP, Evans AC. A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data, IEEE Transaction Medical Imaging.1998 Feb; 17(1):87–97. https://doi.org/10.1109/42.668698
  • Likar B, Viergever MA, Pernuˇs F. Retrospective Correction of MR Intensity Inhomogeneity by Information Minimization, IEEE Transaction Medical Imaging. 2001 Dec; 20(12):1398–410. https://doi.org/10.1109/42.974934
  • Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM. Magnetic Resonance Image Tissue Classification using a Partial Volume Model, Neuroimage. 2001 Nov; 13(5):856–76. https://doi.org/10.1006/ nimg.2000.0730
  • Styner M, Brechbuhler C, Szekely G, Gerig G. Parametric Estimate of Intensity Inhomogeneities Applied to MRI, IEEE Transaction Medical Imaging. 2000 Mar; 19(3):153– 65. https://doi.org/10.1109/42.845174
  • Dai L, Ding J, Yang J. Inhomogeneity-Embedded Active Contour for Natural Image Segmentation, Pattern Recognition. 2015 Mar, 48(2), pp. 2513-29. https://doi.org/10.1016/j.patcog.2015.03.001
  • Sujatha P, Sudha KK. Performance Analysis of Different Edge Detection Techniques for Image Segmentation, Indian Journal of Science and Technology. 2015 July; 8(14):1–6.
  • Kumar R, Arthanariee AM. Performance Evaluation and Comparative Analysis of Proposed Image Segmentation Algorithm, Indian Journal of Science and Technology. 2014 Jan; 7(1):39–47.
  • Berkeley Image Database. Date accessed: 10/10/2016. https://www2.eecs.berkeley.edu/Research/Projects/CS/ vision/bsds/.

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