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( 170 بازدید ) ( 51 دانلود )
نوع مقاله : Article
محورهای مقاله : Transactions on Computer Science & Engineering , Transactions on Electrical Engineering
عنوان لاتین : A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection
خلاصه لاتین مقاله : Edge detection in medical imaging is a significant task for object recognition of human organs and it is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: a global threshold is calculated using generalized Hill entropy. This global threshold is then used to separate the image into two parts called object and background. In a second step, a local threshold value is determined for each part of the image. The final edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared against Canny by using sets of medical images corrupted with various types of noise. We used Pratt’s Figure of Merit (PFOM) as a quantitative measure for an objective comparison. For four different types of noise, the proposed method provided better results than Canny for all four analyzed images (using PFOM mean values: for salt & pepper noise [proposed (60.240725) vs Canny (42.2394)]; using Gaussian noise [proposed (70.5913) vs Canny (34.8012); using Poisson noise [proposed (95.01405) vs Canny (66.778225)] and speckle noise [proposed (86.83815) vs Canny (79.76755)]). Experimental results indicate that the proposed algorithm displayed superior noise resilience and better edge detection than Canny, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images.
کلمات کلیدی لاتین : image edge detection, Hill entropy, thresholding, Canny edge detection, medical imaging
منابع : 1. S. E. Umbaugh, Computer Imaging: Digital Image Analysis and Processing, CRC Press, Boca Raton, 2005. 2.  R. C. Gonzalez, R. E. Woods, Digital Image Processing, Prentice Hall, New Jersey, 2008. 3. S. E. Umbaugh, Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIP tools, Second ed., CRC Press, Boca Raton, 2011. 4. S. Ullman,  R. Basri, Recognition by linear combinations of models, IEEE T Pattern Anal. 13 (1991) 962-1006. 5. V. Ferrari, L. Fevrier, F. Jurie, C. Schmid, Groups of adjacent contour segments for object detection, IEEE T Pattern Anal. 30 (2008) 36–51. 6. J. Malik, S. Belongie, T. Leung, J. Shi, Contour and texture analysis for image segmentation, Int J Comput Vision. 43 (2001) 7- 27. 7. M. Arbelaez, C. Maire, Fowlkes, J. Malik, Contour detection and hierarchical image segmentation, IEEE T Pattern Anal. 33,5 (2011) 898-916. 8. M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models, Int J Comput Vision. 1 (1988) 321–331. 9. P. Azad, T. Gockel, R. Dillmann, Computer Vision: Principles and Practice. Elektor International Media BV, Netherlands, 2008. 10. M. J. McAuliffe, F. M. Lalonde, D. McGarry, W. Gandler, K. Csaky, B. L. Trus, Medical Image Processing,  Analysis & Visualization in Clinical Research, Computer-Based Medical Systems, CBMS 2001, Proceedings 14th IEEE Symposium on Bethesda, MD, (2001), 381- 386. 11.T. T. Peng, Detection of femur fractures in X-ray images, MSc Thesis, National University of Singapore, 2002. 12. P. G. Nes, Fast multi-scale edge-detection in medical ultrasound signals, Signal Process. 92 (2012) 2394–2408. 13. S. Ontiverosa, J. A. Yagüea, R. Jiménezb, F. Broseda, Computer tomography 3D edge detection comparative for metrology applications, Procedia Engineering. 63 (2013)710–719. 14. H. Tang, E. X.Wua, Q. Y Ma, D. Gallagher, G. M Perera, T. Zhuang, MRI brain image segmentation by multi-resolution edge detection and region selection, Comput MedImagGrap.24 (2000) 349–357. 15. C. X Wang, L. Small, W. E. Snyde,; R. William; Edge detection in gated cardiac nuclear medicine images, Computer-Based Medical Systems, Proceedings IEEE Seventh Symposium, 10-12 Jun 1994, Winston-Salem, NC. 16. T. Gebäck, P. Koumoutsakos, Edge detection in microscopy images using curvelets, BMC Bioinformatics. (2009) 10-75. 17. A. Toprak,  I. Güler, Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter, Digit Signal Process. 17 (2007) 11–23. 18. P. Gravel, G. Beaudoin, J. A. De Guise, A method for modeling noise in medical images. IEEE T Med Imaging.23 (2004) 1221-1232. 19. G. Landi, E. L. Piccolomini, An efficient method for nonnegatively constrained Total Variation-based denoising of medical images corrupted by Poisson noise.Comput MedImagGrap. 36 (2012) 38– 46. 20. M. G. Sánchez, V. Vidal, G. Verdú, P. Mayo, F. Rodenas, Medical image restoration with different types of noise, Proceedings of the 34th IEEE Annual International Conference EMBS, (2012) 28 Aug. - 1 Sep., San Diego, California USA. 21. R. M. Gray, Entropy and Information Theory, First ed. Corrected, Springer, 2013. 22. I.  Shunsuke, Information theory for continuous system, World Scientific, Singapore, 1993. 23. C. E. Shannon, A mathematical theory of communication. The Bell System Technical Journal. 27 (1948) 379-423. 24. B. Léon. Science & Information Theory, Dover Publications. Second ed. 2004. 25. W. Pratt, Digital Image Processing: PIKS Scientific Inside. Wiley Interscience, 2007. 26. B. Singh, A. P. Singh, Edge detection in gray level images based on the Shannon entropy. Journal of Computer Science. 4 (2008)186–191. 27. C. Lopez-Molinaa, M. Galara, H. Bustincea, B. De Baets, On the impact of anisotropic diffusion on edge detection, Pattern Recogn. 47 (2014) 270–281. 28. F. Haoa, J. Shia, Z. Zhanga, R. Chenc, S. Zhud, Canny edge detection enhancement by general auto-regressionmodel and bi-dimensional maximum conditional entropy. Optik 125(2014) 3946–3953. 29. C. Lopez-Molina, B. De Baets, H. Bustince,A framework for edge detection based on relief functions,Inform Sciences. 278 (2014)127–140. 30. Q. Sun, Y. Hou, Q. Tan, C. Li, M. Liu, A robust edge detection method with sub-pixel accuracy. Optik. 125 (2014) 3449–3453. 31. K. Ray, Unsupervised edge detection and noise detection from a single image. Pattern Recogn. 46 (2013) 2067–2077. 32. C. Lopez-Molina, B. De Baets, H. Bustince, J. Sanz and E. Barrenechea, Multiscaleedge detection based on Gaussian smoothing and edge tracking, Knowl-Based Syst. 44 (2013) 101-111. 33. C. Lopez-Molina, B. De Baets and H. Bustince, Generating fuzzy edge images from gradient magnitudes, ComputVis Image Und. 115 (2011)1571–1580. 34. O. Verma, M. Hanmandlu, P. Kumar,S. Chhabra, A. Jindal, A novel bacterial foraging technique for edge detection, Pattern Recogn Lett. 32(2011) 1187-1196. 35. M. Setayesh, M. Zhang, M. Johnston. A novel particle swarm optimisation approach to detecting continuous, thin and smooth edges in noisy images.Information Sciences, 246 (2013) 28–51. 36. A. Jevtic, I. Melgar, D. Andina, Ant based edge linking algorithm, Proceedings of 35th Annual Conference of the IEEE Industrial Electronics Society (IECON 2009), Porto, Portugal, 3353–3358. 37. F. Y. Shih, S. Cheng, Adaptive mathematical morphology for edge linking, Inform Sciences. 167 (2004) 9–21. 38. L. Wei, L. Sheng, R. X. Yi, D. Peng, A new contour detection in mammogram using sequential edge linking, 2008 Second International Symposium on Intelligent Information Technology Application (IITA ’08), Vol. 1, Hong Kong, China, 197–200. 39. I. Sobel. Camera models and perception, Ph.D. Thesis, Stanford University, Stanford, CA, 1970. 40. J. M. S. Prewitt, Object enhancement and extraction. In Picture Processing and Psychopictorics, B. Lipkin and A. Rosenfeld, Eds. New York: Academic, (1970) 75-149. 41. R. M. Haralick, Digital step edges from zero crossing of second directional derivatives, IEEE T Pattern Anal. 6 (1984)58–68. 42. A. Huertas, G. Medioni, Detection of intensity changes with subpixel accuracy using laplacian-gaussian masks, IEEE T Pattern Anal. 8 (1986) 651–664. 43. J. Canny. A computational approach to edge detect. IEEE T Pattern Anal. 8 (1986) 679–698. 44. Y. Xiao,C. Zhiguo, Y. Jansong, Entropic image thresholding based on GLGM histogram, Pattern Recogn Lett. 40 (2014) 47–55. 45. S. E. El-Khamy, I. Galeb, N. A. El-Yamany, Fuzzy edge detection with minimum fuzzy entropy criterion, Proceedings 11th Mediterranean Electrotechnical Conference (IEEE), MELECON, 2002; 498-503. 46. M. A. El-Sayed. A New Algorithm Based Entropic Threshold for Edge Detection in Images.International Journal of Computer Science Issues, 8 (2011) 71-78. 47. A. Elaraby, H. El-Owny, M. Heshmat, M. Hassaballah, A. S. Abel Rardy, Edge detection of noisy medical images based mixed entropy, Computer Engineering and Intelligent Systems. 4 (2013) 97–06. 48. M. O. Hill, Diversity and Evenness: A unifying notation and its consequences. Ecology 54 (1973) 427–432. 49.  L. Jost, Entropy and diversity, Oikos. 113 (2006) 363–375. 50. H. B. A. Evangelista, S. M. Thomaz, R. S. Mendes, L. R. Evangelista, Generalized entropy indices to measure α- and β diversities of macrophytes. Braz J Phys. 39 (2009)396-401. 51.  T. Pun, A new method for gray-level picture thresholding using the entropy of histogram,Signal Process. 2 (1980) 223–237. 52.  J. N. Kapur, P. K. Sahoo, A. K. C. Wong, A new method for gray-level picture thresholdingusing the entropy of histogram, Comput Vis Graph Image Process. 29 (1985) 273–285. 53. N. Pal, S. Pal, Entropic thresholding. Signal Process. 16 (1989) 97–108. 54. C. I. Chang, K. Chen, J. Wang, M. Althouse, A relative entropy-based approach to image thresholding, Pattern Recogn. 29 (1994) 1275–1289. 55. A. S. Abutaleb. Automatic thresholding of gray-level pictures using two-dimensional entropy, Comput Vis Graph Image Process. 47 (1989) 22–32. 56. W. O. Fu, M. Johnston, M. Zhang. Figure of merit based fitness functions in genetic programming for edge detection, Lecture Notes in Computer Science. 7673 (2012)22-31.


نویسندگان مقاله :
نویسندهترتیب نویسندهدانشگاه / سازمان/ موسسهدانشگاه / سازمان/ موسسه ( لاتین )سمتپست الکترونیکیمدرک تحصیلی
Dr. David Moratal
(نویسنده مسئول)
2 Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain dmoratal@eln.upv.es 
Mr. Ahmed Elaraby 1 Department of Mathematics, Faculty of Science, South Valley University, Qena, Egypt   
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