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Comparison of Global Histogram-based Thresholding Methods that Applied on Wound Images

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Sümeyya İlkin, Fatma Selin Hangişi, Suhap Şahin
10.5120/ijca2017914002

Sümeyya İlkin, Fatma Selin Hangişi and Suhap Şahin. Comparison of Global Histogram-based Thresholding Methods that Applied on Wound Images. International Journal of Computer Applications 165(9):23-28, May 2017. BibTeX

@article{10.5120/ijca2017914002,
	author = {Sümeyya İlkin and Fatma Selin Hangişi and Suhap Şahin},
	title = {Comparison of Global Histogram-based Thresholding Methods that Applied on Wound Images},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {9},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {23-28},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume165/number9/27603-2017914002},
	doi = {10.5120/ijca2017914002},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Image processing is used effectively in the medical field because of the convenience it brings to human life. Incorrect data which obtained during image processing operations in the medical area can have serious consequences. Therefore, the selection of the thresholding method used as pre-image processing step is also vital. In this study, comparison of image thresholding methods was performed. The selected maximum entropy, minimum error threshold, Otsu's method, simple threshold selection minimum and simple threshold selection mean methods were tested on a special data set consisting of wound images. The methods were compared using the values obtained from the selected metrics results. According to the comparison results, the most successful methods is determined as Otsu's method and maximum entropy methods for dermatologic images which have different resolutions and image qualities. The success rates of the methods are presented in the paper using the metrics results obtained.

References

  1. Vala, H. J., and Astha B. 2013. A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering & Technology 2, no. 2 (2013), 387-389.
  2. Huang, Z.-K., and Kwok-Wing C. 2008. A new image thresholding method based on Gaussian mixture model. Applied Mathematics and Computation 205, no. 2 (2008), 899-907.
  3. Huang, D.S. 1996 Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China.
  4. Bhandari, A. K., Vineet, K. S., Anil K., and Girish K. S. 2014. Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Systems with Applications 41, no. 7 (2014), 3538-3560.
  5. Bezdek, J.C., Keller, J., Krisnapuram, R., and Nikhil P. 2006 Fuzzy models and algorithms for pattern recognition and image processing. Vol. 4. Springer Science & Business Media.
  6. Xiaohui, H., Gao, S., and Gao, X. 1999. A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Transactions on Medical Imaging 18, no. 9 (1999), 787-794.
  7. Li, Z., and Chuancai L. 2009. An image thresholding method based on standard deviation. In Computational Sciences and Optimization (CSO 2009) IEEE International Joint Conference on, vol. 1, 835-838.
  8. Torres-Sánchez, J., Francisca L.-G., and Peña, J. M. 2015. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture 114 (2015), 43-52.
  9. Huang, S., Majid, A., and Maher, A. S.-A. 2006. An edge based thresholding method. IEEE International Conference on In Systems, Man and Cybernetics, (SMC'06), vol. 2, 1603-1608.
  10. Chang, S., Grace, B. Y., and Martin V. 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on image processing 9, no. 9 (2000), 1532-1546.
  11. Chan, F. H. Y., Francis K. L., and Hui Z. 1998. Adaptive thresholding by variational method. IEEE Transactions on Image Processing 7, no. 3 (1998), 468-473.
  12. Zhou, Y., Zixue C., and Lei J. 2015. Threshold selection and adjustment for online segmentation of one-stroke finger gestures using single tri-axial accelerometer. Multimedia Tools and Applications 74, no. 21 (2015), 9387-9406.
  13. Neves, A. A., Silva, E. J., Roter, J. M., Belladona, F. G., Alves, H. D., Lopes, R. T., Paciornik, S., and De‐Deus, G. A. 2015. Exploiting the potential of free software to evaluate root canal biomechanical preparation outcomes through micro‐CT images. International endodontic journal 48, no. 11 (2015), 1033-1042.
  14. Kiwanuka, F. N., and Michael H. F. W. 2016. Automatic attribute threshold selection for morphological connected attribute filters. Pattern Recognition 53 (2016), 59-72.
  15. Vala, H. J., and Astha B. 2013. A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering & Technology 2, no. 2 (2013), 387-389.
  16. Yan, F., Hong, Z., and Kube, C. R. 2005. A multistage adaptive thresholding method. Pattern recognition letters 26, no. 8 (2005), 1183-1191.
  17. Cuevas, E., and Humberto, S. 2013. A comparison of nature inspired algorithms for multi-threshold image segmentation. Expert Systems with Applications 40, no. 4 (2013), 1213-1219.
  18. Rider, T. W., and Calvard, S. 1978. Picture thresholding using an iterative selection method. IEEE Transactions on Systems Man and Cybernetics 8, no. 8 (1978), 630–632.
  19. Prewitt, J.M.S., and Mendelsohn, M.L. 1966. The analysis of cell images. Annals of the New York Academy of Sciences 128, no. 1 (1966), 1035–1053.
  20. Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man and Cybernetics 9, no. 1 (1979) 62–66.
  21. Tsai, W. 1985. Moment-preserving thresholding: a new approach. Computer Vision Graphics and Image Processing 29, no. 3 (1985), 377–393.
  22. Kapur, J.N., Sahoo, P.K., and Wong, A.K.C. 1985. A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram. Computer vision, graphics, and image processing 29, no. 3 (1985), 273-285.
  23. Qi, C. 2014. Maximum entropy for image segmentation based on an adaptive particle swarm optimization. Appl. Math 8, no. 6 (2014), 3129-3135.
  24. Meethongjan, K., and Dzulkifli, M. Maximum Entropy-based Thresholding algorithm for Face image segmentation. http://comp.utm.my/publications/files/2013/04/An-efficient-Automatic-Face-segmentation-Algorithm-using-Maximum-Entropy-Based-Thresholding.pdf.
  25. Chi, Z., Hong, Y., and Tuan, P. 1996 Fuzzy algorithms: with applications to image processing and pattern recognition. Vol. 10. World Scientific.
  26. Kittler, J., and Illingworth, J. 1986. Minimum error thresholding. Pattern recognition 19, no. 1 (1986), 41-47.
  27. Martí, J., Benedí, J.M., Mendonça, A.M. and Serrat, J. eds., 2007 Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings (Vol. 4477). Springer Science & Business Media.
  28. Gonzales, R. C., and Woods, R. E. 2008 Digital Image Processing. 3rd Ed. Prentice Hall.
  29. Raju, P., Ratna, D., and Neelima, G. 2012. Image segmentation by using histogram thresholding. International Journal of Computer Science Engineering and Technology 2 (2012), 776-779.
  30. Tan, K. S., and Nor, A. M. I. 2011. Color image segmentation using histogram thresholding–Fuzzy C-means hybrid approach. Pattern Recognition 44, no. 1 (2011), 1-15.
  31. Bhargavi, K., and Jyothi, S. A Survey on Threshold Based Segmentation Technique in Image Processing. International Journal of Innovative Research and Development 3, no. 12 (2014), 234-239.

Keywords

Thresholding, Global Histogram-Based Thresholding, Image Segmentation, Medical Image Processing, Dermatologic Images.