ICTACT Journal on Image and Video Processing

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Image Segmentation Using Multi-Threshold Technique by Histogram Sampling


Affiliations
1 Department of Information Technology, Martin Luther Christian University, India
     

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The segmentation of digital images is one of the essential steps in image processing or a computer vision system. It helps in separating the pixels into different regions according to their intensity level. A large number of segmentation techniques have been proposed, and a few of them use complex computational operations. Among all, the most straightforward procedure that can be easily implemented is thresholding. In this paper, we present a unique heuristic approach for image segmentation that automatically determines multilevel thresholds by sampling the histogram of a digital image. Our approach emphasis on selecting a valley as optimal threshold values. We demonstrated that our approach outperforms the popular Otsu’s method in terms of CPU computational time. We demonstrated that our approach outperforms the popular Otsu’s method in terms of CPU computational time. We observed a maximum speed-up of 33.63× and a minimum speed-up of 10.21× on popular image processing benchmarks. To demonstrate our approach’s correctness in determining threshold values, we compute PSNR, SSIM, and FSIM values to compare with the values obtained by Otsu’s method. This valuation shows that our approach is comparable and better in many cases than well-known Otsu’s method.

Keywords

Digital Image Processing, Image Segmentation, Multilevel Thresholding, Histogram, Histogram Valley.
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  • B.S. Babu, S. Varadarajan and S. Swarnalatha, “Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption”, I-Manager’s Journal on Image Processing, Vol. 3, No. 2, pp. 1-18, 2016.

  • C. Li, R. Huang, Z. Ding, J.C. Gatenby, D.N. Metaxas and J.C. Gore, “A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities with Application to MRI”, IEEE Transactions on Image Processing, Vol. 20, No. 7, pp. 2007-2016, 2011.

  • F. Shi, Y. Fan, S. Tang, J.H. Gilmore, W. Lin and D. Shen, “Neonatal Brain Image Segmentation in Longitudinal MRI Studies”, Neuroimage, Vol. 49, No. 1, pp. 391-400, 2010.

  • R.P. Joseph, C.S. Singh and M. Manikandan, “Brain Tumor MRI Image Segmentation and Detection in Image Processing”, International Journal of Research in Engineering and Technology, Vol. 3, No. 1, pp. 1-5, 2014.

  • A. El-Sisi, “Design and Implementation Biometric Access Control System using Fingerprint for Restricted Area based on Gabor Filter”, The International Arab Journal of Information Technology, Vol. 8, No. 4, pp. 355-363, 2011.

  • B. Devereux, G. Amable and C.C. Posada, “An Efficient Image Segmentation Algorithm for Landscape Analysis”, International Journal of Applied Earth Observation and Geoinformation, Vol. 6, No. 1, pp. 47-61, 2004.

  • K. Yamaoka, T. Morimoto, H. Adachi, T. Koide and H.J. Mattausch, “Image Segmentation and Pattern Matching based FPGA/ASIC Implementation Architecture of Real-Time Object Tracking”, Proceedings of Asia and South Pacific Conference on Design Automation, pp. 1-6, 2006.

  • D. Kaur and Y. Kaur, “Various Image Segmentation Techniques: A Review”, International Journal of Computer Science and Mobile Computing, Vol. 3, No. 5, pp. 809-814,2014.

  • K.S. Fu and J. Mui, “A Survey on Image Segmentation”, Pattern Recognition, Vol. 13, No. 1, pp. 3-16, 1981.

  • W.X. Kang, Q.Q. Yang and R.P. Liang, “The Comparative Research on Image Segmentation Algorithms”, Proceedings of IEEE International Workshop on Education Technology and Computer Science, pp. 703-707, 2009.

  • J. Shi, J. Malik, “Normalized Cuts and Image Segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 888-905, 2000.

  • A. Bleau and L.J. Leon, “Watershed-Based Segmentation and Region Merging”, Computer Vision and Image Understanding, Vol. 77, No. 3, pp. 317-370, 2000.

  • S. Indira and A. Ramesh, “Image Segmentation using Artificial Neural Network and Genetic Algorithm: A Comparative Analysis”, Proceedings of IEEE International Conference on Process Automation, Control and Computing, pp. 1-6, 2011.

  • J. Wei and L. Chan, “An Image Segmentation Method based on Partial Differential Equation Models”, International Journal of Simulation–Systems, Science and Technology, Vol. 17, No. 36, pp. 1-13, 2019.

  • N. Otsu, “A Threshold Selection Method from Gray-Level Histograms”, IEEE Transactions on Systems, Man, and Cybernetics, Vol 9, No. 1, pp. 62-66, 1979.

  • J.N. Kapur, P.K. Sahoo and A.K. Wong, “A New Method for Gray-Level Picture Thresholding using the Entropy of the Histogram”, Computer Vision, Graphics, and Image Processing, Vol. 29, No. 3, pp. 273-285, 1985.

  • P. Sathya and R. Kayalvizhi, “Optimal Multilevel Thresholding using Bacterial Foraging Algorithm”, Expert Systems with Applications, Vol. 38, No. 12, pp. 15549-15564, 2011.

  • S. Kullback, “Information Theory and Statistics”, Courier Corporation, 1997.

  • P.Y. Yin, “Multilevel Minimum Cross Entropy Threshold Selection based on Particle Swarm Optimization”, Applied mathematics and Computation, Vol. 184, No. 2, pp. 503-513, 2007.

  • M.H. Horng, “Multilevel Minimum Cross Entropy Threshold Selection based on the Honey Bee Mating Optimization”, Expert Systems with Applications, Vol. 37, No. 6, pp. 4580-4592, 2010.

  • R. Manikandan and M. Ramkumar, “Sequential Pattern Mining on Chemical Bonding Database in the Bioinformatics Field”, Proceedings of International Conference on AIP, pp. 1-9, 2022.

  • D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar and V. Osuna, “A Multilevel Thresholding Algorithm using Electromagnetism Optimization”, Neurocomputing, Vol. 139, pp. 357-381, 2014.

  • A.K. Bhandari, V.K. Singh, A. Kumar and G.K. Singh, “Cuckoo Search Algorithm and Wind Driven Optimization based Study of Satellite Image Segmentation for Multilevel Thresholding using Kapurs Entropy”, Expert Systems with Applications, Vol. 41, No. 7, pp. 3538-3560, 2014.

  • A.K.M. Khairuzzaman and S. Chaudhury, “Multilevel Thresholding using Grey Wolf Optimizer for Image Segmentation”, Expert Systems with Applications, Vol. 86, pp. 64-76, 2017.

  • A.K. Bhandari, A. Kumar and G.K. Singh, “Modified Artificial Bee Colony based Computationally Efficient Multilevel Thresholding for Satellite Image Segmentation using Kapurs, Otsu and Tsallis Functions”, Expert Systems with Applications, Vol. 42, pp. 1573-1601, 2015.

  • H.F. Ng, “Automatic Thresholding for Defect Detection”, Pattern Recognition Letters, Vol. 27, No. 14, pp. 1644-1649, 2006.

  • P. Thangam, “Digital Image Processing”, Charulatha, 2010.

  • C. Poynton, “Digital Video and HD: Algorithms and Interfaces”, Elsevier, 2012.

  • A. Ismail and M. Marhaban, “A Simple Approach to Determine the Best Threshold Value for Automatic Image Thresholding”, Proceedings of IEEE International Conference on Signal and Image Processing Applications, pp. 162-166, 2009.

  • C.H. Li and C. Lee, “Minimum Cross Entropy Thresholding”, Pattern Recognition, Vol. 26, No. 4, pp. 617-625, 1993.

  • K. Tang, X. Yuan, T. Sun, J. Yang and S. Gao, “An Improved Scheme for Minimum Cross Entropy Threshold Selection based on Genetic Algorithm”, Knowledge-Based Systems, Vol. 24, No. 8, pp. 1131-1138, 2011.

  • D. Oliva, S. Hinojosa, V. Osuna-Enciso, E. Cuevas and G. Sanchez-Ante, “Image Segmentation by Minimum Cross Entropy using Evolutionary Methods”, Soft Computing, Vol. 23, No. 2, pp. 431-450, 2019.

  • M.H. Horng and R.J. Liou, “Multilevel Minimum Cross Entropy Threshold Selection based on the Firefly Algorithm”, Expert Systems with Applications, Vol. 38, No. 12, pp. 14805-14811, 2011.

  • M. Maitra and A. Chatterjee, “A Hybrid Cooperative-Comprehensive Learning based PSO Algorithm for Image Segmentation using Multilevel Thresholding”, Expert Systems with Application, Vol. 34, No. 2, pp. 1341-1350, 2008.

  • S.T. Welstead, “Fractal and Wavelet Image Compression Techniques”, SPIE Optical Engineering Press Bellingham, 1999.

  • Z. Wang, A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, “Image Quality Assessment: from Error Visibility to Structural Similarity”, IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612, 2004.

  • L. Zhang, L. Zhang, X. Mou and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment”, IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 2378-2386, 2011.


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  • Image Segmentation Using Multi-Threshold Technique by Histogram Sampling

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Authors

Sangyal Lama Tamang
Department of Information Technology, Martin Luther Christian University, India
Amit Gurung
Department of Information Technology, Martin Luther Christian University, India

Abstract


The segmentation of digital images is one of the essential steps in image processing or a computer vision system. It helps in separating the pixels into different regions according to their intensity level. A large number of segmentation techniques have been proposed, and a few of them use complex computational operations. Among all, the most straightforward procedure that can be easily implemented is thresholding. In this paper, we present a unique heuristic approach for image segmentation that automatically determines multilevel thresholds by sampling the histogram of a digital image. Our approach emphasis on selecting a valley as optimal threshold values. We demonstrated that our approach outperforms the popular Otsu’s method in terms of CPU computational time. We demonstrated that our approach outperforms the popular Otsu’s method in terms of CPU computational time. We observed a maximum speed-up of 33.63× and a minimum speed-up of 10.21× on popular image processing benchmarks. To demonstrate our approach’s correctness in determining threshold values, we compute PSNR, SSIM, and FSIM values to compare with the values obtained by Otsu’s method. This valuation shows that our approach is comparable and better in many cases than well-known Otsu’s method.

Keywords


Digital Image Processing, Image Segmentation, Multilevel Thresholding, Histogram, Histogram Valley.

References