Abstract
In this article, a fast context-sensitive threshold selection technique is presented to solve the image segmentation problems. In lieu of histogram, the proposed technique employs recently defined energy curve of the image. First, the initial thresholds are selected in the middle of two consecutive peaks on the energy curve. Then based on the cluster validity measure, the optimal number of potential thresholds and the bounds where the optimal value of each potential threshold may exist are determined. Finally, genetic algorithm (GA) is employed to detect the optimal value of each potential threshold from their respective defined bounds. The proposed technique incorporates spatial contextual information of the image in threshold selection process without loosing the benefits of histogram-based techniques. Computationally it is very efficient. Moreover, it is able to determine the optimal number of segments in the input image. To assess the effectiveness of the proposed technique, the results obtained are compared with four state-of-the-art methods cited in the literature. Experimental results on large number of images confirmed the effectiveness of the proposed technique.
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References
Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13, 3066–3091 (2013)
Ananthi, V.P., Balasubramaniam, P., Lim, C.P.: Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions. Pattern Recognit. 47, 3870–3880 (2014)
Chang, C.C., Wang, L.L.: A fast multilevel thresholding method based on lowpass and highpass filter. Pattern Recognit. Lett. 18, 1469–1478 (1997)
Davis, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1 2: 224–227 (1979)
Ghamisi, P., Couceiro, M.S., Benediktsson, J.A., Ferreira, M.F.N.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. 39, 12407–12417 (2012)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New York (1989)
Halkidi, M., Vazirgiannis, M.: Clustering validity assessment: finding the optimal partitioning of a data set. In: In: Proc. ICDM, California, USA (2001)
Huang, L.K., Wang, M.J.J.: Image thresholding by minimizing the measures of fuzziness. Pattern Recognit. 28, 41–51 (1995)
Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognit. 19(1), 41–47 (1986)
Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognit. 33, 1455–1965 (2000)
Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)
Patra, S., Gautam, R., Singla, A.: A novel context sensitive multilevel thresholding for image segmentations. Appl. Soft Comput. 23, 122–127 (2014)
Patra, S., Ghosh, S., Ghosh, A.: Histogram thresholding for unsupervised change detection of remote sensing images. Int. J. Remote Sens. 32(21), 6071–6089 (2011)
Sarkar, S., Das, S.: Multilevel image thresholding based on 2D histogram and maximum tsallis entropy a differential evolution approach. IEEE Trans. Image Process. 22(12), 4788–4797 (2013)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imag. 13(1), 146–165 (2004)
Siahi, M., Razjouyan, J., Khayat, O., Mansouri, A.A., Azimi, Z.: A multi-class bi-level thresholding method for accurate anthropometric measurements of scanned plantar images. Signal Image Video Process. 9(2), 295–304 (2015)
Singla, A., Patra, S.: A context sensitive thresholding technique for automatic image segmentation. Comput. Intell. Data Min. 2, 19–25 (2015)
Song, Y.Q., Liu, Z., Chen, J.M., Zhu, F., Xie, C.H.: Medical image segmentation based on non-parametric mixture models with spatial information. Signal Image Video Process. 6(4), 569–578 (2012)
Tobias, O.J., Seara, R.: Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans. Image Process. 11(12), 1457–1465 (2012)
Xiao, Y., Cao, Y., Yuan, Z.: Entropic image thresholding based on GLGM histogram. Pattern Recognit. Lett. 40, 47–55 (2014)
Yen, J.C., Chang, F.J., Chang, S.: A new criterion for automatic multilevel thresholding. IEEE Trans. Image Process. 4(3), 370–378 (1995)
Yimit, A., Hagihara, Y., Miyoshi, T., Hagihara, Y.: 2-D direction histogram based entropic thresholding. Neurocomputing 120(23), 287–297 (2013)
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The authors wish to thank the anonymous referees for their constructive criticism and valuable suggestions.
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Singla, A., Patra, S. A fast automatic optimal threshold selection technique for image segmentation. SIViP 11, 243–250 (2017). https://doi.org/10.1007/s11760-016-0927-0
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DOI: https://doi.org/10.1007/s11760-016-0927-0