DIRECTIONAL THRESHOLDING ALGORITHM FOR GRAY SCALE IMAGE SEGMENTATION
DOI:
https://doi.org/10.29284/ijasis.4.1.2018.23-29Keywords:
Image segmentation, grayscale image, codebook, directional thresholding, misclassification error, Jaccard indexAbstract
The main aim of the image segmentation is to change the representation of the image so that the boundaries and objects in an image can be easily observed. In this study, a novel algorithm is proposed for the image segmentation using gray scale images. The codebook algorithm is used in the proposed approach for optimal multidirectional thresholding approach. The background and foreground pixel values are stored in the codebook. It uses standard deviations along the four directions to search the background and foreground pixels iteratively. The misclassification error and Jaccard index are used to measure the system efficiency. The mean of misclassification error is 95.80% with standard deviation of 1.91 and the mean of Jaccard index is 92.36% with standard deviation of 5.6. These measures shows the efficacy of the proposed system.
References
Z. Ye, Z. Hu, H. Wang, and W. Liu, “A Image Thresholding Method Based on Binary Coded Ant Colony Algorithm”, International Workshop on Intelligent Systems and Applications, 2010, pp. 1-4.
F. Qin, B. Fang, and H. Zhao, “Traffic sign segmentation and recognition in scene images”, Chinese Conference on Pattern Recognition, 2010, pp. 1-5.
L. Djerou, H. Dehimi, N. Khelil, and M. Batouche, “Using the BPSO algorithm in image segmentation for dynamic thresholding”, International on Conference on Bio-Inspired Computing, 2009, pp. 1-6.
H. Tanaka, “Threshold correction of document image binarization for ruled-line extraction”, International Conference on Document Analysis and Recognition, 2009, pp. 541-545.
S. Ghanbari, J.C. Woods, H.R. Rabiee, and S.M. Lucas, “Wavelet domain binary partition trees for image segmentation”, International Workshop on Content-Based Multimedia Indexing, 2008, pp. 302-307.
Z. Li, Z.X. Cai, J. Xie, and X.P. Ren, “Road markings extraction based on threshold segmentation”, International Conference on Fuzzy Systems and Knowledge Discovery, 2012, pp. 1924-1928.
L. Fang, Y. Zou, F. Dong, S. Sun, and B. Lei, “Image thresholding based on maximum mutual information”, International Congress on Image and Signal Processing, 2014, pp. 403-409.
M.J. Islam, S. Basalamah, M. Ahmadi, and M.A. Sid-Ahmed, “Capsule image segmentation in pharmaceutical applications using edge-based techniques”, IEEE International Conference on Electro/Information Technology, 2011, pp. 1-5.
M. Dhieb, and M. Frikha, “A multilevel thresholding algorithm for image segmentation based on particle swarm optimization”, International Conference of Computer Systems and Applications, 2016, pp. 1-7.
P. Sankaran, and V.K. Asari, “Adaptive thresholding based cell segmentation for cell-destruction activity verification”, IEEE Applied Imagery and Pattern Recognition Workshop, 2006, pp. 14-14.
Y.R. Huang, and C.M. Kuo, “Image segmentation using edge detection and region distribution”, International Congress on Image and Signal Processing, 2010, pp. 1410-1414.
S. Jun, “A fast self-adapt target image segmentation algorithm”, Chinese Control Conference, 2008, pp. 500-504.
F. PirahanSiah, S.N.H.S. Abdullah, and S. Sahran, “Comparison single thresholding method for handwritten images segmentation”, International Conference on Pattern Analysis and Intelligence Robotics, 2011, pp. 92-96.
D.P. Song, “Optimal threshold control of empty vehicle redistribution in two depot service systems”, IEEE Transactions on Automatic Control, Vol. 50, No. 1, 2005, pp. 87-90.
W. Kaihua, and B. Tao, “Optimal threshold image segmentation method based on genetic algorithm in wheel set online measurement”, International Conference on Measuring Technology and Mechatronics Automation, 2011, pp. 799-802.
B. Sun, S.J. Song, C. Wu, and H. Zhang, “Adjustable entropy funtion method for misclassification minimization problems”, International Conference on Machine Learning and Cybernetics, 2009, pp. 1556-1564.
A.K. Gupta, and N. Sardana, “Significance of clustering coefficient over jaccard index”, International Conference on Contemporary Computing, 2015, pp. 463-466.
H. R. Tizhoosh, “Image thresholding using type II fuzzy sets,” Pattern Recognition, Vol. 38, 2005, pp. 2363–2372.
N. Otsu, “A threshold selection method from gray level histograms,” IEEE Transactions on System, Man, Cybernetics, Vol. SMC-9, 1979, pp. 62-66.
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