Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy
DOI:
https://doi.org/10.26555/jiteki.v6i2.19168Keywords:
Thresholding, Tsallis entropy, Moth-Flame algorithm, Performance evaluation, ValidationAbstract
In the current era, image evaluations play a foremost role in a variety of domains, where the processing of digital images is essential to identify vital information. The image multi-thresholding is a vital image pre-processing field in which the available digital image is enhanced by grouping similar pixel values. Normally, the digital test images are available in RGB/greyscale format and the appropriate processing methodology is essential to treat the images with a chosen methodology. In the proposed approach, Tsallis Entropy (TE) supported multi-level thresholding is planned for the benchmark greyscale imagery of dimension 512x512x1 pixels using a chosen threshold values (T=2,3,4,5). This work suggests the possible Cost Value (CV) that can be considered during the optimization search and the proposed work is executed by considering the maximization of the TE as the CV. The entire thresholding task is executed using Moth-Flame Algorithm (MFA) and the accomplished results are validated based on the image quality measures of various thresholds. The attained result with MFO is better compared to the result of CS, BFO, PSO, and GA.
References
B. Muthu, C.B. Sivaparthipan, G. Manogaran, R. Sundarasekar, S. Kadry, A. Shanthini, and A. Dasel, “IOT Based Wearable Sensor for Diseases Prediction and Symptom Analysis in Healthcare Sector,†Peer-to-peer networking and applications, pp.1-12, 2020. DOI: https://doi.org/10.1007/s12083-019-00823-2
F. Kulakov, S. Kadry, G. Alferov, and A. Sharlay, “Bilateral Remote Control Over Space Manipulators,†In AIP Conference Proceedings, vol. 2040, no. 1, pp. 150015, 2018. DOI: https://doi.org/10.1063/1.5079218
A. Madi, O.K. Zein, and S. Kadry., “On the Improvement of Cyclomatic Complexity Metric,†International Journal of Software Engineering and Its Applications, vol.7, no.2, pp.67-82, 2013.
A. Bakiya, K. Kamalanand, V. Rajinikanth, R.S. Nayak, and S. Kadry, “Deep Neural Network Assisted Diagnosis Of Time-Frequency Transformed Electromyograms,†Multimedia Tools and Applications, vol.79, no.15, pp.11051-11067, 2020. DOI: https://doi.org/10.1007/s11042-018-6561-9
L.A.Lund, Z. Omar, I. Khan, S. Kadry, S. Rho, I.A., Mari, and K.S.Nisar, “Effect Of Viscous Dissipation in Heat Transfer of MHD Flow of Micropolar Fluid Partial Slip Conditions: Dual Solutions and Stability Analysis,†Energies, vol.12, no.24, pp.4617, 2019. DOI: https://doi.org/10.3390/en12244617
S. Cotsakis, S. Kadry, G. Kolionis, and A. Tsokaros, “Asymptotic Vacua With Higher Derivatives,†Physics Letters B, vol.755, pp.387-392, 2016. DOI: https://doi.org/10.1016/j.physletb.2016.02.036
K. Smaili, T. Kadri, and S. Kadry, “A Modified-Form Expressions for the Hypoexponential Distribution,†British Journal of Mathematics & Computer Science, vol.4, no.3, pp.322-332, 2014. DOI: https://doi.org/10.9734/BJMCS/2014/6317
A. Abou Jaoude, E.T. Khaled, S. Kadry, and H. Noura, “Prognostic Model for Buried Tubes,†Journal of Mathematics and Statistics, vol.6, no.2, pp.116-124, 2010. DOI: https://doi.org/10.3844/jmssp.2010.116.124
V. Rajinikanth, S.C. Satapathy, N. Dey, and R. Vijayarajan,., “DWT-PCA Image Fusion Technique to Improve Segmentation Accuracy in Brain Tumor Analysis,†Lecture Notes in Electrical Engineering,†vol.471, pp.453-462, 2018. DOI: https://doi.org/10.1007/978-981-10-7329-8_46.
V. Rajinikanth, N.S.M. Raja, and S.C. Satapathy, “Robust Color Image Multi-Thresholding Using Between-Class Variance and Cuckoo Search Algorithm,†Advances in Intelligent Systems and Computing, vol.433, pp. 379-386, 2016. DOI: https://doi.org/10.1007/978-81-322-2755-7_40
S.L. Fernandes, V. Rajinikanth, and S. Kadry, “A Hybrid Framework to Evaluate Breast Abnormality Using Infrared Thermal Images,†IEEE Consumer Electronics Magazine,†vol.8, no.5, pp.31-36, 2019. https://doi.org/10.1109/MCE.2019.2923926
S.L.Fernandes, U.J.Tanik, V. Rajinikanth, and K.A. Karthik, “A Reliable Framework for Accurate Brain Image Examination and Treatment Planning Based on Early Diagnosis Support For Clinicians,†Neural Computing and Applications, vol.32, no.20, pp.15897-15908, 2020. DOI: https://doi.org/10.1007/s00521-019-04369-5
S.C. Satapathy and V.Rajinikanth, “Jaya Algorithm Guided Procedure to Segment Tumor From Brain MRI,†Journal of Optimization, vol. 2018, ID 3738049, 2018. DOI: https://doi.org/10.1155/2018/3738049
T.V. Shree, K. Revanth, N.S.M. Raja, and V. Rajinikanth, “A Hybrid Image Processing Approach to Examine Abnormality in Retinal Optic Disc,†Procedia Computer Science, vol.125, pp.157-164, 2018. DOI: https://doi.org/10.1016/j.procs.2017.12.022
V. Rajinikanth, N.S.M. Raja, and N. Dey, A Beginner’s Guide to Multilevel Image Thresholding, 1st Edition, CRC Press, 2020. DOI: https://doi.org/10.1201/9781003049449-1
S. Kadry, G. Alferov, G. Ivanov, A. Sharlay “Almost Periodic Solutions of First-Order Ordinary Differential Equations,†Mathematics, vol.6, no.9, pp.171, 2018. DOI: https://doi.org/10.3390/math6090171
N.S.M. Raja, K.S. Manic, and V. Rajinikanth, “Firefly Algorithm with Various Randomization Parameters: An Analysis,†Lecture Notes in Computer Science, vol.8297, pp.110-121, 2013. DOI: https://doi.org/10.1007/978-3-319-03753-0_11
V. Rajinikanth, K. Kamalanand,C. Emmanuel, and B. Thayumanavan, Biomedical Signal and Image Examination with Entropy-Based Techniques, 1st edition, CRC Press, 2020. DOI: https://doi.org/10.1201/9780367477240-1
A. Ahilan, G. Manogaran,C. Raja, S.Kadry, S.N.Kumar, C.A. Kumar,T. Jarin, S. Krishnamoorthy, P.M. Kumar, G.C., Babu, and N.S. Murugan, “Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images,†IEEE Access, vol.7, pp.89570-89580, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2891632
S. Agrawal, R. Panda, S. Bhuyan, and B.K. Panigrahi, “Tsallis Entropy Based Optimal Multilevel Thresholding Using Cuckoo Search Algorithm,†Swarm and Evolutionary Computation, vol.11, pp.16-30., 2013. DOI: https://doi.org/10.1016/j.swevo.2013.02.001
Y. Zhang, and L. Wu, “Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy Via an Artificial Bee Colony Approach,†Entropy, vol.13, no.4, pp.841-859, 2011. DOI: https://doi.org/10.3390/e13040841
Y. Wang, G. Zhang, and X. Zhang, “Multilevel Image Thresholding Using Tsallis Entropy and Cooperative Pigeon-inspired Optimization Bionic Algorithm,†Journal of Bionic Engineering, vol.16, no.5, pp.954-964, 2019. DOI: https://doi.org/10.1007/s42235-019-0109-1
K.S. Manic, R.K. Priya, and V. Rajinikanth, “Image multithresholding based on Kapur/Tsallis entropy and firefly algorithm. Indian Journal of Science and Technology,†vol. 9, no.12, pp.89949, 2016. DOI: https://doi.org/10.17485/ijst/2016/v9i12/89949
H.S. Naji Alwerfali, M.A. Al-qaness, M. Abd Elaziz, M., A.A. Ewees,D. Oliva, and S. Lu, “Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy,†Entropy, vol.22, no.3, pp.328, 2020. DOI: https://doi.org/10.3390/e22030328
A.K.M. Khairuzzaman and S. Chaudhury, “Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation,†International Journal of Applied Metaheuristic Computing (IJAMC), vol.8, no.4, pp.58-83, 2017. DOI: https://doi.org/10.4018/IJAMC.2017100104
M. Abd El Aziz, A. A. Ewees, and A.E. Hassanien, “Whale Optimization Algorithm and Moth-Flame Optimization for Multilevel Thresholding Image Segmentation,†Expert Systems with Applications, vol.83, pp.242-256, 2017. DOI: https://doi.org/10.1016/j.eswa.2017.04.023
H. Jia, J. Ma, and W. Song, “Multilevel Thresholding Segmentation for Color Image Using Modified Moth-Flame Optimization,†IEEE Access, vol.7, pp.44097-44134, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2908718
A.E. Hassanien, T. Gaber, U. Mokhtar, and H. Hefny, “An Improved Moth Flame Optimization Algorithm Based on Rough Sets for Tomato Diseases Detection,†Computers and Electronics in Agriculture, vol.136, pp.86-96, 2017. DOI: https://doi.org/10.1016/j.compag.2017.02.026
C. Tsallis, “Possible Generalization of Boltzmann-Gibbs Statistics,†Journal of statistical physics, vol.52, no.1-2, pp.479-487, 1988. DOI: https://doi.org/10.1007/BF01016429
V. Rajinikanth and M.S. Couceiro, “Optimal Multilevel Image Threshold Selection Using A Novel Objective Function,†In Information Systems Design and Intelligent Applications, pp. 177-186, 2015. DOI: https://doi.org/10.1007/978-81-322-2247-7_19
S. Mirjalili, “Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm,†Knowledge-based systems, vol.89, pp.228-249, 2015. DOI: https://doi.org/10.1016/j.knosys.2015.07.006
V. Rajinikanth, N. Dey, E. Kavallieratou, and H. Lin, “Firefly Algorithm-Based Kapur’s Thresholding and Hough Transform to Extract Leukocyte Section from Hematological Images,†In Applications of Firefly Algorithm and its Variants, pp. 221-235, Springer, Singapore., 2020. DOI: https://doi.org/10.1007/978-981-15-0306-1_10
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