Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images

Authors

  • Daniel Martomanggolo Wonohadidjojo Ciputra University

DOI:

https://doi.org/10.21512/comtech.v10i1.5632

Keywords:

performance comparison, Firefly algorithm, Cuckoo Search algorithm, cancer cells

Abstract

This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of the images were computed to validate the comparison. The image histograms of both methods show that the distribution of red, green, and blue channel is better than the histograms of original images. In terms of the four metrics, the method that uses FA shows higher performance than CS. In terms of image variance and entropy, the method using CS shows better results than FA. These results suggest that when the performance metrics used are MSE, PSNR, MSSIM, and FSIM, the method using FA is more suitable for cancer cells image enhancement and thresholding. However, when the variance and entropy of the images are used as the performance metrics, the method using CS is more suitable for cancer cells image enhancement and thresholding. Both methods will be useful to assist in the analysis of cancer cell images by the experts in the field.

Dimensions

Plum Analytics

References

Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2009). Digital image processing using Matlab. Gatesmark Publishing.

Gupta, A. K., Chauhan, S. S., & Shrivastava, M. (2016). Low contrast image enhancement technique by using fuzzy method. International Journal of Engineering Research and General Science, 4(2), 518-526.

Kaur, R., & Kaur, S. (2016). Comparison of contrast enhancement techniques for medical images. In 2016 Conference on Emerging Devices and Smart Systems (ICEDSS)

Kaur, T., & Sidhu, R. K. (2015). Performance evaluation of fuzzy and histogram based color image enhancement. Procedia Computer Science, 58, 470-477.

Maolood, I. Y., Al-Salhi, Y. E. A., & Lu, S. (2018). Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm. Open Medicine, 13(1), 374-383.

Naidu, M., & Kumar, P. R. (2017a). Adaptive cuckoo search based image segmentation. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 5(XII), 2481-2487.

Naidu, M. S. R., & Kumar, P. R. (2017b). Multilevel image thresholding for image segmentation by optimizing fuzzy entropy using firefly algorithm. International Journal of Engineering and Technology, 9(2), 472-488.

Naidu, M. S. R., Kumar, P. R., & Chiranjeevi, K. (2018). Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria engineering Journal, 57(3), 1643-1655.

Nandy, S., Yang, X. S., Sarkar, P. P., & Das, A. (2015). Color image segmentation by cuckoo search. Intelligent Automation & Soft Computing, 21(4), 673-685.

Pare, S., Bhandari, A. K., Kumar, A., & Singh, G. K. (2018). A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm. Computers & Electrical Engineering, 70(August), 476-495.

Patel, P. D., Trivedi, V. K., & Mishra, S. (2014). Image enhancement using fuzzy techniques: Survey and overview. International Journal of Science, Technology and Management, 3(12), 154-160.

Raja, N., Rajinikanth, V., & Latha, K. (2014). Otsu based optimal multilevel image thresholding using firefly algorithm. Modelling and Simulation in Engineering, 2014(2), 1-17.

Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—A comparative study. Journal of Computer and Communications, 7(3), 8-18.

Sesadri, U., Sankar, B. S., & Nagaraju, C. (2015). Fuzzy entropy based optimal thresholding technique for image enhancement. International Journal on Soft Computing, 6(2), 17-26.

Sharma, S., & Bhatia, A. (2015). Contrast enhancement of an image using fuzzy logic. International Journal of Computer Applications, 111(17), 14-20.

Sunny, N., Srikanth, M., & Eswar, K. (2017). Cancer cells detection using OTSU threshold algorithm. International Journal of Engineering Technology Science and Research, 4(12), 737-743.

Thomas, R. M., & John, J. (2017). A review on cell detection and segmentation in microscopic images. In 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

Vennila, K., & Thamizhmaran, K. (2017). Implementation of Multilevel Thresholding on Image using Firefly Algorithm. International Journal of Advanced Research in Computer Science, 8(3), 373-378.

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612. Wikimedia. (n.d.). Wikimedia commons. Retrieved from https://upload.wikimedia.org/wikipedia/commons/4/49/Breast_cancer_cells_%281%29.jpg

Wonohadidjojo, D. M. (2018). Comparative analysis of thresholding methods in cancer cells image processing. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 141-147.

Yang, X. S. (2010). Firefly algorithm, levy flights and global optimization. In M. Bramer, R. Ellis, & M. Petridis (Eds.), Research and development in intelligent systems XXVI - Incorporating applications and innovations in intelligent systems XVII. London: Springer.

Yang, X. S., & Debb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.

Yang, X. S., Deb, S., & Fong, S. (2014). Metaheuristic algorithms: Optimal balance of intensification and diversification. Applied Mathematics & Information Sciences, 8(3), 977-983.

Yang, X. S., & He, X. (2013). Firefly algorithm: Recent advances and applications. International Journal of Swarm Intelligence, 1(1), 36-50.

Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8), 2378-2386.

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Published

2019-06-30

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