Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized By Artificial Bee Colony Algorithm
Keywords:local contrast enhancement, cell, Intuitionistic Fuzzy Sets, Artificial Bee Colony algorithm
AbstractThe article presented the enhancement method of cells images. The first method used in the local contrast enhancement was Intuitionistic Fuzzy Sets (IFS). The proposed method is the IFS optimized by Artificial Bee Colony (ABC) algorithm. The ABC was used to optimize the membership function parameter of IFS. To measure the image quality, Image Enhancement Metric (IEM)
was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method.
Al-Azawi, M. A. (2013). Image thresholding using
histogram fuzzy approximation. International Journal of Computer Applications, 83(9), 36-40.
Atanassov, K. T. (2012). On Intuitionistic Fuzzy Sets theory.
New York: Springer.
Chaira, T. (2015). Medical image processing: Advanced
fuzzy set theoretic techniques. USA: CRC Press Taylor & Francis Group.
Chen, H. C., & Wang, W. J. (2009). Fuzzy-adapted linear
interpolation algorithm for image zooming. Signal Processing, 89(12), 2490-2502.
Deng, H., Sun, X., Liu, M., Ye, C., & Zhou, X. (2016).
Image enhancement based on Intuitionistic Fuzzy Sets theory. IET Image Processing, 10(10), 701-709.
Despi, I., Opris, D., & Yalcin, E. (2013). Generalised
Atanassov intuitionistic fuzzy sets. In Proceeding of the Fifth International Conference on Information, Process, and Knowledge Management.
Hasikin, K., & Isa, N. A. M. (2012, March). Enhancement
of the low contrast image using fuzzy set theory. In 14th International Conference on Computer Modelling and Simulation (UKSim), 2012, (pp. 371-376). IEEE.
Jaya, V. L., & Gopikakumari, R. (2013). IEM: A new image
enhancement metric for contrast and sharpness measurements. International Journal of Computer Applications, 79(9), 1-9.
Jayaram, B., Narayana, K. V., & Vetrivel, V. (2011). Fuzzy
inference system based contrast enhancement. In 7th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2011 and French Days on Fuzzy Logic and Applications, LFA 2011, 18th July, 2011.France: Aix-les-Bains.
Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), 108-132.
Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N.
Local Contrast Enhancement .... (Daniel M. Wonohadidjojo) 13 (2014). A comprehensive survey: Artificial Bee
Colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57.
Nayak, D. R. (2016). Image enhancement using intuitionistic
fuzzy reconstruction. International Journal of Advance Electrical and Electronics Engineering, 5(1), 2278-8948.
Nayak, D. R., & Bhoi, A. (2014). Image enhancement using
fuzzymorphology. Journal of Engineering Computers & Applied Sciences, 3(3), 22-26.
Selvy, P. T., Palasinany, V., & Purusothaman, T. (2011). Performance analysis of clustering algorithms in braintumor
detection of MR images. European Journal of Scientific Research, 62(3), 321-330.
Sharma, S., & Bhatia, A. (2015). Contrast Enhancement of
an Image using Fuzzy Logic. International Journal
of Computer Applications, 111(17), 15-20.
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: