Luhana Aracelli González Zarza
ABSTRACT
Radiographic images suffer from contrast problems. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used local contrast enhancement algorithm that can solve this problem. There are no fixed values of CLAHE input parameters that always produce the best output images in terms of contrast enhancement for radiographic images, so to find the parameters that satisfy the optimal condition in a radiography image we use the Speed-constrained Modified Multi-objective Particle Swarm Optimization (SMPSO). Since the output is a set of solutions (called Pareto Set), in this paper we propose to use and compare a posteriori decision methods with a multi-objective approach, in order to select the solutions of the Pareto front that are aligned with the visual criteria of an expert. Three decision making methods were used (utility function-based, distance-based and a fuzzy method). In order to validate which decision methods gave better results, they were evaluated by a medical expert, concluding that methods based on utility functions and distance obtain better quality information. The Fuzzy method was discarded because it selects solutions with a lot of distortion.
- L. G. More, M. A. Brizuela, H. L. Ayala, D. P. Pinto-Roa, J. L. V. Noguera, "Parameter tuning of CLAHE based on multi-objective optimization to achieve different contrast levels in medical images", Image Processing (ICIP) 2015 IEEE International Conference on, pp. 4644--4648, 2015 Google Scholar
Digital Library
- S.M. Pizer, E.P. Amburn, J.D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B.T.H. Romeny, J.B. Zimmerman. "Adaptive histogram equalization and its variations", Comput. Vision Graph. Image Process. 39 (1987) 355--368 Google ScholarDigital Library
- X. Wang, B. S. Wong, and T. Chen Guan. "Image enhancement for radiography inspection", Proc. SPIE 5852, Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, (12 April 2005) Google ScholarCross Ref
- A. J. Nebro, J. J. Durillo, J. Garcia-Nieto, C. A. C. Coello, F. Luna, E. Alba. "SMPSO: A new PSO-based metaheuristic for multi-objective optimization", Proc. IEEE Symp. Comput. Intell. Multi-Criteria Decision-Making, pp. 66--73, Mar./Apr. 2009 Google ScholarCross Ref
- W. Edwards and F. Barron, Smarts and smarter: Improved simple", Org. Behavior Human Decision Processes, vol. 60, pp. 306--325, 1994.Google ScholarCross Ref
- K. Yoon and C. Hwang. "TOPSIS (technique for order preference by similarity to ideal solution)-a multiple attribute decision making, w: Multiple attribute decision making-methods and applications, a state-of-the-at survey," ed: Berlin: Springer Verlag, 1981.Google Scholar
- R. O. Parreiras, J. H. R. D. Maciel, and J. A. Vasconcelos. "The A Posteriori Decision in Multiobjective Optimization Problems With Smarts", Promethee II, and a Fuzzy Algorithm", IEEE Transactions on Magnetics, vol. 42, no. 4, april 2006.Google Scholar
- G. Bradski, "Opencv," Dr. Dobb's Journal of Software Tools, 2000.Google Scholar
- J. Durillo, A. Nebro, and E. Alba. "The jmetal framework for multi-objective optimization: Design and architecture," in Evolutionary Computation (CEC), 2010 IEEE Congress on, pp. 1--8, July 2010.Google Scholar
- R. Roberts and P. Goodwin. "Weight approximations in multi-attribute decision models." Journal of Multi-Criteria Decision Analysis 2002; 11:291--303Google ScholarCross Ref
Index Terms
- Framework to select an improved radiographic image using Speed-constrained modified particle swarm optimization
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