Multimedia Tools and Applications

, Volume 78, Issue 6, pp 6487–6511 | Cite as

Contrast Enhancement of Medical Images through Adaptive Genetic Algorithm (AGA) over Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

  • S. MuniyappanEmail author
  • P. Rajendran


Assessment of images after processing is a significant step for determining how good the images are being analyzed. Quality of image is usually estimated with the help of image quality metrics. Unfortunately, most of the commonly used metrics cannot sufficiently portray the visual aspect of the enhanced image. In this proposed system, an approach for medical image enhancement is presented. Here adaptive genetic algorithm is proposed for medical image contrast enhancement. Initially, the chromosomes having gene value of the image gray levels have been generated. After that the fitness function will be calculated for each generated chromosome based on the image edge and their overall intensity values. The selected best chromosomes which have the high fitness value will be given to crossover and mutation operation. In GA the adaptive property is introduced by including adaptive crossover and mutation operations. The proposed method is compared with two different types of optimization algorithms such as Genetic algorithm (GA) and Particle swarm optimization (PSO) that ensure accuracy and quality of medical images in proposed adaptive genetic algorithm (AGA). The experimental solutions are got with the help of metrics like PSNR, SDME, MSE, SSIM, MSSIM, AD, MD, NAE, PSO and SC which proves the proposed algorithm, produces better results as compared to the existing algorithms.


Adaptive Genetic Algorithm (AGA) Particle Swarm Optimization (PSO) Genetic Algorithm (GA) Image Enhancement(IE) Medical Image(MI) Mutation Peak Signal-To-Noise Ratio (PSNR) Second Derivative based Measure of Enhancement (SDME) Mean Squared Error (MSE) Structural Similarity Index (SSIM) Mean Structural Similarity Index (MSSIM) Average Difference (AD) Maximum Difference (MD) Normalized Absolute Error (NAE) Structural Content (SC) Histogram equalization(HE) Wireless Capsule Endoscopy(WCE) 



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018
corrected publication August/2018

Authors and Affiliations

  1. 1.Anna UniversityGuindyIndia
  2. 2.Department of Computer Science and EngineeringKnowledge Institute of TechnologyTamil NaduIndia

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