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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
Article
  • 114 Downloads

Abstract

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.

Keywords

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) 

Notes

References

  1. 1.
    Al-Ameen Z, Sulong G, Johar MGM (2012) Enhancing the Contrast of CT Medical Images by Employing a Novel Image Size Dependent Normalization Technique. Int J Bio-Sci Bio-Technol 4(3):63–68Google Scholar
  2. 2.
    Chaira T (2012) A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set. Appl Soft Comput 12:125–1266CrossRefGoogle Scholar
  3. 3.
    Chang D-C, Wu W-R (1998) Image Contrast Enhancement Based on a Histogram Transformation of Local Standard Deviation. IEEE Trans Med Imaging 17(4):518–531MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cheung WK, Gujral DM, Shah BN, Chahal NS, Bhattacharyya S, Cosgrove DO, Eckersley RJ, Harrington KJ, Senior R, Nutting CM, and Tang M-X (In Press) Attenuation Correction And Normalisation For Quantification Of Contrast Enhancement In Ultrasound Images Of Carotid ArteriesGoogle Scholar
  5. 5.
    Gatos B, Pratikakisand I, Perantonis SJ (2006) Adaptive degraded document image binarization. Pattern Recogn 39(3):317–327CrossRefGoogle Scholar
  6. 6.
    Gouhar GK, El-Hariri MA, Lotfy WE (2011) Malignant breast tumours: Correlation of apparent diffusion coefficient values using diffusion-weighted images and dynamic contrast-enhancement ratio with histologic grading. Egypt J Radiol Nucl Med 42:451–460CrossRefGoogle Scholar
  7. 7.
    Goyaland S, Baghla S (2011) Region Growing Adaptive Contrast Enhancement of Medical MRI Images. J Glob Res Comput Sci 2(7):93–95Google Scholar
  8. 8.
    Huang K-Q, Wang Q, Wu Z-Y (2006) Natural color image enhancement and evaluation algorithm basedon human visual system. Comput Vis Image Underst 103(1):52–63CrossRefGoogle Scholar
  9. 9.
    Huang K, Wang Q, Wu Z-y (2004) Color image enhancement and evaluation algorithm based on human visual system, IEEE In preceding of International Conference on Acoustics, Speech, and Signal Processing, Nanjing, China, 3, 721–724Google Scholar
  10. 10.
    Huang K, Wu Z, Wang Q (2005) Image enhancement based on the statistics of visual representation. Image Vis Comput 23(1):51–57CrossRefGoogle Scholar
  11. 11.
    Jagatheeswari P, Suresh Kumar S, Rajaram M (2009) Contrast Enhancement for Medical Images Based on Histogram Equalization Followed by Median Filter, In proceedings of the International Conference on Man-Machine Systems, Batu Ferringhi, Penang, Malaysia, 1–4Google Scholar
  12. 12.
    Khellaf A, Beghdadi A, Dupoisot H (1991) Entropic Contrast Enhancement. IEEE Trans Med Imaging 10(4):589–592CrossRefGoogle Scholar
  13. 13.
    Kwok NM, Ha QP, Liu DK, Fang G (2006) Intensity-Preserving Contrast Enhancement for Gray-Level Images using Multi-objective Particle Swarm Optimization, In proceeding of the IEEE International Conference on Automation Science and Engineering, 7–10Google Scholar
  14. 14.
    Lan X, Ma AJ Yuen PC (2014) Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation, In Proceedings of the IEEE conference on computer vision and pattern recognition, 1194–1201Google Scholar
  15. 15.
    Lan X, Ma AJ, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826–5841MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lan X, Zhang S Yuen PC (2016) Robust Joint Discriminative Feature Learning for Visual Tracking, In IJCAI, 3403–3410Google Scholar
  17. 17.
    Lan X, Yuen PC Chellappa R (2017) Robust MIL-Based Feature Template Learning for Object Tracking, In AAAI, pp. 4118–4125Google Scholar
  18. 18.
    Lan X, Zhang S, Yuen PC, Chellappa R (2017) Learning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-based Tracker, IEEE Transactions on Image ProcessingGoogle Scholar
  19. 19.
    Li B, Meng MQ-H (2012) Wireless capsule endoscopy images enhancement via adaptive contrast diffusion. J Vis Commun Image Represent 23:222–228CrossRefGoogle Scholar
  20. 20.
    Li S, Yan Z (2015) Study on Auto Parts Suppliers Composition Selection Based on Adaptive Genetic Algorithm, In proceedings of IEEE International Conference on Grey Systems and Intelligent Services (GSIS), 521–527Google Scholar
  21. 21.
    Medukonduru P, Joshi MA (2015) Enhancement of Low Contrast Biometric Images using Genetic Algorithm, In proceedings of International Conference on Industrial Instrumentation and Control (ICIC), 28–30Google Scholar
  22. 22.
    Meylan L, Susstrunk S (2006) High Dynamic Range Image Rendering with a Retinex-Based Adaptive Filter. IEEE Trans Image Process 15(9):2820–2830CrossRefGoogle Scholar
  23. 23.
    Panetta KA, Wharton EJ, Agaian SS (2008) Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure. Syst Man Cybern B Cybern 38(1):174–188CrossRefGoogle Scholar
  24. 24.
    Pizer SM, Philip Amburn E, Austin JD, Cromartie R, Geselowitz A, Greer T, HaarRomeny B, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368CrossRefGoogle Scholar
  25. 25.
    Shyua M-S, Leou J-J (1998) A genetic algorithm approach to color image enhancement. Pattern Recogn 31(7):871–880CrossRefGoogle Scholar
  26. 26.
    Starck J-L, Murtagh F, Candes EJ, Donoho DL (2003) Gray and Color Image Contrast Enhancement by the Curvelet Transform. IEEE Trans Image Process 12(6):706–717MathSciNetCrossRefGoogle Scholar
  27. 27.
    Stark JA (2000) Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9(5):889–896CrossRefGoogle Scholar
  28. 28.
    Sundaram M, Ramar K, Arumugam N, Prabin G (2011) Histogram Modified Local Contrast Enhancement for mammogram images. Appl Soft Comput 11:5809–5816CrossRefGoogle Scholar
  29. 29.
    Zeyuti Y and Bajaj C (2004) A Fast and Adaptive Method for Image Contrast Enhancement, International Conference on Image Processing, Austin, TX, USA, Vol. 2, pp. 1001–1004Google Scholar

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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