Image Enhancement using NHSI Model Employed in Color Retinal Images

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2018 by IJETT Journal
Volume-58 Number-1
Year of Publication : 2018
Authors : Mr. R. Sathish Kumar, M. Nivetha, G. Madhumita, P. Santhoshy
DOI :  10.14445/22315381/IJETT-V58P203


Mr. R. Sathish Kumar, M. Nivetha, G. Madhumita, P. Santhoshy "Image Enhancement using NHSI Model Employed in Color Retinal Images", International Journal of Engineering Trends and Technology (IJETT), V58(1),14-19 April 2018. ISSN:2231-5381. published by seventh sense research group

To produce an enhanced retinal image using enhancement technique because uneven illumination, image blurring, and low contrast retinal images with poor quality are not useful for medical diagnosis. Here we propose a new image enhancement method to improve color retinal image luminosity and contrast which include improving the intensity of the image. Luminance gain matrix is done. Contrast is then enhanced in the luminosity channel of L*a*b* color space by CLAHE (contrast limited adaptive histogram equalization).A method known as nonlinear hue-saturation-intensity color model (iNHSI) to preserve color information of the retinal images is used. Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images. This model in color retinal image enhancement may be employed to assist ophthalmologists in more efficient screening of retinal diseases because we get clear image and enhanced image as a result which helps in development of improved automated image analysis for clinical diagnosis.

[1]Mei Zhou#, Kai Jin#, Shaoze Wang, Juan Ye, and Dahong Qian*, Senior Member, IEEE “Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment”.
[2] M. D. Abramoff et al., “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng., vol. 3, pp. 169-208, Dec. 2010.
[3] A. F. M. Hani and H. A. Nugroho, “Retinal vasculature enhancement using independent component analysis,” J. Biomed. Sci. Eng., vol. 2, no. 7, pp. 543-549, Nov. 2009.
[4] J. Paulus et al., “Automated quality assessment of retinal fundus photos,” Int. J. Comput.Assist. Radiol. Surg., vol. 5, no. 6, pp. 557-564, Nov. 2010.
[5] U. Sevik et al., “Identification of suitable fundus images using automated quality assessment methods,” J. Biomed. Opt., vol. 19, no. 4, p. 046006, Apr. 2014.
[6] M. R. K. Mookiah et al., “Computer-aided diagnosis of diabetic retinopathy: a review,” Comput. Biol. Med., vol. 43, no. 12, pp. 2136-2155, Dec. 2013.
[7] E. Daniel and J. Anitha, “Optimum green plane masking for the contrast enhancement of retinal images using enhanced genetic algorithm,” Optik, vol. 126, no. 18, pp. 1726-1730, Sep. 2015.
[8] M. Foracchia et al., “Luminosity and contrast normalization in retinal images,” Med. Image Anal., vol. 9, no. 3, pp. 179-190, Jun. 2005.
[9] R. Sathish Kumar ,T. Dhinesh, V. Kathirresh.- “Consensus Based Algorithm to Detecting Malicious Nodes in Mobile Adhoc Network”, International Journal of Engineering Research & Technology (IJERT) Vol. 6 Issue 03, March-2017.
[10] Sathish Kumar R, Aktharunissa.A. Koperundevi.S, S. Suganthi "Enhanced Trust Based Architecture in MANET using AODV Protocol to Eliminate Packet Dropping Attacks", International Journal of Engineering Trends and Technology (IJETT), V34(1),21-27 April 2016. ISSN:2231-538.
[11] G. S. Ramlugun et al., “Small retinal vessels extraction towards proliferative diabetic retinopathy screening,” Expert Syst. Appl., vol. 39,no. 1, pp. 1141-1146, Jan. 2012.
[12] R. GeethaRamani and L. Balasubramanian, “Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis,” Biocybern.Biomed. Eng., vol. 36, no. 1, pp. 102-118, Jan. 2016.
[13] Y. Zhao et al., “Retinal vessel segmentation: an efficient graph cut approach with retina and local phase,” Plos One, vol. 10, no. 4, p. e0122332, Apr. 2015.
[14] Sathish Kumar. R and Pariselvam .S, Formative impact of Gauss Markov Mobility model on Data Availability in MANET, Asian Journal of Information Technology 11(3): 108-116,2012.
[15] M. Liao et al., “Retinal vessel enhancement based on multiscale top-hattransformation and histogram fitting stretching,” Opt. Laser Technol., vol. 58, pp. 56-62, Jun. 2014.
[16] B. Chen et al., “Blood vessel enhancement via multidictionary and sparse coding: application to retinal vessel enhancing,” Neuro computing, vol. 200, pp. 110-117, Aug. 2016.
[17] Sathish Kumar. R R. Logeswari, N. Anitha Devi, S. Divya Bharathy “Efficient Clustering using ECATC Algorithm to Extend Network Lifetime in Wireless Sensor Networks”, International Journal of Engineering Trends and Technology(IJETT), vol-45 no- 9-march2017. ISSN:2231-5381
[18] H. Tang and Y. Zhao, “Edge detection in CIE L*a *b * based on fractional differential,” J. Image Graph., vol. 18, no. 6, pp. 628- 636, Jun. 2013.
[19] S. Wang et al., “Human visual system-based fundus image quality assessment of portable fundus camera photographs,” IEEE T. Med. Imaging, vol. 35, no. 4, pp. 1046-1055, Apr. 2016.
[20] P. Feng et al., “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett.,vol. 28, no. 4, pp. 516-522, Mar. 2007.
[21] E. D. Pisano et al., “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging, vol. 11, no. 4, pp. 193-200, Nov. 1998.
[22] S. Wang et al., “Naturalness preserved enhancement algorithm for non-uniform illumination images,” IEEE T. Image Process., vol. 22, no. 9, pp. 3538-3548, Sep. 2013.
[23] R. Kirifi et al., “CIEL*a*b* color space predictive models for colorimetrydevices - analysis of perfume quality,” Talanta, vol. 104, pp. 58-66, Jan. 2013.
[24] J. Li, Y. Tian, T. Huang, and W. Gao. Cost-sensitive rank learning from positive and unlabeled data for visual saliency estimation. IEEE Signal Processing Letters, 17(6):591–594, 2010.
[25] J. Li, Y. Tian, T. Huang, and W. Gao. Multi-task rank learning for visual saliency estimation. IEEE Transactions on Circuits and Systems for Video Technology, 21(5):623–636, 2011.
[24] Z. Li. A saliency map in primary visual cortex. Trends in cognitive sciences, 6(1):9–16, 2002.
[26] Z. Li, S. Qin, and L. Itti. Visual attention guided bit allocation in video compression. Image Vision Computing, 29(1):1–14, Jan. 2011.
[27] Z. Ma, L. Qing, J. Miao, and X. Chen. Advertisement evaluation using visual saliency based on foveated image. In IEEE International Conference on Multimedia and Expo (ICME), pages 914–917, 2009.
[28] A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, (3):145–175, 2001.
[29] S. Wei, D. Xu, X. Li, and Y. Zhao. Joint optimization toward effective and efficient image search. IEEE Transactions on Cybernetics, 43(6):2216–2227, 2013.
[30] S. Wei, Y. Zhao, C. Zhu, C. Xu, and Z. Zhu. Frame fusion for video copy detection. IEEE Transactions on Circuits and Systems for Video Technology, 21(1):15–28, 2011.
[31] J. Zhang and S. Sclaroff. Saliency detection: A boolean map approach. In IEEE International Conference on Computer Vision (ICCV), pages 153–160, 2013.
[32] Q. Zhao and C. Koch. Learning visual saliency by combining feature maps in a nonlinear manner using adaboost. Journal of Vision, 12(6):22, 1–15, 2012.

Contrast enhancement, iNHSI gamma correction, L*a*b* color space, luminosity channel, retinal image.