Luminosity Correction Using Statistical Features on Retinal Images

Abstract:

Article Preview

Retinal fundus image is important for the ophthalmologist to identify and detect many vision-related diseases, such as diabetes and hypertension. From an acquisition process, retinal images often have low gray level contrast and low dynamic range. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure is used to derive diagnostic parameters. In this paper, a new proposed method based on statistical information such as mean and standard deviation was studied. The combination of local and global technique was successful to detect the luminosity region. Then, a simple correction intensity equation was proposed in order to replace the problem intensity. The results of the numerical simulation (SNR = 2.347 and GCF = 4.581) indicate that the proposed method effective to enhance the luminosity region. Implications of the results and future research directions are also presented. Keywords: Detection, Luminosity, Retinal, Statistical.

Info:

Pages:

74-84

Citation:

W. A. Mustafa et al., "Luminosity Correction Using Statistical Features on Retinal Images", Journal of Biomimetics, Biomaterials and Biomedical Engineering, Vol. 37, pp. 74-84, 2018

Online since:

June 2018

Export:

Price:

$38.00

* - Corresponding Author

[1] L. Kubecka, J. Jan, and R. Kolar, Retrospective illumination correction of retinal images,, Int. J. Biomed. Imaging, vol. 2010, no. 1, p.1–10, (2010).

[2] Y. Zheng, B. Vanderbeek, R. Xiao, E. Daniel, D. Stambolian, M. Maguire, J. O'Brien, and J. Gee, Retrospective illumination correction of retinal fundus images from gradient distribution sparsity,, Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on. IEEE, p.972–975, (2012).

DOI: https://doi.org/10.1109/isbi.2012.6235719

[3] K. M. Williams, M. Dogramaci, and T. H. Williamson, Retrospective study of rhegmatogenous retinal detachments secondary to round retinal holes,, Eur. J. Ophthalmol., vol. 22, no. 4, p.635–640, (2012).

DOI: https://doi.org/10.5301/ejo.5000080

[4] W. A. Mustafa, H. Yazid, and S. Yaacob, A Review : Comparison Between Different Type of Filtering Methods on the Contrast Variation Retinal Images,, in IEEE International Conference on Control System, Computing and Engineering, 2014, p.542–546.

DOI: https://doi.org/10.1109/iccsce.2014.7072777

[5] W. A. Mustafa and H. Yazid, Conversion of the Retinal Image Using Gray World Technique,, J. Biomimetics, Biomater. Biomed. Eng., vol. 36, p.70–77, (2018).

DOI: https://doi.org/10.4028/www.scientific.net/jbbbe.36.70

[6] H. Lee and J. Kim, Retrospective correction of nonuniform illumination on bi-level images,, vol. 17, no. 26. Optical Society of America, p.23880–23893, (2009).

DOI: https://doi.org/10.1364/oe.17.023880

[7] W. A. Mustafa and H. Yazid, Image Enhancement Technique on Contrast Variation : A Comprehensive Review,, J. Telecommun. Electron. Comput. Eng., vol. 9, no. 3, p.199–204, (2017).

[8] W. A. Mustafa, H. Yazid, and W. Kamaruddin, Combination of Gray-Level and Moment Invariant for Automatic Blood Vessel Detection on Retinal Image,, J. Biomimetics, Biomater. Biomed. Eng., vol. 34, p.10–19, (2017).

DOI: https://doi.org/10.4028/www.scientific.net/jbbbe.34.10

[9] W. A. Mustafa, H. Yazid, S. Yaacob, and S. Basah, Blood vessel extraction using morphological operation for diabetic retinopathy,, IEEE Reg. 10 Symp., no. 3, p.208–212, Apr. (2014).

DOI: https://doi.org/10.1109/tenconspring.2014.6863027

[10] C. Leahy, A. O'Brien, and C. Dainty, Illumination correction of retinal images using Laplace interpolation.,, Appl. Opt., vol. 51, no. 35, p.8383–9, Dec. (2012).

DOI: https://doi.org/10.1364/ao.51.008383

[11] S. H. Rasta, M. E. Partovi, and A. Javadzadeh, A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement,, J. Med. Signals Sens., vol. 5, no. 1, p.40–48, (2015).

[12] T. Jintasuttisak and S. Intajag, Color retinal image enhancement by Rayleigh contrast-limited adaptive histogram equalization,, in International Conference on Control, Automation and Systems, 2014, p.692–697.

DOI: https://doi.org/10.1109/iccas.2014.6987868

[13] W. A. Mustafa, H. Yazid, and S. Yaacob, Illumination Correction of Retinal Images Using Superimpose Low Pass and Gaussian Filtering,, in International Conference on Biomedical Engineering (ICoBE), 2015, p.1–4.

DOI: https://doi.org/10.1109/icobe.2015.7235889

[14] A. A. A. Youssif, A. Z. Ghalwash, and A. S. Ghoneim, Comparative study of contrast enhancement and illumination equalization methods for retinal vasculature segmentation,, in Cairo International Biomedical Engineering Conference, 2006, p.1–5.

[15] A. Hoover and M. Goldbaum, Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,, IEEE Trans. Med. Imaging, vol. 22, no. 8, p.951–958, (2003).

DOI: https://doi.org/10.1109/tmi.2003.815900

[16] D. Wu, M. Zhang, J.-C. Liu, and W. Bauman, On the adaptive detection of blood vessels in retinal images.,, IEEE Trans. Biomed. Eng., vol. 53, no. 2, p.341–343, (2006).

DOI: https://doi.org/10.1109/tbme.2005.862571

[17] M. Foracchia, E. Grisan, and A. Ruggeri, Luminosity and contrast normalization in retinal images,, Med. Image Anal., vol. 9, p.179–190, (2005).

DOI: https://doi.org/10.1016/j.media.2004.07.001

[18] W. A. Mustafa and H. Yazid, Background Correction using Average Filtering and Gradient Based Thresholding,, J. Telecommun. Electron. Comput. Eng., vol. 8, no. 5, p.81–88, (2016).

[19] H. Kawano, K. Oohama, H. Maeda, Y. Okada, and N. Ikoma, Degraded document image binarization combining local statistics,, 2009 ICCAS-SICE, (2009).

[20] G. Schaefer, M. I. Rajab, M. Emre Celebi, and H. Iyatomi, Colour and contrast enhancement for improved skin lesion segmentation,, Comput. Med. Imaging Graph., vol. 35, no. 2, p.99–104, (2011).

DOI: https://doi.org/10.1016/j.compmedimag.2010.08.004

[21] N. J. Dhinagar and M. Celenk, Ultrasound medical image enhancement and segmentation using adaptive homomorphic filtering and histogram thresholding,, in Conference on Biomedical Engineering and Sciences, 2012, p.349–353.

DOI: https://doi.org/10.1109/iecbes.2012.6498021

[22] M. A. Bakhshali, Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory,, Soft Comput., vol. 20, p.1–8, (2016).

DOI: https://doi.org/10.1007/s00500-016-2210-2

[23] M. D. Vlachos and E. S. Dermatas, Non-uniform illumination correction in infrared images based on a modified fuzzy c-means algorithm,, J. Biomed. Graph. Comput., vol. 3, no. 1, p.6–19, Nov. (2012).

DOI: https://doi.org/10.5430/jbgc.v3n1p6

[24] I. Elyasi, M. A. Pourmina, and M. Moin, Speckle reduction in breast cancer ultrasound images by using homogeneity modified bayes shrink,, Measurement, (2016).

DOI: https://doi.org/10.1016/j.measurement.2016.05.025

[25] W. A. Mustafa and H. Yazid, Illumination and Contrast Correction Strategy using Bilateral Filtering and Binarization Comparison,, J. Telecommun. Electron. Comput. Eng., vol. 8, no. 1, p.67–73, (2016).

[26] W. A. Mustafa, H. Yazid, M. Jaafar, M. Zainal, A. S. Abdul-, and N. Mazlan, A Review of Image Quality Assessment (IQA): SNR, GCF, AD, NAE, PSNR, ME,, J. Adv. Res. Comput. Appl., vol. 7, no. 1, p.1–7, (2017).

[27] K. Matkovic, L. Neumann, A. Neumann, T. Psik, and W. Purgathofer, Global Contrast Factor - a New Approach to Image Contrast,, in Computational Aesthetics in Graphics, Visualization and Imaging, 2005, p.159–167.