A hybrid feature preservation technique based on luminosity and edge based contrast enhancement in color fundus images

• Autorzy: Palanisamy Gopinath , Shankar Natarajan B. , Ponnusamy Palanisamy , Gopi Varun P.

Identyfikatory

DOI

10.1016/j.bbe.2020.02.006

• Języki publikacji: EN

Abstrakty

EN

Color fundus image analysis for detecting the retinal abnormalities requires an improved visualization of image attributes with sufficient luminosity, contrast and accurate edge details. A hybrid technique based on singular value equalization using shearlet transform and adaptive gamma correction, followed by contrast limited adaptive histogram equalization (CLAHE) is proposed for the enhancement of luminosity and contrast in color fundus images. The low frequency components of the original and adaptive gamma transformed value channel in HSV color space obtained by applying shearlet transform are considered for singular value equalization. The high frequency components of the unchanged value channel, denoised using soft thresholding are applied while performing inverse shearlet transform. Luminosity component in Lab colorspace is considered for performing CLAHE on the singular value equalized image. Subjective analysis is done based on visualization of the image attributes and the objective analysis is carried out based on the parameters such as Peak signal to noise ratio, entropy, feature similarity index, edge-based contrast measure, quality index and noise suppression measure. The simulation results evince superior noise performance, sufficient luminosity adjustment and improved contrast along with excellent edge detail preservation when compared with the existing state-of-the-art techniques.

Słowa kluczowe

EN

luminosity adjustment; contrast enhancement; adaptive gamma correction; shearlet transform; singular value equalization; fundus image

PL

poprawa kontrastu; transformacja Shearleta; obraz dna oka

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 752--763
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Palanisamy Gopinath

• Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu 620015, India

autor

Shankar Natarajan B.

• Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu, India

autor

Ponnusamy Palanisamy

• Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu, India

autor

Gopi Varun P.

• Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu, India

Bibliografia

• [1] Hani AFM, Soomro TA, Faye I, Kamel N, Yahya N. Denoising methods for retinal fundus images. 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS). 2014. pp. 1–6.
• [2] Davis H, Russell S, Barriga E, Abramoff M, Soliz P. Vision-based, real-time retinal image quality assessment. 2009 22nd IEEE International Symposium on Computer-Based Medical Systems. 2009. pp. 1–6.
• [3] Hani AFM, Soomro TA, Fayee I, Kamel N, Yahya N. Identification of noise in the fundus images. 2013 IEEE International Conference on Control System, Computing and Engineering. 2013. pp. 191–6. http://dx.doi.org/10.1109/ICCSCE.2013.6719957.
• [4] Trucco E, Ruggeri A, Karnowski T, Giancardo L, Chaum E, Hubschman JP, et al. Validating retinal fundus image analysis algorithms: issues and a proposal. Investig Ophthalmol Visual Sci 2013;54(5):3546–59. http://dx.doi.org/10.1167/iovs.12-10347.
• [5] Stella Mary MCV, Rajsingh EB, Naik GR. Retinal fundus image analysis for diagnosis of glaucoma: a comprehensive survey. IEEE Access 2016;4:4327–54. http://dx.doi.org/10.1109/ACCESS.2016.2596761.
• [6] Soomro TA, Gao J, Khan MAU, Khan TM, Paul M. Role of image contrast enhancement technique for ophthalmologist as diagnostic tool for diabetic retinopathy. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). 2016. pp. 1–8. http://dx.doi.org/10.1109/DICTA.2016.7797078.
• [7] Lin J, Yu L, Weng Q, Zheng X. Retinal image quality assessment for diabetic retinopathy screening: a survey. Multimedia Tools Appl 2019. http://dx.doi.org/10.1007/s11042-019-07751-6.
• [8] Abdel-Hamid L, El-Rafei A, El-Ramly S, Michelson G, Hornegger J. Retinal image quality assessment based on image clarity and content. J Biomed Opt 2016;21. http://dx.doi.org/10.1117/1.JBO.21.9.096007.
• [9] Kar SS, Maity SP. Automatic detection of retinal lesions for screening of diabetic retinopathy. IEEE Trans Biomed Eng 2018;65(3):608–18. http://dx.doi.org/10.1109/TBME.2017.2707578.
• [10] Kausu T, Gopi VP, Wahid KA, Doma W, Niwas SI. Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Biocybern Biomed Eng 2018;38(2):329–41. http://dx.doi.org/10.1016/j.bbe.2018.02.003.
• [11] Karkuzhali S, Manimegalai D. Robust intensity variation and inverse surface adaptive thresholding techniques for detection of optic disc and exudates in retinal fundus images. Biocybern Biomed Eng 2019;39 (3):753–64. http://dx.doi.org/10.1016/j.bbe.2019.07.001.
• [12] Khakzar M, Pourghassem H. A retinal image authentication framework based on a graph-based representation algorithm in a two-stage matching structure. Biocybern Biomed Eng 2017;37(4):742–59. http://dx.doi.org/10.1016/j.bbe.2017.09.001.
• [13] Yao Min, Zhu Changming. Study and comparison on histogram-based local image enhancement methods. 2017 2nd International Conference on Image, Vision and Computing (ICIVC). 2017. pp. 309–14.
• [14] Patel S, Goswami M. Comparative analysis of histogram equalization techniques. 2014 International Conference on Contemporary Computing and Informatics (IC3I). 2014. pp. 167–8. http://dx.doi.org/10.1109/IC3I.2014.7019808.
• [15] Chen Soong-Der, Ramli AR. Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consumer Electron 2003;49(4):1310–9. http://dx.doi.org/10.1109/TCE.2003.1261234.
• [16] Chen Soong-Der, Ramli AR. Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consumer Electron 2003;49(4):1301–9. http://dx.doi.org/10.1109/TCE.2003.1261233.
• [17] Chang YT, Wang JT, Yang WH, Chen XW. Contrast enhancement in palm bone image using quad-histogram equalization. 2014 International Symposium on Computer, Consumer and Control. 2014. pp. 1091–4. http://dx.doi.org/10.1109/IS3C.2014.284.
• [18] Demirel H, Anbarjafari G, Jahromi MNS. Image equalization based on singular value decomposition. 2008 23rd International Symposium on Computer and Information Sciences. 2008. pp. 1–5. http://dx.doi.org/10.1109/ISCIS.2008.4717878.
• [19] Franklin SW, Rajan SE. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybern Biomed Eng 2014;34(2):117–24. http://dx.doi.org/10.1016/j.bbe.2014.01.004.
• [20] Bandara AMRR, Giragama PWGRMPB. A retinal image enhancement technique for blood vessel segmentation algorithm. 2017 IEEE International Conference on Industrial and Information Systems (ICIIS). 2017. pp. 1–5. http://dx.doi.org/10.1109/ICIINFS.2017.8300426.
• [21] Hani AFM, Ahmed Soomro T, Nugroho H, Nugroho HA. Enhancement of colour fundus image and FFA image using retica. 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences. 2012. pp. 831–6. http://dx.doi.org/10.1109/IECBES.2012.6498205.
• [22] Hani AFM, Soomro TA, Fayee I. Non-invasive contrast enhancement for retinal fundus imaging. 2013 IEEE International Conference on Control System, Computing and Engineering. 2013. pp. 197–202. http://dx.doi.org/10.1109/ICCSCE.2013.6719958.
• [23] Soomro T, Gao J. Non-invasive contrast normalisation and denosing technique for the retinal fundus image. Ann Data Sci 2016;3. http://dx.doi.org/10.1007/s40745-016-0079-7.
• [24] Sonali SS, Singh AK, Ghrera S, Elhoseny M. An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Opt Laser Technol 2019;110:87–98. http://dx.doi.org/10.1016/j.optlastec.2018.06.061. Special Issue: Optical Imaging for Extreme Environment.
• [25] Wang L, Liu G, Fu S, Xu L, Zhao K, Zhang C. Retinal image enhancement using robust inverse diffusion equation and self-similarity filtering. PLOS ONE 2016;11(7):1–13. http://dx.doi.org/10.1371/journal.pone.0158480.
• [26] Zhou W, Wu C, Yi Y, Du W. Automatic detection of exudates in digital color fundus images using superpixel multi-feature classification. IEEE Access 2017;5:17077–88. http://dx.doi.org/10.1109/ACCESS.2017.2740239.
• [27] Huang S, Cheng F, Chiu Y. Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans Image Process 2013;22(3):1032–41. http://dx.doi.org/10.1109/TIP.2012.2226047.
• [28] Wang X, Chen L. Contrast enhancement using feature-preserving bi-histogram equalization. Signal Image Video Process 2018;12(4):685–92. http://dx.doi.org/10.1007/s11760-017-1208-2.
• [29] Zhou M, Jin K, Wang S, Ye J, Qian D. Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans Biomed Eng 2018;65(3):521–7. http://dx.doi.org/10.1109/TBME.2017.2700627.
• [30] Easley G, Labate D, Lim W-Q. Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmonic Anal 2008;25(1):25–46. http://dx.doi.org/10.1016/j.acha.2007.09.003.
• [31] Hou B, Zhang X, Bu X, Feng H. SAR image despeckling based on nonsubsampled shearlet transform. IEEE J Sel Top Appl Earth Observ Rem Sens 2012;5(3):809–23. http://dx.doi.org/10.1109/JSTARS.2012.2196680.
• [32] Decenciére E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, et al. Feedback on a publicly distributed database: the messidor database. Image Anal Stereol 2014;33(3):231–4. http://dx.doi.org/10.5566/ias.1155.
• [33] Rahman S, Rahman MM, Abdullah-Al-Wadud M, Al-Quaderi GD, Shoyaib M. An adaptive gamma correction for image enhancement. EURASIP J Image Video Process 2016;35. http://dx.doi.org/10.1186/s13640-016-0138-1.
• [34] Santhakumar R, Rajkumar ER, Tandur M, Geetha KS, Rajamani KT, Haritz G. Novel method for automatic generation of fundus mask. 2015 Third International Conference on Image Information Processing (ICIIP). 2015. pp. 147–51. http://dx.doi.org/10.1109/ICIIP.2015.7414756.
• [35] W.L.G. Easley, D. Labate, Software and demo. https://www.math.uh.edu/dlabate/software.html [Accessed 27 November 2019].
• [36] Diwakar M, Lamba S, Gupta H. Ct image denoising based on thresholding in shearlet domain. Biomed Pharmacol J 2018;11:671–7. http://dx.doi.org/10.13005/bpj/1420.
• [37] Palanisamy G, Ponnusamy P, Gopi VP. An improved luminosity and contrast enhancement framework for feature preservation in color fundus images. Signal Image Video Process 2019;13(4):719–26. http://dx.doi.org/10.1007/s11760-018-1401-y.
• [38] Soomro T, Saand A, Soomro S, Shah S, Khuhawar A. Enhancement of colour fundus images by using single and multi-scale retinex. Bahria Univ J Inform Commun Technol 2015;8(1):1–6, https://www.bahria.edu.pk/ojs/index.php/bujict/article/ view/85/97.
• [39] Sreeja P, Hariharan S. An improved feature based image fusion technique for enhancement of liver lesions. Biocybern Biomed Eng 2018;38(3):611–23. http://dx.doi.org/10.1016/j.bbe.2018.03.004.
• [40] Gopi VP, Palanisamy P, Niwas SI. Capsule endoscopic colour image denoising using complex wavelet transform. In: Venugopal KR, Patnaik LM, editors. Wireless networks and computational intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 220–9.
• [41] Tunga B, Koçanaogullari A. Digital image decomposition and contrast enhancement using high-dimensional model representation. Signal Image Video Process 2018;12 (2):299–306. http://dx.doi.org/10.1007/s11760-017-1158-8.
• [42] Zhang L, Zhang L, Mou X, Zhang D. FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 2011;20(8):2378–86. http://dx.doi.org/10.1109/TIP.2011.2109730.
• [43] Gopi V, Ponnusamy P. Capsule endoscopic image denoising based on double density dual tree complex wavelet transform. Int J Imaging Robot 2012;9:48–60.
• [44] Gopi V, Ponnusamy P, Wahid KA, Babyn P. MR image reconstruction based on framelets and nonlocal total variation using split Bregman method. Int J Comput Assist Radiol Surg 2013;9. http://dx.doi.org/10.1007/s11548-013-0938-z.
• [45] Wieclawek W, Pietka E. Granular filter in medical image noise suppression and edge preservation. Biocybern Biomed Eng 2019;39(1):1–16. http://dx.doi.org/10.1016/j.bbe.2018.09.006.
• [46] Wang Zhou, Bovik AC. A universal image quality index. IEEE Signal Process Lett 2002;9(3):81–4. http://dx.doi.org/10.1109/97.995823.
• [47] Sattar F, Floreby L, Salomonsson G, Lovstrom B. Image enhancement based on a nonlinear multiscale method. IEEE Trans Image Process 1997;6(6):888–95. http://dx.doi.org/10.1109/83.585239.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).