An Efficient Framework for Macula Exudates Detection in Fundus Eye Medical Images | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 11, Volume 29, Issue 1, January 2020, Page 78-83 PDF (601.77 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2020.69188 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Noha A. El-Hag1; Walid El-Shafie1; Ghada M. El-Banby2; El-Sayd M. El-Rabaie1; Adel S. El-Fishawy3; Fathi I. Abd El-Samie4 | ||||
| 1Communications and Electronics Department Faculty of Electronic Engineering,Manoufia University: Menouf, Egypt | ||||
| 2Automatic Control Department Faculty of Electronic Engineering,Manoufia University: Menouf, Egypt | ||||
| 3Communications and Electronics Department Faculty of Electronic Engineering,Manoufia University: Menouf, Egypt, | ||||
| 4Communications and ElectronicsDepartment Faculty of Electronic Engineering, Menuufia Univeristy: Menof, Egypt | ||||
| Abstract | ||||
| This paper presents a computer-based framework for the segmentation of medical eye images. Also, the proposed framework achieves the detection of exudates in medical eye images for better diagnosis of maculopathy disease. The proposed framework begins with fuzzy image enhancement of eye images for contrast enhancement in order to enhance the objects representation of the images. After that, the segmentation process is performed to determine the optic disc and blood vessels to remove them. The next step is detecting the region of interest edges in exudates. A gradient process is also performed on the image and the histogram of gradient is evaluated. Accumulative histogram is further generated for discrimination between image with and without exudates. A threshold histogram curve is generated based on predefined images with and without exudates for classification of images in the testing phase. The simulation results prove that the proposed framework has an appreciated performance. | ||||
| Keywords | ||||
| Diabetic Maculpathy; exudates; Fuzzy Technique; Macula; Gaussian Gradient | ||||
| References | ||||
|
[1] Nayak, J., P. S. Bhat,P.S.,Acharya, U. R., “Automatic identification of diabetic maculopathy stages. [2] Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R., “Automatic recognition of exudative maculopathy using fuzzy C-means clustering and neural network".Available on http://www.cs.bris.ac.uk/Publications/ Papers/1000553.pdf , on November 7, 2017. [3] Vimala and Kajamohideen, Detection of diabetic maculpathy in human retinal images using morphological operations'' Online 14 (3) 2014. (http://www.thescipub.com/ojbs.toc), Online Journal of Biological Sciences 14 (3): 175-180, 2014. [4] Nayak, J., Bhat, P. S., & Acharya, U. R. (2009). Automatic identification of diabetic maculopathy stages using fundus images. Journal of medical engineering & technology, 33(2), 119-129. [5] D. Marin , M. E. Gegundez-Arias, B. Ponte,''An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification'', Medical & Biological Engineering & Computing,Jan 2018. [6] Walter, T., and Klein, J.-C., “Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques”, Springer-Verlag Berlin Heidelberg 2001, pp. 282–287. [7] HashikinK., & Isa, N. A. M. (2012, March). Enhancement of the low contrast image using fuzzy set theory. In Computer Modelling and Simulation (UKSim), 2012 UKSim 14th International Conference on (pp. 371-376). IEEE. [8] Akhavan, R., & Faez, K. (2013, December). Automated retinal blood vessel segmentation using fuzzy mathematical morphology and morphological reconstruction. In International Symposium on Artificial Intelligence and Signal Processing (pp. 131-140). Springer, Cham. [9] Li, G., Tong, Y., & Xiao, X. (2011). Adaptive fuzzy enhancement algorithm of surface image based on local discrimination via grey entropy. Procedia Engineering, 15, 1590-1594. [10] Cheng, H. D., & Xu, H. (2000). A novel fuzzy logic approach to contrast enhancement. Pattern recognition, 33(5), 809-8. Chaira, T. (2015). Medical image processing: Advanced fuzzy set theoretic techniques. CRC Press.
[1] Wang, X. Y., Wang, T., & Bu, J. (2011). Color image segmentation using pixel wise support vector machine classification. Pattern Recognition, 44(4), 777-787. [2] ] Dong-liang, P., & An-ke, X. (2005, October). Degraded image enhancement with applications in robot vision. In Systms, Man and Cybernetics, 2005 IEEE International Conference on (Vol. 2, pp. 1837-1842). IEEE . [3] Kerre, E. E., & Nachtegael, M. (Eds.). (2013). Fuzzy techniques in image processing (Vol. 52). Physica. [4] Pal, S. K., & King, R. (1981). Image enhancement using smoothing with fuzzy sets. IEEE TRANS. SYS., MAN, AND CYBER., 11(7), 494-500. [5] Morphological Operations. http://www.viz.tamu.edu/faculty/parkeends489f00/notes/sec1_9.html./ accessed on 7-11-2017. [6] Shih, F. Y. (2009). Image processing and mathematical morphology: fundamentals and applications. CRC press. [7] Zhang, X., & Fan, G. (2006, November). Retinal spot lesion detection using adaptive multiscale morphological processing. In International Symposium on Visual Computing (pp. 490-501). Springer, Berlin, Heidelberg. [8] McAndrew, A. (2004). An introduction to digital image processing with matlab notes for scm2511 image processing. School of Computer Science and Mathematics, Victoria Univ. of Tech., 264(1). [9] Sinthanayothin, C., Boyce, J. F., Cook, H. L., & Williamson, T. H. (1999). Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology, 83(8), 902-910. [10] Mui Hong Ang. '' Computer –Based Identification of Diabetic Maculpathy Stages Using Fundus Images'', Multi-Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies,2011. [11] Walter, T., Klein, J. C., Massin, P., & Erginay, A. (2002). A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE transactions on medical imaging, 21(10), 1236-1243. [12] Tang, H., Wu, E. X., Ma, Q. Y., Gallagher, D., Perera, G. M., & Zhuang, T. (2000). MRI brain image segmentation by multi-resolution edge detection and region selection. Computerized Medical Imaging and Graphics, 24(6), 349-357. [13] Chandy, D. A., & Kumari, V. V. (2006). Genetic algorithm-based location of optic disc in retinal images. Academic Open Internet Journal, 17. [14] Singh, J., & Sivaswamy, J. (2008, February). Fundus foveal localization based on image relative subtraction-IReS approach. In Proceedings of the 14th national conference on communications. [15] Smith, M. A., & Kanade, T. (1998, January). Video skimming and characterization through the combination of image and language understanding. In Content-Based Access of Image and Video Database, 1998. Proceedings., 1998 IEEE International Workshop on (pp. 61-70). IEEE. [16] Lindeberg, T. (2013). Scale selection properties of generalizedscale-space interest point detectors. Journal of Mathematical Imaging and vision, 46(2), 177-210. | ||||
|
Statistics Article View: 234 PDF Download: 159 |
||||
