Optic Disc Detection using Fish School Search Algorithm based on FPGA

  • Sa'ed Abed Kuwait University
  • Dalal Al-Oraifan Computer Engineering Department, College of Engineering and Petroleum, Kuwait University
  • Amna Safar Computer Engineering Department, College of Engineering and Petroleum, Kuwait University
Keywords: Diabetic Retinopathy, Fish School Search Techniques, Optic Disc Detection, Pre-processing, FPGA, Speed and Accuracy

Abstract

Many people worldwide suffer from Diabetic Retinopathy (DP). This health ailment affects their vision throughout the years, as they get older. The fundus image is examined for detecting diabetic diseases that could affect the retina such as the DP. Correctly detecting the optic disc is required to discover the disease. Several methods have been proposed to improve the detection of the optic disc in respect to different performance metrics. In this work, we investigate the performance, mainly the power consumption and the computational time of the Fish School Search (FSS) technique. We detect the optic disc by using contrast enhanced multi-step pre-processing technique to improve the color fundus image. The pre-processing steps used in this work improve the quality of the colored image by filtering out the noises, smoothing the image, and masking out the regions where it is guaranteed that the optic disc is not located in. The FSS algorithm is applied to find the brightest pixel in the pre-processed image, which is marked as the optic disc. The algorithm is also implemented in the FPGA to benefit from the parallel processing power of the FPGA. The algorithm is tested on DRIVE and STARE databases, and compared to other methods in literature. The accuracy of the FSS was 100% and 95.7% when using DRIVE and STARE databases, respectively. Moreover, the running time of the FPGA implementation was found to be 1.605 ms with a total power dissipation of 121.818 mW.

Author Biography

Sa'ed Abed, Kuwait University

Computer Engineering Department,

Assistant Professor

References

R. Pires, H. Jelinek, J. Wainer, S. Goldestein, E. Valle, A Rocha, Assessing the Need for Referral Automatic Diabetic Retinopathy Detection, IEEE Transactions on Biomedical Engineering 60 (12) (2013) 3391-3398

H. Jaafar, A. Nandi, W. Al-Nuaimy, Decision Support System for the Detection and Grading of Hard Exudates from Color Fundus Photographs, Journal of Biomedical Optics 16 (11) (2011) 116001-11600110

S. Abed, S. Al-Roomi, M. Al-Shayeji, Effective Optic Disc Detection Method Based on Swarm Intelligence Techniques and Novel Pre-processing Steps, Applied Soft Computing 49 (2016) 146-163

C. Bastos-Filho, D. Nascimento, An Enhanced Fish School Search Algorithm, Brics Congress on Computational Intelligence & 11th Brazilian Congress on Computational Intelligence (2013) 152-157

D. Zhang, Y. Zhao, Novel Accurate and Fast Optic Disc Detection in Retinal Images With Vessel Distribution and Directional Characteristics, IEEE Journal of Biomedical and Health Informatics 20 (1) (2016) 333-342.

M. Zahoor, M. Fraz, Fast Optic Disc Segmentation in Retina Using Polar Transform, IEEE Access 5 (2017) 12293 - 12300.

I. Soares, M. C. Branco, A. G. Pinheiro, Optic Disc Localization in Retinal Images Based on Cumulative Sum Fields, IEEE Journal of Biomedical and Health Informatics 20 (2) (2016) 574-585.

P. Manjiri, M. Ramesh, R. Yogesh, S. Manoj, D. Neha, Automated Locolization of Optic Disc, Detection of Microaneurysms and Extraction of Blood Vessels to Bypass Angiography, Proc. of the 3rd International Conference on Frontiers of Intelligent Computing. (FICTA) 327 Advances in Intelligent Systems and Computing (2014) 579-587.

G. F. M. Ponnaiah, S. S. Baboo, GA based Automatic Optic Disc Detection from Fundus Image using Blue Channel and Green Channel Information, International Journal of Computer Applications 69 (2) (2013) 23-31.

R. J. Qureshi, L. Kovacs, B. Harangi, B. Nagy, T. Peto, A. Hajdu, Combining Algorithms for Automatic Detection of Optic Disc and Macula in Fundus Images, Computer Vision and Image Understanding 116 (1) (2012) 138–145.

H.-K. Hsiao, C.-C.Liu, C.-Y.Yu, S.-W.Kuo, S.-S.Yu, A Novel Optic Disc Detection Scheme on Retinal Images, Expert Systems with Applications 39 (12) (2012) 10600–10606.

J. Wigdahl, P. Guimaraes, A. Ruggeri, A shortest path approach to optic disc detection in retinal fundus images, Journal for Modeling in Ophthalmology (2017) 29 – 42.

M. Wyawahare, P. Patil, Performance Evaluation of Optic Disc Segmentation Algorithms in Retinal Fundus Images: an Empirical Investigation, International Journal of Advanced Science and Technology 69 (2014) 19-32.

M. Alshayeji, S. Al-Roomi, S. Abed, Optic Disc Detection in Retinal Fundus Images using Gravitational law-based Edge Detection, International Federation for Medical and Biological Engineering and Computing (2016) 1-14

C. Pereira, L. Goncalves, M. Ferreira, Optic Disc Detection in Color Fundus Images Using Ant Colony Optimization, Medical & Biological Engineering & Computing 295 51 (3) (2013) 295–303.

T. Devasia, P. Jacob, T. Thomas, Automatic Optic Disc Localization and Segmentation using Swarm Intelligence, World of Computer Science and Information Technology 5 (6) (2015) 92-97.

Y. Kimori, Mathematical Morphology-based Approach to the Enhancement of Morphological Features in Medical Images. 1 (1) (2011) 33-40

N. Abdul Majeed, S. Rao, Hardware Implementation of Retinal Image Processing Algorithm on FPGA, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering 3 (1) (2015) 157-161.

A. Fredj, M. Ben Abdallah, J. Malek, A. Azar, Fundus image denoising using FPGA hardware Architecture, International Journal Computer Applications in Technology 54 (1) (2016) 1-13.

Ritika, S. Kaur, Contrast Enhancement Techniques for Images - A Visual Analysis, International Journal of Computer Applications, 64 (17) (2013) 20-25.

Published
2019-08-07
Section
Computer Engineering