Loading [MathJax]/extensions/MathZoom.js
Identification of noise in the fundus images | IEEE Conference Publication | IEEE Xplore

Identification of noise in the fundus images


Abstract:

Analysis of the tiny retinal vasculatures in retinal fundus images becomes difficult due to very low and varied contrast between the retinal vasculature and the backgroun...Show More

Abstract:

Analysis of the tiny retinal vasculatures in retinal fundus images becomes difficult due to very low and varied contrast between the retinal vasculature and the background. Fundus fluorescein angiogram overcomes these problems and provides an excellent visualization of the retinal vasculature; however it is an invasive procedure requiring injection of contrasting agents. Further investigation of the RETICA method, a non-invasive method of image enhancement developed earlier, is reported in this paper. It was found that noise is present in the Retinex image. Thus, the identification of the noise in the Retinex image and its removal has been the focus of this research paper. The method used to identify noise is based on adaptive wiener filters (additive, multiplicative, and additive plus multiplicative filters) and the fundus model image and real fundus images are applied to these filters. It is observed that retinal fundus images contained both additive and multiplicative noise. The noise is reduced by using adaptive wiener filter (additive plus multiplicative adaptive wiener filter) based method which resulted in 2.84db an improvement in PSNR.
Date of Conference: 29 November 2013 - 01 December 2013
Date Added to IEEE Xplore: 23 January 2014
ISBN Information:
Conference Location: Penang, Malaysia
Citations are not available for this document.

I. Introduction

Eye screening is vital in detection of diabetic retinopathy. There are five stages in diabetic retinopathy (DR) ranging from normal (No-DR), mild DR, moderate DR, severe non proliferative DR (NPDR) to PDR. The PDR stage is where there is a total loss of vision [1]. Haemorrhages, exudates and changes in the veins are some of the pathologies that, when present, characterise these DR classifications [2]. It was reported in a research carried out recently on the analysis of images of the fundus that as the severity level of DR advances, the fovea avascular zone size increases. The FAZ is observable in colour fundus images and in fundus fluorescein angiograms (FFA) [3].

Cites in Papers - |

Cites in Papers - IEEE (4)

Select All
1.
Gaurav Sharma, K.M. Soni, Basant Kumar, Maninder Singh, "Deep Learning based Real-Time Noise Removal in Retinal Fundus Images", 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC), vol.1, pp.1-6, 2024.
2.
Niranjana Vannadil, Priyanka Kokil, "Noise and performance analysis on fundus images with CNN and transformer models", 2023 IEEE 7th Conference on Information and Communication Technology (CICT), pp.1-6, 2023.
3.
Ahsan Khawaja, Tariq M. Khan, Khuram Naveed, Syed Saud Naqvi, Naveed Ur Rehman, Syed Junaid Nawaz, "An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser", IEEE Access, vol.7, pp.164344-164361, 2019.
4.
Sidra Aleem, Bin Sheng, Ping Li, Po Yang, David Dagan Feng, "Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks", IEEE Transactions on Industrial Informatics, vol.15, no.7, pp.4099-4110, 2019.

Cites in Papers - Other Publishers (6)

1.
Mona A. S. Ali, Kishore Balasubramanian, Gayathri Devi Krishnamoorthy, Suresh Muthusamy, Santhiya Pandiyan, Hitesh Panchal, Suman Mann, Kokilavani Thangaraj, Noha E. El-Attar, Laith Abualigah, Diaa Salama Abd Elminaam, "Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network", Electronics, vol.11, no.11, pp.1763, 2022.
2.
Adel Elamari, Amine Ben Slama, Hedi Trabelsi, Ezeddine Sediki, "Pre-study for facilitating the discovery of microfluidic properties in blood vessels using retinal fundus images", Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol.10, no.6, pp.599, 2022.
3.
Shahzaib Iqbal, Tariq M. Khan, Khuram Naveed, Syed S. Naqvi, Syed Junaid Nawaz, "Recent trends and advances in fundus image analysis: A review", Computers in Biology and Medicine, vol.151, pp.106277, 2022.
4.
Khuram Naveed, Faizan Abdullah, Hussain Ahmad Madni, Mohammad A.U. Khan, Tariq M. Khan, Syed Saud Naqvi, "Towards Automated Eye Diagnosis: An Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3D Filter", Diagnostics, vol.11, no.1, pp.114, 2021.
5.
Sonali, Sima Sahu, Amit Kumar Singh, S.P. Ghrera, Mohamed Elhoseny, "An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE", Optics & Laser Technology, vol.110, pp.87, 2019.
6.
C. Amala Nair, R. Lavanya, Soft Computing Systems, vol.837, pp.116, 2018.
Contact IEEE to Subscribe

References

References is not available for this document.