1
|
AlRowaily MH, Arof H, Ibrahim I, Yazid H, Mahyiddin WA. Enhancing Retina Images by Lowpass Filtering Using Binomial Filter. Diagnostics (Basel) 2024; 14:1688. [PMID: 39125565 PMCID: PMC11311422 DOI: 10.3390/diagnostics14151688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 07/26/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue channels are read and stored in separate arrays. Then, the area of the eye also called the region of interest (ROI) is located by thresholding. Next, the ratios of R to G and B to G at every pixel in the ROI are calculated and stored along with copies of the R, G and B channels. Then, the RGB channels are subjected to average filtering using a 3 × 3 mask to smoothen the RGB values of pixels, especially along the border of the ROI. In the background brightness estimation stage, the ROI of the three channels is filtered by binomial filters (BFs). This step creates a background brightness (BB) surface of the eye region by levelling the foreground objects like blood vessels, fundi, optic discs and blood spots, thus allowing the estimation of the background illumination. In the next stage, using the BB, the luminosity of the ROI is equalized so that all pixels will have the same background brightness. This is followed by a contrast adjustment of the ROI using CLAHE. Afterward, details of the adjusted green channel are enhanced using information from the adjusted red and blue channels. In the color correction stage, the intensities of pixels in the red and blue channels are adjusted according to their original ratios to the green channel before the three channels are reunited. The resulting color image resembles the original one in color distribution and tone but shows marked improvement in luminosity and contrast. The effectiveness of the approach is tested on the test images and enhancement is noticeable visually and quantitatively in greyscale and color. On average, this method manages to increase the contrast and luminosity of the images. The proposed method was implemented using MATLAB R2021b on an AMD 5900HS processor and the average execution time was less than 10 s. The performance of the filter is compared to those of two other filters and it shows better results. This technique can be a useful tool for ophthalmologists who perform diagnoses on the eyes of diabetic patients.
Collapse
Affiliation(s)
- Mofleh Hannuf AlRowaily
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (M.H.A.); (I.I.); (W.A.M.)
| | - Hamzah Arof
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (M.H.A.); (I.I.); (W.A.M.)
| | - Imanurfatiehah Ibrahim
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (M.H.A.); (I.I.); (W.A.M.)
| | - Haniza Yazid
- Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Ulu Pauh Campus, Arau 02600, Malaysia;
| | - Wan Amirul Mahyiddin
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (M.H.A.); (I.I.); (W.A.M.)
| |
Collapse
|
2
|
Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model. Healthcare (Basel) 2022; 10:healthcare10122497. [PMID: 36554021 PMCID: PMC9778546 DOI: 10.3390/healthcare10122497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/19/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model's outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use.
Collapse
|
3
|
Rebinth A, Kumar S. Glaucoma diagnosis based on colour and spatial features using kernel SVM. CARDIOMETRY 2022. [DOI: 10.18137/cardiometry.2022.22.508515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
The main aim of the paper is to develop an early detection system for glaucoma classification using the fundus images. By reviewing the various glaucoma image classification schemes, suitable features and supervised approaches are identified. An automated Computer Aided Diagnosis (CAD) system is developed for glaucoma based on soft computing techniques. It consists of three stages. The Region Of Interest (ROI) is selected in the first stage that comprises of Optic Disc (OD) region only. It is selected automatically based on the on the green channel’s highest intensity. In the second stage, features such as colour and Local Binary patterns (LBP) are extracted. In the final stage, classification of fundus image is achieved by employing supervised learning of Support Vector Machine (SVM) classifier for classifying the fundus images into either normal or glaucomatous. The evaluation of the CAD system on four public databases; ORIGA, RIM-ONE, DRISHTI-GS, and HRF show that LBP gives promising results than the conventional colour features.
Collapse
|
4
|
DSLN: Dual-tutor student learning network for multiracial glaucoma detection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07078-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
5
|
Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1601354. [PMID: 35222876 PMCID: PMC8866016 DOI: 10.1155/2022/1601354] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/07/2022] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
Abstract
Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma by the year 2040. 90% of glaucoma is undetected in developing nations. It is essential to develop a glaucoma detection system for early diagnosis. In this research, early prediction of glaucoma using deep learning technique is proposed. In this proposed deep learning model, the ORIGA dataset is used for the evaluation of glaucoma images. The U-Net architecture based on deep learning algorithm is implemented for optic cup segmentation and a pretrained transfer learning model; DenseNet-201 is used for feature extraction along with deep convolution neural network (DCNN). The DCNN approach is used for the classification, where the final results will be representing whether the glaucoma infected or not. The primary objective of this research is to detect the glaucoma using the retinal fundus images, which can be useful to determine if the patient was affected by glaucoma or not. The result of this model can be positive or negative based on the outcome detected as infected by glaucoma or not. The model is evaluated using parameters such as accuracy, precision, recall, specificity, and F-measure. Also, a comparative analysis is conducted for the validation of the model proposed. The output is compared to other current deep learning models used for CNN classification, such as VGG-19, Inception ResNet, ResNet 152v2, and DenseNet-169. The proposed model achieved 98.82% accuracy in training and 96.90% in testing. Overall, the performance of the proposed model is better in all the analysis.
Collapse
|
6
|
End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
7
|
Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm. Med Biol Eng Comput 2022; 60:785-796. [PMID: 35080695 DOI: 10.1007/s11517-022-02510-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 01/18/2022] [Indexed: 10/19/2022]
Abstract
Glaucoma disease is optic neuropathy; in glaucoma, the optic nerve is damaged because the long duration of intraocular pressure can be caused blindness. Nowadays, deep learning classification algorithms are widely used to diagnose various diseases. However, in general, the training of deep learning algorithms is carried out by traditional gradient-based learning techniques that converge slowly and are highly likely to fall to the local minimum. In this study, we proposed a novel decision support system based on deep learning to diagnose glaucoma. The proposed system has two stages. In the first stage, the preprocessing of glaucoma disease data is performed by normalization and mean absolute deviation method, and in the second stage, the training of the deep learning is made by the artificial algae optimization algorithm. The proposed system is compared to traditional gradient-based deep learning and deep learning trained with other optimization algorithms like genetic algorithm, particle swarm optimization, bat algorithm, salp swarm algorithm, and equilibrium optimizer. Furthermore, the proposed system is compared to the state-of-the-art algorithms proposed for the glaucoma detection. The proposed system has outperformed other algorithms in terms of classification accuracy, recall, precision, false positive rate, and F1-measure by 0.9815, 0.9795, 0.9835, 0.0165, and 0.9815, respectively.
Collapse
|
8
|
Mrad Y, Elloumi Y, Akil M, Bedoui MH. A fast and accurate method for glaucoma screening from smartphone-captured fundus images. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.06.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
|
9
|
Sarhan A, Swift A, Gorner A, Rokne J, Alhajj R, Docherty G, Crichton A. Utilizing a responsive web portal for studying disc tracing agreement in retinal images. PLoS One 2021; 16:e0251703. [PMID: 34032798 PMCID: PMC8148353 DOI: 10.1371/journal.pone.0251703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 05/02/2021] [Indexed: 11/18/2022] Open
Abstract
Glaucoma is a leading cause of blindness worldwide whose detection is based on multiple factors, including measuring the cup to disc ratio, retinal nerve fiber layer and visual field defects. Advances in image processing and machine learning have allowed the development of automated approached for segmenting objects from fundus images. However, to build a robust system, a reliable ground truth dataset is required for proper training and validation of the model. In this study, we investigate the level of agreement in properly detecting the retinal disc in fundus images using an online portal built for such purposes. Two Doctors of Optometry independently traced the discs for 159 fundus images obtained from publicly available datasets using a purpose-built online portal. Additionally, we studied the effectiveness of ellipse fitting in handling misalignments in tracing. We measured tracing precision, interobserver variability, and average boundary distance between the results provided by ophthalmologists, and optometrist tracing. We also studied whether ellipse fitting has a positive or negative impact on properly detecting disc boundaries. The overall agreement between the optometrists in terms of locating the disc region in these images was 0.87. However, we found that there was a fair agreement on the disc border with kappa = 0.21. Disagreements were mainly in fundus images obtained from glaucomatous patients. The resulting dataset was deemed to be an acceptable ground truth dataset for training a validation of models for automatic detection of objects in fundus images.
Collapse
Affiliation(s)
- Abdullah Sarhan
- Department of Computer Science, University of Calgary, Calgary, Canada
- * E-mail:
| | - Andrew Swift
- Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Adam Gorner
- Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Jon Rokne
- Department of Computer Science, University of Calgary, Calgary, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, Canada
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey
- Department of Health Informatics, University of Southern Denmark, Odense, Denmark
| | - Gavin Docherty
- Department of Ophthalmology and Visual Sciences, University of Calgary, Calgary, Canada
| | - Andrew Crichton
- Department of Ophthalmology and Visual Sciences, University of Calgary, Calgary, Canada
| |
Collapse
|
10
|
Shabbir A, Rasheed A, Shehraz H, Saleem A, Zafar B, Sajid M, Ali N, Dar SH, Shehryar T. Detection of glaucoma using retinal fundus images: A comprehensive review. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:2033-2076. [PMID: 33892536 DOI: 10.3934/mbe.2021106] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.
Collapse
Affiliation(s)
- Amsa Shabbir
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Aqsa Rasheed
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Huma Shehraz
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Aliya Saleem
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Bushra Zafar
- Department of Computer Science, Government College University, Faisalabad 38000, Pakistan
| | - Muhammad Sajid
- Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Nouman Ali
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Saadat Hanif Dar
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| | - Tehmina Shehryar
- Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
| |
Collapse
|
11
|
Abdelmonem R, Elhabal SF, Abdelmalak NS, El-Nabarawi MA, Teaima MH. Formulation and Characterization of Acetazolamide/Carvedilol Niosomal Gel for Glaucoma Treatment: In Vitro, and In Vivo Study. Pharmaceutics 2021; 13:pharmaceutics13020221. [PMID: 33562785 PMCID: PMC7915822 DOI: 10.3390/pharmaceutics13020221] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 01/25/2021] [Accepted: 01/30/2021] [Indexed: 11/18/2022] Open
Abstract
Acetazolamide (ACZ) is a diuretic used in glaucoma treatment; it has many side effects. Carvedilol (CAR) is a non-cardioselective beta-blocker used in the treatment of elevated intraocular pressure; it is subjected to the first-pass metabolism and causes fluids accumulation leading to edema. This study focuses on overcoming previous side effects by using a topical formula of a combination of the two previous drugs. Sixty formulations of niosomes containing Span 20, Span 60, Tween 20, and Tween 60 with two different ratios were prepared and characterized. Formulation with the lowest particle size (416.30 ± 0.23), the highest zeta potential (72.04 ± 0.43 mv), and the highest apparent coefficient of corneal permeability (0.02 ± 0.29 cm/h) were selected. The selected formula was incorporated into the gel using factorial design 23. Niosomes (acetazolamide/carvedilol) consisting of Span 60 and cholesterol in the molar ratio (7:6), HMPC, and carbopol with two different ratios were used. The selected formula was subjected to an in vivo study of intraocular pressure in ocular hypertensive rabbits for 60 h. The sustained gel formula of the combination decreased (IOP) to normal after 1 h and sustained efficacy for 4 days. Histological analysis of rabbit eyeballs treated with the selected formula showed improvement in glaucomatous eye retinal atrophy.
Collapse
Affiliation(s)
- Rehab Abdelmonem
- Department of Industrial Pharmacy, College of Pharmacy, Misr University for Science and Technology (MUST), 6th of October City, Giza 12566, Egypt;
| | - Sammar F. Elhabal
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Mokattam, Cairo 11571, Egypt
- Correspondence: ; Tel.: +20-010-088-56536
| | - Nevine S. Abdelmalak
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Kasr El-Aini Street, Cairo 11562, Egypt; (N.S.A.); (M.A.E.-N.); (M.H.T.)
- Department of Pharmaceutics and Industrial Pharmacy, School of Pharmacy, Newgiza University (NGU), Km 22 Cairo-Alex Road, Giza 12256, Egypt
| | - Mohamed A. El-Nabarawi
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Kasr El-Aini Street, Cairo 11562, Egypt; (N.S.A.); (M.A.E.-N.); (M.H.T.)
| | - Mahmoud H. Teaima
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Kasr El-Aini Street, Cairo 11562, Egypt; (N.S.A.); (M.A.E.-N.); (M.H.T.)
| |
Collapse
|