Identification of retinal diseases based on retinal blood vessel segmentation using Dagum PDF and feature-based machine learning

References

• Rawas-Qalaji M, Williams CA. Advances in ocular drug delivery. Curr Eye Res 2012;37(5):345–356.
   PubMed Web of Science ®Google Scholar
• GeethaRamani R, Balasubramanian L. Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybern Biomed Eng. 2016;36(1):102–118.
   Web of Science ®Google Scholar
• Carss KJ, Arno G, Erwood M, et al. Comprehensive rare variant analysis via whole-genome sequencing to determine the molecular pathology of inherited retinal disease. Am J Hum Genet. 2017;100(1):75–90.
   PubMed Web of Science ®Google Scholar
• Perdomo O, Rios H, Rodríguez FJ, et al. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput Methods Programs Biomed. 2019;178:181–189.
   PubMed Web of Science ®Google Scholar
• Badar M, Haris M, Fatima A. Application of deep learning for retinal image analysis: a review. Comput Sci Rev. 2020;35:100203.
   Web of Science ®Google Scholar
• Arunkumar R, Karthigaikumar P. Multi-retinal disease classification by reduced deep learning features. Neural Comput Appl. 2017;28(2):329–334.
   Web of Science ®Google Scholar
• Imran A, Li J, Pei Y, et al. Comparative analysis of vessel segmentation techniques in retinal images. IEEE Access. 2019;7:114862–114887.
   Web of Science ®Google Scholar
• Mumtaz R, Hussain M, Sarwar S, et al. Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients. Int J Diabetes Dev Ctries. 2018;38(1):80–87.
   Web of Science ®Google Scholar
• Mohan NJ, Murugan R, Goel T. Investigations of diabetic retinopathy algorithms in retinal fundus images. Int J Image Process Pattern Recognit. 2020;6(1):14–26.
   Google Scholar
• Mohan NJ, Murugan R, Goel T, et al. Optic disc segmentation in fundus images using operator splitting approach. In 2020 Advanced Communication Technologies and Signal Processing (ACTS), IEEE. 2020: 1–5.
   Google Scholar
• Jagan MN, Murugan R, Goel T, et al. An improved accuracy rate in microaneurysms detection in retinal fundus images using non-local mean filter. In international conference on machine learning, image processing, network security and data sciences, Springer, Singapore, 2020; 183-193.
   Google Scholar
• Mohan NJ, Murugan R, Goel T. Deep learning for diabetic retinopathy detection: challenges and opportunities. Next Gener Healthcare Inf; 2022: 213:213–232.
   Google Scholar
• Umesh L, Mrunalini M, Shinde S. Review of image processing and machine learning techniques for eye disease detection and classification. Int Res J Eng Technol (IRJET). 2016;3(3):547–551.
   Google Scholar
• Dodo BI, Li Y, Kaba D, et al. Retinal layer segmentation in optical coherence tomography images. IEEE Access. 2019;7:152388–152398.
   Web of Science ®Google Scholar
• Li Q, Feng B, Xie L, et al. A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging. 2016;35(1):109–118.
   PubMed Web of Science ®Google Scholar
• Xu Y, Fan Y. Dual-channel asymmetric convolutional neural network for an efficient retinal blood vessel segmentation in eye fundus images. Biocybern Biomed Eng. 2022;42(2):695–706.
   Web of Science ®Google Scholar
• Gharaibeh N, Al-Hazaimeh OM, Nahar A-NB, et al. An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. Int J Signal Imaging Sys Eng. 2018;11(4):206–216.
   Web of Science ®Google Scholar
• Mohan NJ, Murugan R, Goel T, et al. Exudate detection with improved U-Net using fundus images. In 2021 international conference on computational performance evaluation (ComPE), IEEE, 2021; 560-564.
   Google Scholar
• Mohan NJ, Murugan R, Goel T, et al. Fast and robust exudate detection in retinal fundus images using extreme learning machine autoencoders and modified KAZE features. J Digit Imaging. 2022;35(3):1–18.
   PubMed Web of Science ®Google Scholar
• Mohan NJ, Murugan R, Goel T, et al. Exudate localization in retinal fundus images using modified speeded up robust features algorithm. In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). 2021: 367–371.
   Google Scholar
• Islam M, Dinh A, Wahid K, et al. Detection of potato diseases using image segmentation and multi-class support vector machine. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). 2017: 1–4.
   Google Scholar
• Dasgupta A, Singh S. A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). 2017: 248–251.
   Google Scholar
• Zhou M, Jin K, Wang S, et al. Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans Biomed Eng. 2017;65(3):521–527.
   PubMed Web of Science ®Google Scholar
• Zhong H, Jiao LC, Hou P. retinal vessel segmentation using nonsubsampled Contourlet Transform. Jisuanji Xuebao (Chinese Journal of Computers). 1911;34(3):574–582.
   Google Scholar
• Shanthamalar JJ, Ramani RG. Automatic blood vessel segmentation in retinal fundus images using image enhancement and dynamic gray-level thresholding. In proceedings of international conference on computational intelligence and data engineering, Springer, Singapore, 2022; 337-348.
   Google Scholar
• Jiang Z, Zhang H, Ko WY, et al. Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph. 2018;68:1–15.
   PubMed Web of Science ®Google Scholar
• Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: overview, challenges and the future. Classification in BioApps. 2018;26:323–350.
   Google Scholar
• Pekala M, Joshi N, Liu TA, et al. Deep learning based retinal OCT segmentation. Comput Biol Med 2019;114:103445.
   PubMed Web of Science ®Google Scholar
• Mohan NJ, Murugan R, Goel T. Machine learning algorithms for hypertensive retinopathy detection through retinal fundus images. In: Computer vision and recognition systems. Apple Academic Press; 2022. p. 39–67.
   Google Scholar
• Anupam A, Mohan NJ, Sahoo S, et al. Preliminary diagnosis of COVID-19 based on cough sounds using machine learning algorithms. In 2021 5th international conference on intelligent computing and control systems (ICICCS), IEEE, 2021; 1391-1397.
   Google Scholar
• Soomro TA, Afifi AJ, Zheng L, et al. Deep learning models for retinal blood vessels segmentation: a review. IEEE Access. 2019;7:71696–71717.
   Web of Science ®Google Scholar
• Alqudah AM. AOCT-NET: a convolutional network automated classification of multi-class retinal diseases using spectral-domain optical coherence tomography images. Med Biol Eng Comput. 2020;58(1):41–53.
   PubMed Web of Science ®Google Scholar
• Tayal A, Gupta J, Solanki A, et al. DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Syst. 2021;28:1417–1438.
   Web of Science ®Google Scholar
• Guo F, Li W, Kuang Z, et al. MES-Net: a new network for retinal image segmentation. Multimed Tools Appl. 2021;80(10):14767–14788.
   Web of Science ®Google Scholar
• Orujov F, Maskeliūnas R, Damaševičius R, et al. Fuzzy based image edge detection algorithm for blood vessel detection in retinal images. Appl Soft Comput. 2020;94:106452.
   Web of Science ®Google Scholar
• Vijayan T, Sangeetha M, Kumaravel A, et al. Gabor filter and machine learning based diabetic retinopathy analysis and detection. Microprocess Microsyst. 2020: 103353.
   Google Scholar
• Ramos-Soto O, Rodríguez-Esparza E, Balderas-Mata SE, et al. An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering. Comput Methods Programs Biomed. 2021;201:105949.
   PubMed Web of Science ®Google Scholar
• Pachade S, Porwal P, Kokare M, et al. Retinal vasculature segmentation and measurement framework for color fundus and SLO images. Biocybern Biomed Eng. 2020;40(3):865–900.
   Web of Science ®Google Scholar
• dos Santos JCM, Carrijo GA, de Fátima dos Santos Cardoso C, et al. Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter. Res Biomed Eng. 2020;36(2):107–119.
   Google Scholar
• Zhou C, Zhang X, Chen H. A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model. Comput Methods Programs Biomed. 2020;187:105231.
   PubMed Web of Science ®Google Scholar
• Tchinda BS, Tchiotsop D, Noubom M, et al. Retinal blood vessels segmentation using classical edge detection filters and the neural network. Inf Med Unlocked. 2021;23:100521.
   Google Scholar
• Saranya P, Prabakaran S, Kumar R, et al. Blood vessel segmentation in retinal fundus images for proliferative diabetic retinopathy screening using deep learning. Vis Comput. 2022;38(3):977–992.
   Web of Science ®Google Scholar
• Toptaş B, Hanbay D. Retinal blood vessel segmentation using pixel-based feature vector. Biomed Signal Process Control. 2021;70:103053.
   Web of Science ®Google Scholar
• Hakim L, Kavitha MS, Yudistira N, et al. Regularizer based on Euler characteristic for retinal blood vessel segmentation. Pattern Recognit Lett. 2021;149:83–90.
   Web of Science ®Google Scholar
• Rehman A, Harouni M, Karimi M, et al. Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors. Microsc Res Tech 2022;85(5):1899–1914.
   PubMed Web of Science ®Google Scholar
• Mohan NJ, Murugan R, Goel T, et al. A novel four-step feature selection technique for diabetic retinopathy grading. Phys Eng Sci Med. 2021;44(4):1351–1366.
   PubMedGoogle Scholar
• Sheet SSM, Tan T-S, As’ari MA, et al. Retinal disease identification using upgraded CLAHE filter and transfer convolution neural network. ICT Express. 2022;8(1):142–150.
   Web of Science ®Google Scholar
• Khanal A, Motevali S, Estrada R. Fully Automated Tree Topology Estimation and Artery-Vein Classification. arXiv preprint arXiv:2202.02382, 2022.
   Google Scholar
• Khaldi B, Aiadi O, Kherfi ML. Combining colour and grey-level co-occurrence matrix features: a comparative study. IET Image Proc. 2019;13(9):1401–1410.
   Web of Science ®Google Scholar
• Zhang X, Cui J, Wang W, et al. A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors. 2017;17(7):1474.
   PubMed Web of Science ®Google Scholar
• Pratiwi M, Harefa J, Nanda S. Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. Procedia Comput Sci. 2015;59:83–91.
   Google Scholar
• Tao H, Lu X. Smoky vehicle detection based on multi-scale block Tamura features. Signal Image Video Process. 2018;12(6):1061–1068.
   Web of Science ®Google Scholar
• Jaiswal AK, Srivastava R. A technique for image splicing detection using hybrid feature set. Multimed Tools Appl. 2020;79(17):11837–11860.
   Web of Science ®Google Scholar
• Tan K, Lee WS, Gan H, et al. Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes. Biosystems Eng. 2018;176:59–72.
   Web of Science ®Google Scholar
• Harini R, Sheela N. Feature extraction and classification of retinal images for automated detection of Diabetic Retinopathy. In 2016 s International Conference on Cognitive Computing and Information Processing (CCIP) IEEE. 2016: 1–4.
   Google Scholar
• Nath SS, Anoop BK, Sankar P. Classification of outer retinal layers based on KNN-classifier. In 2018 international conference on emerging trends and innovations In engineering And technological research (ICETIETR) IEEE 2018; 1-4.
   Google Scholar
• Banerjee S, Chowdhury AR. Case based reasoning in the detection of retinal abnormalities using decision trees. Procedia Comput Sci. 2015;46:402–408.
   Google Scholar
• Annunziata R, Garzelli A, Ballerini L, et al. Leveraging multi-scale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J Biomed Health Inform. 2016;20(4):1129–1138.
   PubMed Web of Science ®Google Scholar
• Quellec G, Al Hajj H, Lamard M, et al. ExplAIn: explanatory artificial intelligence for diabetic retinopathy diagnosis. Med Image Anal. 2021;72:102118.
   PubMed Web of Science ®Google Scholar
• Ramasamy LK, Padinjappurathu SG, Kadry S, et al. Detection of diabetic Retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier. PeerJ Comput Sci. 2021;7:e456.
   PubMedGoogle Scholar
• Bajwa MN, Singh GAP, Neumeier W, et al. G1020: a benchmark retinal fundus image dataset for computer-aided glaucoma detection. In 2020 international joint conference on neural networks (IJCNN) IEEE 2020; 1-7.
   Google Scholar
• Yang D, Martinez C, Visuña L, et al. Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci Rep. 2021;11(1):1–13.
   PubMedGoogle Scholar