ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

SOBEL Operator and PCA for Nearest Target of Retina Images


Affiliations
1 Department of Computer Science, Tiruppur Kumaran College for Women, India
2 Department of Computer Applications, Tiruppur Kumaran College for Women, India
     

   Subscribe/Renew Journal


In eye, innermost layer is retina. Various important anatomical structures are available in this. Different eye diseases like diabetic retinopathy, glaucoma, etc are indicated by this. For clinical study, patient screening, and diagnosing ocular diseases, physicians are assisted by vascular intersections and blood vessels extraction in retinal images. Retina image’s nearest template are detected using fuzzy neural network (FNN), Probabilistic neural network (PNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensemble in recent work. However, various factors like low contrast are having adverse effect on image quality. Accuracy is reduced by this and it is susceptible to errors owing to different human factors. For overcoming these issues, image denoising is introduced first in this work. Clustering algorithm is used for performing this denoising. Fuzzy clustering are used for removing noises present in samples. The, Sobel edge detection operator is used for detecting edges in retina images. To enrich retina image in image enhancement, enhanced linear contrast stretching is used. Mutual Information (MI) optimization is initialized as a coarse localization process using dimension reduction, where local optima are avoided and optimization domain is narrowed down. Then, Enhanced fuzzy neural network (FNN), Improved Probabilistic neural network (PNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensembling is used for recognizing retina image’s nearest template.

Keywords

Contrast Stretching, Diagnosing Ocular Diseases, Edge Detection, Retina Image, Eye Diseases.
Subscription Login to verify subscription
User
Notifications
Font Size

  • R. Deepa and N.K. Narayanan, “Detection of Microaneurysm in Retina Image using Machine Learning Approach”, Proceedings of International Conference on Innovative Trends in Information Technology, pp. 1-5, 2020.

  • D. Xiao, S. Yu and Y. Kanagasingam, “Retinal Hemorrhage Detection by Rule-Based and Machine Learning Approach”, Proceedings of International Conference on Engineering in Medicine and Biology Society, pp. 660-663, 2017.

  • Y. Gao, X. Yu and C. Wu, “Automated Segmentation of the Optic Disc from Retinal Image using Modified Local Intensity Clustering Model”, Proceedings of International Conference on Chinese Control and Decision, pp. 602-606, 2019.

  • C. Zhu, B. Zou and R. Zhao, “Retinal Vessel Segmentation in Colour Fundus Images using Extreme Learning Machine”, Computerized Medical Imaging and Graphics, Vol. 55, pp. 68-77, 2017.

  • K. Wisaeng and W. Sa Ngiamvibool, “Exudates Detection using Morphology Mean Shift Algorithm in Retinal Images”, IEEE Access, Vol. 7, pp. 11946-11958, 2019.

  • C. Saravanabhavan, T. Saravanan, D.B. Mariappan, S. Nagaraj and K.M. Baalamurugan, “Data Mining Model for Chronic Kidney Risks Prediction Based on Using NB-CbH”, Proceedings of IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 1023-1026, 2021.

  • P. Mehta, C. Petersen and J.C. Wen, “Automated Detection of Glaucoma with Interpretable Machine Learning using Clinical Data and Multi-Modal Retinal Images”, American Journal of Ophthalmology, Vol. 19, No. 2, pp. 1-20, 2020.

  • P. Yadav and N.P. Singh, “Classification of Normal and Abnormal Retinal Images by using Feature-Based Machine Learning Approach”, Proceedings of International Conference on Recent Trends in Communication, Computing, and Electronics, pp. 387-396, 2019.

  • S.P. Rajan, “Recognition of Cardiovascular Diseases through Retinal Images using Optic Cup to Optic Disc Ratio”, Pattern Recognition and Image Analysis, Vol. 30, No. 2, pp. 256-263, 2020.

  • S. Mishra and M. Banerjee, “Automatic Caption Generation of Retinal Diseases with Self-trained RNN Merge Model, Proceedings of International Conference on Advanced Computing and Systems for Security, pp. 1-10, 2020.

  • M. Javed, P. Nagabhushan, B.B. Chaudhuri and S.K. Singh, “Edge Based Enhancement of Retinal Images using an Efficient JPEG-Compressed Domain Technique”, Journal f Intelligent and Fuzzy Systems, Vol. 36, No. 1, pp. 541-556, 2019.

  • E. Tuba, L. Mrkela and M. Tuba, “Retinal Blood Vessel Segmentation by Support Vector Machine Classification”, Proceedings of International Conference on Radioelektronika, pp. 1-6, 2017.

  • K. Roy, S.S. Chaudhuri and P. Roy, P., Chatterjee, “Transfer Learning Coupled Convolution Neural Networks in Detecting Retinal Diseases using OCT Images”, Proceedings of International Conference on Intelligent Computing: Image Processing Based Applications, pp. 153-173, 2020.

  • J. Guo, S. Ren, Y. Shi and H. Wang, “Automatic Retinal Blood Vessel Segmentation based on Multi-Level Convolutional Neural Network”, Proceedings of International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp. 1-5, 2018.

  • K. Goel, M. Sehrawat and A. Agarwal, “Finding the Optimal Threshold Values for Edge Detection of Digital Images and Comparing among Bacterial Foraging Algorithm, Canny and Sobel Edge Detector”, Proceedings of International Conference on Computing, Communication and Automation, pp. 1076-1080, 2017.

  • S. Israni and S. Jain, “Edge Detection of License Plate using Sobel Operator”, Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques, pp. 3561-3563, 2016.

  • N. Mathur, S. Mathur and D. Mathur, “A Novel Approach to Improve Sobel Edge Detector”, Procedia Computer Science, Vol. 93, pp. 431-438, 2016.

  • M.G. Raman, N. Somu and A.P. Mathur, “Anomaly Detection in Critical Infrastructure using Probabilistic Neural Network”, Proceedings of International Conference on Applications and Techniques in Information Security, pp. 129-141, 2019.

  • Y. Sun and J. Chen, “Indoor Sound Source Localization with Probabilistic Neural Network”, IEEE Transactions on Industrial Electronics, Vol. 65, No. 8, pp. 6403-6413, 2017.

  • R.K.V. Bhupatiraju and V.S.S.S.S. Dhanikonda, “Transient-and Probabilistic Neural Network-Based Fault Classification in EHV Three-Terminal Lines”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 26, No. 2, pp. 974-986, 2018.

  • H.G. Han, X.L. Wu and Z. Liu, “Design of Self-Organizing Intelligent Controller using Fuzzy Neural Network”, IEEE Transactions on Fuzzy Systems, Vol. 26, No. 5, pp. 3097-3111, 2017.

  • J. Fei and D. Cao, “Adaptive Fractional Terminal Sliding Mode Controller for Active Power Filter using Fuzzy-Neural-Network”, Proceedings of International Conference on Knowledge and Systems Engineering, pp. 118-122, 2018.

  • A. Sharifian and M.J. Ghadi, “A New Method based on Type-2 Fuzzy Neural Network for Accurate Wind Power Forecasting under Uncertain Data”, Renewable Energy, Vol. 120, pp. 220-230, 2018.


Abstract Views: 172

PDF Views: 1




  • SOBEL Operator and PCA for Nearest Target of Retina Images

Abstract Views: 172  |  PDF Views: 1

Authors

B. Sivaranjani
Department of Computer Science, Tiruppur Kumaran College for Women, India
C. Kalaiselvi
Department of Computer Applications, Tiruppur Kumaran College for Women, India

Abstract


In eye, innermost layer is retina. Various important anatomical structures are available in this. Different eye diseases like diabetic retinopathy, glaucoma, etc are indicated by this. For clinical study, patient screening, and diagnosing ocular diseases, physicians are assisted by vascular intersections and blood vessels extraction in retinal images. Retina image’s nearest template are detected using fuzzy neural network (FNN), Probabilistic neural network (PNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensemble in recent work. However, various factors like low contrast are having adverse effect on image quality. Accuracy is reduced by this and it is susceptible to errors owing to different human factors. For overcoming these issues, image denoising is introduced first in this work. Clustering algorithm is used for performing this denoising. Fuzzy clustering are used for removing noises present in samples. The, Sobel edge detection operator is used for detecting edges in retina images. To enrich retina image in image enhancement, enhanced linear contrast stretching is used. Mutual Information (MI) optimization is initialized as a coarse localization process using dimension reduction, where local optima are avoided and optimization domain is narrowed down. Then, Enhanced fuzzy neural network (FNN), Improved Probabilistic neural network (PNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensembling is used for recognizing retina image’s nearest template.

Keywords


Contrast Stretching, Diagnosing Ocular Diseases, Edge Detection, Retina Image, Eye Diseases.

References