Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
General Research Article

Efficient Vessel Segmentation Based on Proposed Adaptive Conditional Random Field Model

Author(s): Laxmi Math* and Ruksar Fatima

Volume 15, Issue 5, 2022

Published on: 20 October, 2020

Article ID: e060422187021 Pages: 11

DOI: 10.2174/2666255813999201020143604

Price: $65

Abstract

Objectives: The objective is to provide a precise segmentation technique based on ACRF, which can handle the variations between major and minor vessels and reduce the interference present in the model due to overfitting and can provide a high-quality reconstructed image. Therefore, a robust method with statistical properties needs to be presented to enhance the performance of the model. Moreover, a statistical framework is required to classify images precisely.

Methods: The Adaptive Conditional Random Field (ACRF) model is used to detect DR disease in the early stages. Here, major vessel potential and minor vessel potential features are extracted for precise segmentation of vessel and non-vessel regions. This feature enhances the efficiency of the model. These major vessel and minor vessel potential features rebuild the retinal vasculature parts precisely and help to capture the contextual information present in the ground truth and label images. This method utilizes an ACRF model to reduce interference and computation complexity. Here, two efficient features are extracted to segment fundus images efficiently, such as major vessel potential and minor vessel potential. The proposed ACRF model can provide the design patterns for both input images and labels with the help of major vessel potential, unlike state-of-arttechniques, which provide patterns for only labels and model the contextual information only in labels, which is essential while performing vessel segmentation.

Results: The performance results are tested on the DRIVE dataset. Experimental results verify the superiority of the proposed vessel segmentation technique based on the ACRF model in terms of accuracy, sensitivity, specificity, and F1-measure and segmentation quality.

Conclusion: A highly efficient vessel segmentation technique is evaluated to describe major and minor vessel regions efficiently based on the ACRF to recognize DR in early stages and to ensure an effective diagnosis using eye Fundus Images. The segmentation process decomposes input images into RGB components through histogram labels based on the proposed ACRF model. Here, the Gabor filtering approach is used for pre-processing and predicting parameters. The proposed segmentation method can provide the smooth boundaries of minor and major vessel regions. The proposed ACRF model can provide the design patterns for both input images and labels with the help of major vessel potentials, unlike state-of-art-techniques, which provide patterns for only labels and model the contextual information only in labels.

Keywords: Vessel segmentation, major vessel potentials, minor vessel potentials, adaptive conditional random field.

Graphical Abstract

[1]
R.D. Jager, "Age-related macular degeneration", N. Engl. J. Med., vol. 358, no. 24, pp. 2606-2617, 2008.
[2]
S. Schmitz-Valckenberg, J.S. Steinberg, M. Fleckenstein, and S. Visvalingam, "Combined confocal scanning laser ophthalmoscopy and spectral-domain optical coherence tomography imaging of reticular drusen associated with age-related macular degeneration", Ophthalmology, vol. 117, no. 6, pp. 1169-1176, 2010.
[3]
International Diabetes Federation IDF diabetes atlas-7th.edition.www.diabetesatlas.org
[4]
E.V. Carrera, A. González, and R. Carrera, "Automated detection of DR using SVM", In: 2017 IEEE XXIV International Conference on Elec-tronics, Electrical Engineering and Computing (INTERCON), 2017, pp. 1-4. Cusco
[5]
S.S. Chorage, and S.S. Khot, "Detection of DR and cataract by vessel extraction from fundus images", In: 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), 2017, pp. 638-641. Coimbatore
[http://dx.doi.org/10.1109/ICECA.2017.8203617]
[6]
S. Yu, D. Xiao, and Y. Kanagasingam, “Exudate detection for DR with convolutional neural networks”, 39th Annual International Confer-ence of the IEEE Engineering in Medicine and Biology Society., EMBC: Seogwipo, 2017, pp. 1744-1747.
[7]
Z.A. Omar, M. Hanafi, S. Mashohor, N.F.M. Mahfudz, and M. Muna’im, "Automatic DR detection and classification system", In: 2017 7th IEEE International Conference on System Engineering and Technology (ICSET) Shah Alam 2017, pp. 162-166.
[8]
X. Ren, "Drusen Segmentation From Retinal Images via Supervised Feature Learning", IEEE Access, vol. 6, pp. 2952-2961, 2018.
[http://dx.doi.org/10.1109/ACCESS.2017.2786271]
[9]
N. Karami, and H. Rabbani, "A dictionary learning based method for detection of DR in color fundus images", In: 10th Iranian Conference on Machine Vision and Image Processing (MVIP) Isfahan,, 2017, pp. 119-122.
[10]
T.A. Soomro, "Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network", In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2017, pp. 1-8. Sydney, NSW
[http://dx.doi.org/10.1109/DICTA.2017.8227413]
[11]
P. Costa, A. Galdran, M.I. Meyer, M. Niemeijer, M. Abramoff, A.M. Mendonca, and A. Campilho, "End-to-End Adversarial Retinal Image Synthesis", IEEE Trans. Med. Imaging, vol. 37, no. 3, pp. 781-791, 2018.
[http://dx.doi.org/10.1109/TMI.2017.2759102] [PMID: 28981409]
[12]
P. Kokare, "Wavelet based automatic exudates detection in DR", In: 2017 International Conference on Wireless Communications, Signal Pro-cessing and Networking (WiSPNET), 2017, pp. 1022-1025. Chennai
[http://dx.doi.org/10.1109/WiSPNET.2017.8299917]
[13]
M.D. Abràmoff, M.K. Garvin, and M. Sonka, "Retinal imaging and image analysis", IEEE Rev. Biomed. Eng., vol. 3, pp. 169-208, 2010.
[http://dx.doi.org/10.1109/RBME.2010.2084567] [PMID: 22275207]
[14]
T.A. Ciulla, A.G. Amador, and B. Zinman, "DR and diabetic macular edema: Pathophysiology, screening, and novel therapies", Diabetes Care, vol. 26, no. 9, pp. 2653-2664, 2003.
[http://dx.doi.org/10.2337/diacare.26.9.2653] [PMID: 12941734]
[15]
M. Omar, Diabetic eye screening in Malaysia: Findings from the National Health and Morbidity Survey Diabetic eye screening in Malay-sia: findings from the National Health and Morbidity Survey 2006.
[16]
D.C. Klonoff, and D.M. Schwartz, "An economic analysis of interventions for diabetes", Diabetes Care, vol. 23, no. 3, pp. 390-404, 2000.
[http://dx.doi.org/10.2337/diacare.23.3.390] [PMID: 10868871]
[17]
Z. Feng, D. Nie, L. Wang, and D. Shen, "Semi-supervised learning for pelvic MR image segmentation based on multi-task residual fully convolutional networks IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018pp. 885-888 Washington, DC",
[http://dx.doi.org/10.1109/ISBI.2018.8363713]
[18]
R. Kamimura, "Potential layer-wise supervised learning for training multi-layered neural networks", In: 2017 International Joint Conference on Neural Networks (IJCNN), 2017, pp. 2568-2575. Anchorage, AK
[http://dx.doi.org/10.1109/IJCNN.2017.7966169]
[19]
F.A. Breve, and D.C.G. Pedronette, "Combined unsupervised and semi-supervised learning for data classification IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016pp. 1-6 Vietri sul Mare",
[http://dx.doi.org/10.1109/MLSP.2016.7738877]
[20]
K.M. Adal, P.G. van Etten, J.P. Martinez, K.W. Rouwen, K.A. Vermeer, and L.J. van Vliet, "An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images", IEEE Trans. Biomed. Eng., vol. 65, no. 6, pp. 1382-1390, 2018.
[http://dx.doi.org/10.1109/TBME.2017.2752701] [PMID: 28922110]
[21]
American Diabetes Association, Data from the 2011 national diabetes fact sheet., 2011.http://www.diabetes.org/diabetes-basics/diabetes-statistics/
[22]
J. Staal, M.D. Abràmoff, M. Niemeijer, M.A. Viergever, and B. van Ginneken, "Ridge-based vessel segmentation in color images of the retina", IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501-509, 2004.
[http://dx.doi.org/10.1109/TMI.2004.825627] [PMID: 15084075]
[23]
L. Math, and R. Fatima, "Segmentation Implementation using Adaptive Conditional Random Field Model Considering Fundus Images for Diabetic Retinopathy Detection " 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology ", In: (RTEICT), Bangalore, India,, 2018, pp. 2618-2622.
[http://dx.doi.org/10.1109/RTEICT42901.2018.9012438]

Rights & Permissions Print Cite
Article Metrics
19
2
© 2024 Bentham Science Publishers | Privacy Policy