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A. Hoover, V. Kouznetsova, M. Goldbaum (1998)
Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter responseIEEE transactions on medical imaging, 19 3
I. Lázár, A. Hajdu (2015)
Segmentation of retinal vessels by means of directional response vector similarity and region growingComputers in biology and medicine, 66
R. Fante, T. Gardner, J. Sundstrom (2013)
Current and future management of diabetic retinopathy: a personalized evidence-based approach.Diabetes management, 3 6
M. Miri, Z. Amini, H. Rabbani, R. Kafieh (2017)
A Comprehensive Study of Retinal Vessel Classification Methods in Fundus ImagesJournal of Medical Signals and Sensors, 7
Yu Zhao, Xiao-Hong Wang, Xiaofang Wang, F. Shih (2014)
Retinal vessels segmentation based on level set and region growingPattern Recognit., 47
Thomas Dietterich (2000)
Multiple Classifier Systems, 1857
M. Cardoso, T. Arbel, A. Melbourne, H. Bogunović, P. Moeskops, Xinjian Chen, E. Schwartz, M. Garvin, E. Robinson, E. Trucco, M. Ebner, Yanwu Xu, A. Makropoulos, A. Desjardin, Tom Vercauteren (2017)
Fetal, Infant and Ophthalmic Medical Image Analysis, 10554
C. Srinidhi, P. Aparna, Jeny Rajan (2017)
Recent Advancements in Retinal Vessel SegmentationJournal of Medical Systems, 41
Javad Rahebi, F. Hardalaç (2014)
Retinal Blood Vessel Segmentation with Neural Network by Using Gray-Level Co-Occurrence Matrix-Based FeaturesJournal of Medical Systems, 38
R. Rubinstein, M. Zibulevsky, Michael Elad (2009)
Learning Sparse Dictionaries for Sparse Signal Approximation
Chengjun Liu, H. Wechsler (2002)
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognitionIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 11 4
Paweł Liskowski, K. Krawiec (2016)
Segmenting Retinal Blood Vessels With Deep Neural NetworksIEEE Transactions on Medical Imaging, 35
Qiaoliang Li, Bowei Feng, L. Xie, Ping Liang, Huisheng Zhang, Tianfu Wang (2016)
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal ImagesIEEE Transactions on Medical Imaging, 35
Kevis-Kokitsi Maninis, J. Pont-Tuset, Pablo Arbeláez, L. Gool (2016)
Deep Retinal Image Understanding
M. Javidi, H. Pourreza, A. Harati (2017)
Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representationComputer methods and programs in biomedicine, 139
S. Ourselin, Leo Joskowicz, M. Sabuncu, Gozde Unal, W. Wells (2017)
The 19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016)Medical image analysis, 41
(2013)
a personalized evidence-based approach,” Diabetes Manage
Mohammad Haghighat, S. Zonouz, M. Abdel-Mottaleb (2015)
CloudID: Trustworthy cloud-based and cross-enterprise biometric identificationExpert Syst. Appl., 42
Lei Zhang, Mark Fisher, Wenjia Wang (2015)
Retinal vessel segmentation using multi-scale textons derived from keypointsComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 45
Michal Wozniak, M. Graña, E. Corchado (2014)
A survey of multiple classifier systems as hybrid systemsInf. Fusion, 16
Y. Freund, R. Schapire (1996)
Experiments with a New Boosting Algorithm
Benson Lam, Yongsheng Gao, Alan Liew (2010)
General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity ModelingIEEE Transactions on Medical Imaging, 29
Rasha Alshehhi, P. Marpu, W. Woon (2016)
An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal ImagesMathematical Problems in Engineering, 2016
C. Lupascu, D. Tegolo, E. Trucco (2010)
FABC: Retinal Vessel Segmentation Using AdaBoostIEEE Transactions on Information Technology in Biomedicine, 14
Nagendra Singh, R. Srivastava (2016)
Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filterComputer methods and programs in biomedicine, 129
J. Tropp, Anna Gilbert (2007)
Signal Recovery From Random Measurements Via Orthogonal Matching PursuitIEEE Transactions on Information Theory, 53
Shuangling Wang, Yilong Yin, Guibao Cao, B. Wei, Yuanjie Zheng, Gongping Yang (2015)
Hierarchical retinal blood vessel segmentation based on feature and ensemble learningNeurocomputing, 149
Goksu Tuysuzoglu, Nazanin Moarref, Y. Yaslan (2016)
Ensemble based classifiers using dictionary learning2016 International Conference on Systems, Signals and Image Processing (IWSSIP)
Jiong Zhang, B. Dashtbozorg, E. Bekkers, J. Pluim, R. Duits, B. Romeny (2016)
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation ScoresIEEE Transactions on Medical Imaging, 35
L. Shen, L. Bai, M. Fairhurst (2007)
Gabor wavelets and General Discriminant Analysis for face identification and verificationImage Vis. Comput., 25
A. Budai, Rüdiger Bock, A. Maier, J. Hornegger, G. Michelson (2013)
Robust Vessel Segmentation in Fundus ImagesInternational Journal of Biomedical Imaging, 2013
R. Rubinstein, M. Zibulevsky, Michael Elad (2008)
Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit
Weihua Wang, Jingzhong Zhang, Wenyuan Wu, Shuang Zhou (2018)
An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point TrackingJournal of Medical Imaging and Health Informatics, 8
Ye Ren, Le Zhang, P. Suganthan (2016)
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]IEEE Computational Intelligence Magazine, 11
Yingfeng Zheng, M. He, N. Congdon (2012)
The worldwide epidemic of diabetic retinopathyIndian Journal of Ophthalmology, 60
Khan Khan, Amir Khaliq, A. Jalil, Muhammad Shahid (2018)
A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoisingPLoS ONE, 13
Thomas Dietterich (2000)
Ensemble Methods in Machine Learning
Taibou Sekou, Moncef Hidane, Julien Olivier, H. Cardot (2017)
Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques
J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, B. Ginneken (2004)
Ridge-based vessel segmentation in color images of the retinaIEEE Transactions on Medical Imaging, 23
A. Khan, K. Georges, Saed Rahaman, W. Abdela, A. Adesiyun (2018)
Prevalence and serotypes of Salmonella spp. on chickens sold at retail outlets in TrinidadPLoS ONE, 13
Roberto Vega, Gildardo Sánchez-Ante, L. Falcón-Morales, Juan Azuela, Elizabeth Guevara (2015)
Retinal vessel extraction using Lattice Neural Networks with dendritic processingComputers in biology and medicine, 58
Kuldeep Singh, Anubhav Gupta, Rajiv Kapoor (2015)
Fingerprint image super-resolution via ridge orientation-based clustered coupled sparse dictionariesJournal of Electronic Imaging, 24
Michael Elad, M. Aharon (2006)
Image Denoising Via Sparse and Redundant Representations Over Learned DictionariesIEEE Transactions on Image Processing, 15
J. Orlando, E. Prokofyeva, Matthew Blaschko (2017)
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus ImagesIEEE Transactions on Biomedical Engineering, 64
Christophe Chefd'Hotel, D. Tschumperlé, R. Deriche, O. Faugeras (2004)
Regularizing Flows for Constrained Matrix-Valued ImagesJournal of Mathematical Imaging and Vision, 20
G. Azzopardi, N. Strisciuglio, M. Vento, N. Petkov (2015)
Trainable COSFIRE filters for vessel delineation with application to retinal imagesMedical image analysis, 19 1
G. Bresnick, D. Mukamel, Dickinson Jc, David Cole (2000)
A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy.Ophthalmology, 107 1
(2018)
review of methods, datasets and evaluation metrics,” Comput
Zhang Li (2010)
Contrast Limited Adaptive Histogram EqualizationComputer Knowledge and Technology
(2000)
a statistical view of boosting,” Ann
R. Welikala, J. Dehmeshki, A. Hoppe, V. Tah, Samantha Mann, T. Williamson, S. Barman (2014)
Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classificationComputer methods and programs in biomedicine, 114 3
João Soares, J. Leandro, R. Junior, H. Jelinek, M. Cree (2005)
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classificationIEEE Transactions on Medical Imaging, 25
Kuldeep Singh, D. Vishwakarma, G. Walia (2019)
Blind image deblurring via gradient orientation-based clustered coupled sparse dictionariesPattern Analysis and Applications, 22
Sohini Roychowdhury, D. Koozekanani, K. Parhi (2015)
Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage ClassificationIEEE Journal of Biomedical and Health Informatics, 19
S. Moccia, E. Momi, Sara Hadji, L. Mattos (2018)
Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metricsComputer methods and programs in biomedicine, 158
Nogol Memari, Abd Ramli, M. Saripan, S. Mashohor, M. Moghbel (2017)
Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifierPLoS ONE, 12
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum (1989)
Detection of blood vessels in retinal images using two-dimensional matched filters.IEEE transactions on medical imaging, 8 3
Kuldeep Singh, Rajiv Kapoor, R. Nayar (2015)
Fingerprint denoising using ridge orientation based clustered dictionariesNeurocomputing, 167
D. Marín, A. Aquino, M. Gegúndez-Arias, J. Bravo (2011)
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based FeaturesIEEE Transactions on Medical Imaging, 30
Jiong Zhang, Yuan Chen, E. Bekkers, Meili Wang, B. Dashtbozorg, B. Romeny (2017)
Retinal vessel delineation using a brain-inspired wavelet transform and random forestPattern Recognit., 69
J. Friedman (2000)
Special Invited Paper-Additive logistic regression: A statistical view of boostingAnnals of Statistics, 28
Abstract.Accurate segmentation of the blood vessels from a retinal image plays a significant role in the prudent examination of the vessels. A supervised blood vessel segmentation technique to extract blood vessels from a retinal image is proposed. The uniqueness of the work lies in the implementation of feature-oriented dictionary learning and sparse coding for the accurate classification of the pixels in an image. First, the image is split into patches and for each patch, Gabor features are extracted at multiple scales and orientations to create a set of feature vectors (this is done for the whole training set). Then, an overcomplete feature-oriented dictionary is trained from the extracted Gabor features (selected on the basis of standard deviation) using the generalized K-means for singular value decomposition dictionary learning technique. Sparse representations are subsequently calculated for the corresponding features from the dictionary. The combination of feature vectors and sparse representations constitutes the final feature vector. This feature vector is then fed into the ensemble classifier for the classification of pixels into either blood vessel pixels or nonblood vessel pixels. The method is evaluated on publicly available DRIVE and STARE datasets, as they contain ground truth images precisely marked by experts. The results obtained on both of the datasets show that the proposed technique outperforms most of the state-of-the-art techniques reported in the literature.
Journal of Medical Imaging – SPIE
Published: Oct 1, 2019
Keywords: retinal blood vessel segmentation; feature-oriented dictionary learning; sparse coding; Gabor features
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