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Sidhu RK, Sachdeva J, Katoch D. Segmentation of retinal blood vessels by a novel hybrid technique- Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Microvasc Res 2023; 148:104477. [PMID: 36746364 DOI: 10.1016/j.mvr.2023.104477] [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: 06/09/2022] [Revised: 12/22/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023]
Abstract
Diabetic Retinopathy is a persistent disease of eyes that may lead to permanent loss of sight. In this paper, methodology is proposed to segment region of interest (ROI) i.e. new blood vessels in fundus images of retina of Diabetic Retinopathy (DR). The database of 50 fundus retinal images of healthy subjects and DR patients is fetched from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. The experimental set up consists of three set of experiments for the disease. For DR, in the first stage of automated blood vessel segmentation, gray-scale image is produced from the colored image using Principal Component Analysis (PCA) in the preprocessing step. The contrast enhancement by the Contrast Limited Adaptive Histogram Equalization (CLAHE) highlights the retinal blood vessels in the gray-scale image i.e. it unsheathed newly formed retinal blood vessels whereas PCA preserved their texture and color discrimination in DR images. The expert ophthalmologist(s) scrutiny on both internet repository and real time data acted as the gold standard for further analysis and formation of the proposed method. Further, ophthalmologists ascertained the forming of new blood vessels only on the disc region and divulging them, which were impossible with the naked eye. These operations help in extracting retinal blood vessels present on the disc and non-disc region of the image. The comparison of the results are done with the state of art methods like watershed transform. It is observed from the results that the new blood vessels are better segmented by the proposed methodology and are marked by the experienced ophthalmologist for validation. Further, for quantitative analysis, the features are extracted from new blood vessels as they are crucial for scientific interpretation. The results of the features lie in permissible limits such as no. of segments vary from 2 to 5 and length of segments varies from 49 to 164 pixels. Similarly, other features such as gray level of new blood vessels lie in 0.296-0.935 normalized range, coefficient with variations in gray level in the range of 0.658-10.10 and distance from vessel origin lie in the range of 56-82 pixels respectively. Both quantitative and qualitative results show that the methodologies proposed boosted the ophthalmic and clinical diagnosis. The developed method further handled the false detection of vessels near the optic disk boundary, under-segmentation of thin vessels, detection of pathological anomalies such as exudates, micro-aneurysms and cotton wool spots. From the numerical analysis, ophthalmologist extracted the information of number of vessels formed, length of the new vessels, observation that the new vessels appearing are less homogenous than the normal vessels. Also about the new vessels, whether they lie on the centre of disc region or towards its edges. These parameters lie as per the findings of the ophthalmologists on retinal images and automated detection helped in monitoring and comprehensive patient assessment. The experimental results show case that the proposed method has higher sensitivity, specificity and accuracy as compared to state of art methods i.e. 0.9023, 0.9610 and 0.9921, respectively. Similar results are obtained on retinal fundus images of PGIMER Chandigarh with sensitivity-0.9234, specificity-0.9955 and accuracy-0.9682.
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Affiliation(s)
- R K Sidhu
- Department of Electronics and Communication Engineering, Chandigarh University, Mohali, India.
| | - Jainy Sachdeva
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering & Technology, Patiala, India.
| | - D Katoch
- Department of Ophthalmology, Advanced Eye Centre, PGIMER, Chandigarh, India.
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Retinal blood vessel segmentation by using the MS-LSDNet network and geometric skeleton reconnection method. Comput Biol Med 2023; 153:106416. [PMID: 36586230 DOI: 10.1016/j.compbiomed.2022.106416] [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: 08/23/2022] [Revised: 11/21/2022] [Accepted: 12/04/2022] [Indexed: 12/29/2022]
Abstract
Automatic retinal blood vessel segmentation is a key link in the diagnosis of ophthalmic diseases. Recent deep learning methods have achieved high accuracy in vessel segmentation but still face challenges in maintaining vascular structural connectivity. Therefore, this paper proposes a novel retinal blood vessel segmentation strategy that includes three stages: vessel structure detection, vessel branch extraction and broken vessel segment reconnection. First, we propose a multiscale linear structure detection network (MS-LSDNet), which improves the detection ability of fine blood vessels by learning the types of rich hierarchical features. In addition, to maintain the connectivity of the vascular structure in the process of binarization of the vascular probability map, an adaptive hysteresis threshold method for vascular extraction is proposed. Finally, we propose a vascular tree structure reconstruction algorithm based on a geometric skeleton to connect the broken vessel segments. Experimental results on three publicly available datasets show that compared with current state-of-the-art algorithms, our strategy effectively maintains the connectivity of retinal vascular tree structure.
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García-Sierra R, López-Lifante VM, Isusquiza Garcia E, Heras A, Besada I, Verde Lopez D, Alzamora MT, Forés R, Montero-Alia P, Ugarte Anduaga J, Torán-Monserrat P. Automated Systems for Calculating Arteriovenous Ratio in Retinographies: A Scoping Review. Diagnostics (Basel) 2022; 12:diagnostics12112865. [PMID: 36428925 PMCID: PMC9689345 DOI: 10.3390/diagnostics12112865] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/29/2022] [Accepted: 11/14/2022] [Indexed: 11/22/2022] Open
Abstract
There is evidence of an association between hypertension and retinal arteriolar narrowing. Manual measurement of retinal vessels comes with additional variability, which can be eliminated using automated software. This scoping review aims to summarize research on automated retinal vessel analysis systems. Searches were performed on Medline, Scopus, and Cochrane to find studies examining automated systems for the diagnosis of retinal vascular alterations caused by hypertension using the following keywords: diagnosis; diagnostic screening programs; image processing, computer-assisted; artificial intelligence; electronic data processing; hypertensive retinopathy; hypertension; retinal vessels; arteriovenous ratio and retinal image analysis. The searches generated 433 articles. Of these, 25 articles published from 2010 to 2022 were included in the review. The retinographies analyzed were extracted from international databases and real scenarios. Automated systems to detect alterations in the retinal vasculature are being introduced into clinical practice for diagnosis in ophthalmology and other medical specialties due to the association of such changes with various diseases. These systems make the classification of hypertensive retinopathy and cardiovascular risk more reliable. They also make it possible for diagnosis to be performed in primary care, thus optimizing ophthalmological visits.
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Affiliation(s)
- Rosa García-Sierra
- Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain
- Multidisciplinary Research Group in Health and Society GREMSAS (2017 SGR 917), 08007 Barcelona, Spain
- Nursing Department, Faculty of Medicine, Universitat Autònoma de Barcelona, Campus Bellaterra, 08193 Barcelona, Spain
- Primary Care Group, Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain
| | - Victor M. López-Lifante
- Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain
- Palau-solità i Plegamans Primary Healthcare Centre, Palau-solità i Plegamans, Gerència d’Àmbit d’Atenció Primària Metropolitana Nord, Institut Català de la Salut, 08184 Barcelona, Spain
- Correspondence:
| | | | - Antonio Heras
- Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain
- Primary Healthcare Centre Riu Nord-Riu Sud, Gerència d’Àmbit d’Atenció Primària Metropolitana Nord, Institut Català de la Salut, Santa Coloma de Gramenet, 08921 Barcelona, Spain
| | - Idoia Besada
- ULMA Medical Technologies, S. Coop, 20560 Onati, Spain
| | - David Verde Lopez
- Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), 08007 Barcelona, Spain
| | - Maria Teresa Alzamora
- Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain
- Primary Healthcare Centre Riu Nord-Riu Sud, Gerència d’Àmbit d’Atenció Primària Metropolitana Nord, Institut Català de la Salut, Santa Coloma de Gramenet, 08921 Barcelona, Spain
| | - Rosa Forés
- Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain
| | - Pilar Montero-Alia
- Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain
| | | | - Pere Torán-Monserrat
- Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain
- Multidisciplinary Research Group in Health and Society GREMSAS (2017 SGR 917), 08007 Barcelona, Spain
- Primary Care Group, Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain
- Department of Medicine, Faculty of Medicine, Universitat de Girona, 17004 Girona, Spain
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Zhang J, Zhang Y, Qiu H, Xie W, Yao Z, Yuan H, Jia Q, Wang T, Shi Y, Huang M, Zhuang J, Xu X. Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation. Front Med (Lausanne) 2021; 8:761050. [PMID: 34950679 PMCID: PMC8688400 DOI: 10.3389/fmed.2021.761050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/05/2021] [Indexed: 11/18/2022] Open
Abstract
Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.
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Affiliation(s)
- Jiawei Zhang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shanghai key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, China
| | - Yanchun Zhang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
- College of Engineering and Science, Victoria University, Melbourne, VIC, Australia
| | - Hailong Qiu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wen Xie
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zeyang Yao
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Haiyun Yuan
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qianjun Jia
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Tianchen Wang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Yiyu Shi
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Meiping Huang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jian Zhuang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiaowei Xu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China
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Huang H, Yang G, Zhang W, Xu X, Yang W, Jiang W, Lai X. A Deep Multi-Task Learning Framework for Brain Tumor Segmentation. Front Oncol 2021; 11:690244. [PMID: 34150660 PMCID: PMC8212784 DOI: 10.3389/fonc.2021.690244] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 05/17/2021] [Indexed: 11/25/2022] Open
Abstract
Glioma is the most common primary central nervous system tumor, accounting for about half of all intracranial primary tumors. As a non-invasive examination method, MRI has an extremely important guiding role in the clinical intervention of tumors. However, manually segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects the implementation of follow-up diagnosis and treatment plans. With the development of deep learning, medical image segmentation is gradually automated. However, brain tumors are easily confused with strokes and serious imbalances between classes make brain tumor segmentation one of the most difficult tasks in MRI segmentation. In order to solve these problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion module in the framework to accurately segment brain tumors. In this framework, we have added a distance transform decoder based on the V-Net, which can make the segmentation contour generated by the mask decoder more accurate and reduce the generation of rough boundaries. In order to combine the different tasks of the two decoders, we weighted and added their corresponding loss functions, where the distance map prediction regularized the mask prediction. At the same time, the multi-depth fusion module in the encoder can enhance the ability of the network to extract features. The accuracy of the model will be evaluated online using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets. This method obtains high-quality segmentation results, and the average Dice is as high as 78%. The experimental results show that this model has great potential in segmenting brain tumors automatically and accurately.
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Affiliation(s)
- He Huang
- College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Wenbo Zhang
- College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaomei Xu
- College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Weiji Yang
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Weiwei Jiang
- College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaobo Lai
- College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, China
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7
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Torrens J, von Wirth T. Experimentation or projectification of urban change? A critical appraisal and three steps forward. URBAN TRANSFORMATIONS 2021; 3:8. [PMID: 34608448 PMCID: PMC8479717 DOI: 10.1186/s42854-021-00025-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 08/29/2021] [Indexed: 05/19/2023]
Abstract
Urban experimentation has proliferated in recent years as a response to sustainability challenges and renewed pressures on urban governance. In many European cities, diverse and rapidly changing experimental forms (e.g. urban living laboratories, pilots, trials, experimental districts) are becoming commonplace, addressing ambitious goals for smartness, circularity, and liveability. Academically, there is a growing concern for moving beyond the focus on individual experiments and the insistence on upscaling their primary transformation mechanism. However, the phenomena of 'projectification' - whereby project-based forms of organising have become ubiquitous, shaping expectations about experimentation - is increasingly perceived as a barrier. Nevertheless, how specifically experimentation and projectification intersect remains unclear. Our theoretical perspective examines how the widespread tendency towards projectification shapes urban experimentation and the potential implications for urban transformations. It problematises the current wave of experimentation and how it contributes to the projectification of urban change processes. We present three steps to redress this issue and indicate directions for future research.
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Affiliation(s)
- Jonas Torrens
- Faculty of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Timo von Wirth
- Erasmus University Rotterdam, Erasmus School of Social and Behavioural Sciences, Dutch Research Institute for Transitions (DRIFT), Rotterdam, The Netherlands
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Wang D, Haytham A, Pottenburgh J, Saeedi O, Tao Y. Hard Attention Net for Automatic Retinal Vessel Segmentation. IEEE J Biomed Health Inform 2020; 24:3384-3396. [DOI: 10.1109/jbhi.2020.3002985] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Tang X, Zhong B, Peng J, Hao B, Li J. Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106353] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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10
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Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks. Symmetry (Basel) 2020. [DOI: 10.3390/sym12060894] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, Hessian, and difference of Gaussian values. A post-processing technique depending on mathematical morphological operators is used to optimise the segmentation. Moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image. The proposed method is tested on three known datasets: Digital Retinal Image for Extraction (DRIVE), Structure Analysis of the Retina (STARE), and CHASED_DB1 datasets. The experimental results, both visual and quantitative, testify to the robustness of the proposed method. This proposed method achieved 0.9607, 0.7542, and 0.9843 in DRIVE, 0.9632, 0.7806, and 0.9825 on STARE, 0.9577, 0.7585 and 0.9846 in CHASE_DB1, with respectable accuracy, sensitivity, and specificity performance metrics. Furthermore, they testify that the method is superior to seven similar state-of-the-art methods.
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Kaur S, Mann KS. Retinal Vessel Segmentation Using an Entropy-Based Optimization Algorithm. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2020. [DOI: 10.4018/ijhisi.2020040105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This article presents an algorithm for the segmentation of retinal blood vessels for the detection of diabetic retinopathy eye diseases. This disease occurs in patients with untreated diabetes for a long time. Since this disease is related to the retina, it can eventually lead to vision impairment. The proposed algorithm is a supervised learning method of blood vessels segmentation in which the classification system is trained with the features that are extracted from the images. The proposed system is implemented on the images of DRIVE, STARE and CHASE_DB1 databases. The segmentation is done by forming clusters with the features of patterns. The features were extracted using independent component analysis and the classification is performed by support vector machines (SVM). The results of the parameters are grouped by accuracy, sensitivity, specificity, positive predictive value, false positive rate and are compared with particle swarm optimization (PSO), the firefly optimization algorithm (FA) and the lion optimization algorithm (LOA).
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Yu L, Jin M, Zhou K. Multi-channel biomimetic visual transformation for object feature extraction and recognition of complex scenes. APPL INTELL 2019. [DOI: 10.1007/s10489-019-01550-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Primitivo D, Alma R, Erik C, Arturo V, Edgar C, Marco PC, Daniel Z. A hybrid method for blood vessel segmentation in images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.06.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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14
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Diabetic retinopathy techniques in retinal images: A review. Artif Intell Med 2018; 97:168-188. [PMID: 30448367 DOI: 10.1016/j.artmed.2018.10.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 10/08/2018] [Accepted: 10/24/2018] [Indexed: 12/23/2022]
Abstract
The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy.We give a detailed review of 79 algorithms for detecting different features of diabetic retinopathy during the last eight years.
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Khan KB, Khaliq AA, Jalil A, Iftikhar MA, Ullah N, Aziz MW, Ullah K, Shahid M. A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Appl 2018. [DOI: 10.1007/s10044-018-0754-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Chamoso P, Rodríguez S, de la Prieta F, Bajo J. Classification of retinal vessels using a collaborative agent-based architecture. AI COMMUN 2018. [DOI: 10.3233/aic-180772] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Pablo Chamoso
- Department of Computer Science, University of Salamanca, IBSAL/BISITE Research Group. Edificio I+D+I, 37007, Salamanca, Spain. E-mails: , ,
| | - Sara Rodríguez
- Department of Computer Science, University of Salamanca, IBSAL/BISITE Research Group. Edificio I+D+I, 37007, Salamanca, Spain. E-mails: , ,
| | - Fernando de la Prieta
- Department of Computer Science, University of Salamanca, IBSAL/BISITE Research Group. Edificio I+D+I, 37007, Salamanca, Spain. E-mails: , ,
| | - Javier Bajo
- Department of Artificial Intelligence, Technical University of Madrid, Campus Montegancedo, Boadill a del Monte, 28660, Madrid, Spain. E-mail:
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Kalaie S, Gooya A. Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 151:139-149. [PMID: 28946995 DOI: 10.1016/j.cmpb.2017.08.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Revised: 07/27/2017] [Accepted: 08/21/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases. METHODS In this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation. RESULTS The performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates. CONCLUSIONS This technique results in a classifier with high precision and recall when comparing it with Xu's method.
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Affiliation(s)
- Soodeh Kalaie
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
| | - Ali Gooya
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK
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Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method. BIOMED RESEARCH INTERNATIONAL 2017; 2017:1263056. [PMID: 28840122 PMCID: PMC5559923 DOI: 10.1155/2017/1263056] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 05/25/2017] [Accepted: 07/02/2017] [Indexed: 11/18/2022]
Abstract
As a nonintrusive method, the retina imaging provides us with a better way for the diagnosis of ophthalmologic diseases. Extracting the vessel profile automatically from the retina image is an important step in analyzing retina images. A novel hybrid active contour model is proposed to segment the fundus image automatically in this paper. It combines the signed pressure force function introduced by the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model with the local intensity property introduced by the Local Binary fitting (LBF) model to overcome the difficulty of the low contrast in segmentation process. It is more robust to the initial condition than the traditional methods and is easily implemented compared to the supervised vessel extraction methods. Proposed segmentation method was evaluated on two public datasets, DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structured Analysis of the Retina) (the average accuracy of 0.9390 with 0.7358 sensitivity and 0.9680 specificity on DRIVE datasets and average accuracy of 0.9409 with 0.7449 sensitivity and 0.9690 specificity on STARE datasets). The experimental results show that our method is effective and our method is also robust to some kinds of pathology images compared with the traditional level set methods.
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19
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Retinal vessel segmentation in colour fundus images using Extreme Learning Machine. Comput Med Imaging Graph 2017; 55:68-77. [DOI: 10.1016/j.compmedimag.2016.05.004] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Accepted: 05/17/2016] [Indexed: 11/23/2022]
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Cruz-Aceves I, Hernandez-Aguirre A, Valdez SI. On the performance of nature inspired algorithms for the automatic segmentation of coronary arteries using Gaussian matched filters. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.01.030] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zaki WMDW, Zulkifley MA, Hussain A, Halim WHW, Mustafa NBA, Ting LS. Diabetic retinopathy assessment: Towards an automated system. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2015.09.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Lázár I, Hajdu A. Segmentation of retinal vessels by means of directional response vector similarity and region growing. Comput Biol Med 2015; 66:209-21. [PMID: 26432200 DOI: 10.1016/j.compbiomed.2015.09.008] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2015] [Revised: 09/08/2015] [Accepted: 09/09/2015] [Indexed: 10/23/2022]
Abstract
This paper presents a novel retinal vessel segmentation method. Opposed to the general approach in similar directional methods, where only the maximal or summed responses of a pixel are used, here, the directional responses of a pixel are considered as a vector. The segmentation method is a unique region growing procedure which combines a hysteresis thresholding scheme with the response vector similarity of adjacent pixels. A vessel score map is constructed as the combination of the statistical measures of the response vectors and its local maxima to provide the seeds for the region growing procedure. A nearest neighbor classifier based on a rotation invariant response vector similarity measure is used to filter the seed points. Many techniques in the literature that capture the Gaussian-like cross-section of vessels suffer from the drawback of giving false high responses to the steep intensity transitions at the boundary of the optic disc and bright lesions. To overcome this issue, we also propose a symmetry constrained multiscale matched filtering technique. The proposed vessel segmentation method has been tested on three publicly available image sets, where its performance proved to be competitive with the state-of-the-art and comparable to the accuracy of a human observer, as well.
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Affiliation(s)
- István Lázár
- Faculty of Informatics, University of Debrecen, 4010 Debrecen, Hungary.
| | - András Hajdu
- Faculty of Informatics, University of Debrecen, 4010 Debrecen, Hungary.
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Raja MAZ, Khan JA, Siddiqui A, Behloul D, Haroon T, Samar R. Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2014.10.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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