Wyniki wyszukiwania - Biblioteka Nauki

1

Segmentation of fundus eye images : detection of microaneurysms and blood vessels using mathematical morphology

100%

Stąpor K. , Świtoński A.

Archiwum Informatyki Teoretycznej i Stosowanej

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2004

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tom T. 16, z. 2

77-92

EN

In this paper the new method for automatic segmentation of fundus eye images are proposed: one for the detection of microaneurysms, the second for the detection of blood vessels. Both rely on tools from mathematical, grayscale morphology. The proposed method can be integrated in a tool for diagnosis of diabetic retinopathy which is under development.

PL

W artykule przedstawiono nowe metody automatycznej segmentacji obrazów dna oka pozyskanych z funduskamery: jedna dla detekcji mikroaneurazymów, druga dla naczyń krwionośnych na siatkówce oka. Metoda detekcji mikroaneurazymów składa się z następujących kroków: 1) przetwarzanie wstępne, 2) detekcja markerów dla transformaty wodnej, 3) transformata wodna, 4) klasyfikacja. W celu automatycznej detekcji markerów wewnętrznych dla transformaty wodnej zdefiniowano nowe przekształcenie trafi-nie-trafi w morfologii wieloodcieniowej. Uzyskana średnia sprawność metody wynosi 89,4% i jest wyższa od wyników uzyskanych w metodach poprzednich (84%). Metoda detekcji naczyń krwionośnych składa się z następujących kroków: 1) lokalna poprawa kontrastu, 2) odszumianie, 3) detekcja naczyń, 4)klasyfikacja. W celu odszumienia zastosowano operacje złożone ze specjalnie zaprojektowanych operacji dylatacji i erozji wieloodcieniowej. Sama detekcja naczyń wykorzystuje operację czubek-kapelusza. Opracowane algorytmy detekcji stanowić będą podstawowe moduły systemu komputerowego wspomagającego diagnozowanie jak również monitorowanie tej retinopatii cukrzycowej.

2

Involvement of serum anti-retinal antibodies in the pathophysiology of diabetic retinopathy: a pilot study

88%

Weglarz M. , Romanowska-Dixon B. , Wilanska J. , Sanak M. , Karska-Basta I.

Journal of Physiology and Pharmacology

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2020

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tom 71

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nr 5

3

An intelligible deep convolution neural network based approach for classification of diabetic retinopathy

88%

Sharma S. , Maheshwari S. , Shukla A.

Bio-Algorithms and Med-Systems

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2018

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tom Vol. 14, no. 2

art. no. 20180011

EN

Deep convolution neural networks (CNNs) have demonstrated their capabilities in modern-day medical image classification and analysis. The vital edge of deep CNN over other techniques is their ability to train without expert knowledge. Time bound detection is very beneficial for the early cure of disease. In this paper, a deep CNN architecture is proposed to classify nondiabetic retinopathy and diabetic retinopathy fundus eye images. Kaggle 2015 diabetic retinopathy competition dataset and messier experiment dataset are used in this study. The proposed deep CNN algorithm produces significant results with 93% area under the curve (AUC) for the Kaggle dataset and 91% AUC for the Messidor dataset. The sensitivity and specificity for the Kaggle dataset are 90.22% and 85.13%, respectively; the corresponding values of the Messidor dataset are 91.07% and 80.23%, respectively. The results outperformed many existing studies. The present architecture is a promising tool for diabetic retinopathy image classification.

4

The association between ALR2 -106C > T gene polymorphisms and diabetic retinopathy susceptibility in diabetes mellitus patient: a systematic review and meta-analysis

88%

Putri I. S. , Rezkita B. E. , Irving S. , Azmiardi A.

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tom 10

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nr 1

12-21

EN

Aldose reductase gene polymorphisms has been indicated to be associated with diabetic retinopathy (DR). The research data were from PubMed and EMBASE. We identified -106C > T single nucleotide polymorphism (SNP). Pool odds ratio (OR) with 95% CI were calculated. Nine studies were included. ALR2 106C > T gene polymorphisms was associated with the increased risk of DR in T1DM (C vs. T, OR = 2.07, p = 0.001; CC vs. CT + TT, OR = 2.56, p = 0.005). T allele and TT genotype were associated with decreased risk of DR in T1DM (OR = 0.48, p = 0.0001 and OR = 0.12, p = 0.0005). In conclusion, C allele and CC genotype may be a risk factor, while T allele and TT genotype may serve as protective factor for DR in T1DM patient.

5

Retinopathy severity correlates with RANTES concentrations and CCR5-positive microvesicles in diabetes

88%

Tokarz A. , Konkolewska M. , Kuśnierz-Cabala B. , Maziarz B. , Hanarz P. , Żurakowski A. , Szuścik I. , Stępień E. Ł. , Tokarz A. , Konkolewska M. , Kuśnierz-Cabala B. , Maziarz B. , Hanarz P. , Żurakowski A. , Szuścik I. , Stępień E. Ł.

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nr 3

6

Hyperreflective dots in optical coherence tomography

75%

Paczwa K. , Szabelska P. , Rerych M. , Różycki R. , Gołębiewska J.

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nr 3

175-179

EN

Optical coherence tomography is a non-invasive and repeatable imaging method of posterior segment of the eye used in medical practice. Hyperreflective dots visible in OCT scans have been reported in various retinal diseases such as age-related macular degeneration, diabetic macular edema, retinal vein occlusion and central serous chorioretinopathy. In the future, HRDs may become a useful biomarker in making treatment decision and monitoring ocular conditions among the patients with mentioned diseases.

7

Diagnozowanie stanu retinopatii cukrzycowej przy pomocy głębokich sieci neuronowych

75%

Jarzembiński B. , Kwasigroch A. , Grochowski M.

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

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2018

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tom Nr 60

37--40

PL

W referacie opisano problem wykrywania oraz klasyfikacji stanu retinopatii cukrzycowej ze zdjęć dna oka przy pomocy głębokich sieci neuronowych. Retinopatia cukrzycowa jest chorobą oczu często występującą u osób z cukrzycą. Nieleczona prowadzi do uszkodzenia wzroku, a nawet ślepoty. W pracy badawczej opracowano system wykrywania retinopatii cukrzycowej na podstawie zdjęć dna oka. Opracowana sieć neuronowa przypisuje stan choroby w 5 stopniowej skali – od braku choroby do najbardziej zaawansowanego stanu choroby. Zaproponowano specjalny system kodowania klas w celu uchwycenia wielkości różnicy pomiędzy rzeczywistymi a predykowanymi stanami choroby. Uzyskano wysokie wyniki klasyfikacji na zbiorze testowym. W celu oceny skuteczności działania systemu wykorzystano miary statystyczne takie jak ważona Kappa i dokładność.

EN

In the paper we described computer aided detection system of diabetic retinopathy based on fundus photos of retina. Diabetic retinopathy is an eye disease associated with diabetes. Non-treated diabetic retinopathy leads to sight degeneration and even blindness. Early detection is crucial due to provide effective treatment. Currently, diabetic retinopathy detection is time consuming process, done manualy by medical specialist. The disease is dangerous issue in places where the availability of phisicians is limited. We employed the computer system that detect diabetic retinopathy and assess a stage of the disease based on retinal photo of fundus. We used one of the best image classification system – deep neural networks. Employed system assess the stage of the disease in 5 level scale – from absence of disease to the most severe stage of disease. We employed transfer learning and data augmentation to enhance classification result. Moreover we proposed special class coding system to catch the difference between real and predicted stage of disease. We tested employed system using different statistical measures like accuracy, sensitivity, specificity and Kappa score.

8

Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques

75%

Janaki S. D. , Geetha K.

Polish Journal of Medical Physics and Engineering

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2019

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tom Vol. 25, Iss. 2

131--139

EN

Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.

9

Robust intensity variation and inverse surface adaptive thresholding techniques for detection of optic disc and exudates in retinal fundus images

63%

Karkuzhali S. , Manimegalai D.

Biocybernetics and Biomedical Engineering

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2019

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tom Vol. 39, no. 3

753--764

EN

Diabetic Retinopathy (DR) is an adverse change in retinal blood vessels leads to blindness for diabetic patients without any symptoms. Diabetes is characterized by imbalance level of glucose in the human body. The optic disc (OD) is the major retinal landmark. Localization of OD is an important step in fundus image analysis and to develop Computer Aided Diagnosis tool for DR. OD center detection is necessary to reduce false positive rate in the detection of exudates (EXs). EXs is the white lesion present in the retina which is the early symptom for the diagnosis of DR. OD is detected using intensity variation algorithm and EXs is segmented using inverse surface adaptive thresholding algorithm. This algorithm achieves better result in localizing OD and segmenting EXs when compared to literature-reviewed methods. The maximum intensity variance method is used to locate OD with average ACC of 96.54%, 98.65%, 98.12%, 99.23%, 99.81% and 98.47% in DIARETDB0, DIARETDB1, MESSIDOR, DRIVE, STARE and Bejan Singh Eye Hospital databases with less computation time of 102 ms, 108 ms, 120 ms, 93 ms, 110 ms and 131 ms. The inverse surface adaptive thresholding method has achieved an SE of 97.43%, 98.87%, 99.12%, 97.21%, 98.72%, and 96.63%, a SPE of 91.56%, 92.31%, 90.21%, 90.14%, 89.58%, 92.56% and an ACC of 99.34%, 99.67%, 98.34%, 98.87%, 99.13%, 98.34% for DIARETDB0, DIARETDB1, MESSIDOR, DRIVE, STARE and Bejan Singh Eye Hospital databases respectively.

10

Differences in anti-endothelial and anti-retinal antibody titers: implications for the pathophysiology of acute and chronic central serous chorioretinopathy

63%

Karska-Basta I. , Pociej-Marciak W. , Chrzaszcz M. , Wilanska J. , Jager M. J. , Markiewicz A. , Romanowska-Dixon B. , Sanak M. , Kubicka-Trzaska A.

Journal of Physiology and Pharmacology

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2020

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tom 71

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nr 2

11

Obrazowanie naczyń pozagałkowych w retinopatii cukrzycowej metodą ultrasonografi i dopplerowskiej w kolorze

63%

Pauk-Domańska M. , Walasik-Szemplińska D.

Journal of Ultrasonography

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2014

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tom 14

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nr 56

28-35

EN

Diabetes is a metabolic disease characterized by elevated blood glucose level due to impaired insulin secretion and activity. Chronic hyperglycemia leads to functional disorders of numerous organs and to their damage. Vascular lesions belong to the most common late complications of diabetes. Microangiopathic lesions can be found in the eyeball, kidneys and nervous system. Macroangiopathy is associated with coronary and peripheral vessels. Diabetic retinopathy is the most common microangiopathic complication characterized by closure of slight retinal blood vessels and their permeability. Despite intensive research, the pathomechanism that leads to the development and progression of diabetic retinopathy is not fully understood. The examinations used in assessing diabetic retinopathy usually involve imaging of the vessels in the eyeball and the retina. Therefore, the examinations include: fl uorescein angiography, optical coherence tomography of the retina, B-mode ultrasound imaging, perimetry and digital retinal photography. There are many papers that discuss the correlations between retrobulbar circulation alterations and progression of diabetic retinopathy based on Doppler sonography. Color Doppler imaging is a non-invasive method enabling measurements of blood fl ow velocities in small vessels of the eyeball. The most frequently assessed vessels include: the ophthalmic artery, which is the fi rst branch of the internal carotid artery, as well as the central retinal vein and artery, and the posterior ciliary arteries. The analysis of hemodynamic alterations in the retrobulbar vessels may deliver important information concerning circulation in diabetes and help to answer the question whether there is a relation between the progression of diabetic retinopathy and the changes observed in blood fl ow in the vessels of the eyeball. This paper presents the overview of literature regarding studies on blood fl ow in the vessels of the eyeball in patients with diabetic retinopathy.

PL

Cukrzyca jest chorobą metaboliczną, charakteryzującą się podwyższonym poziomem glukozy we krwi na skutek nieprawidłowego wydzielania lub działania insuliny. Przewlekła hiperglikemia prowadzi do zaburzeń w funkcjonowaniu wielu narządów oraz do ich uszkodzenia. Zmiany w naczyniach należą do najczęściej spotykanych późnych powikłań cukrzycy. Uszkodzenia typu mikroangiopatii występują w gałce ocznej, nerkach i układzie nerwowym. Makroangiopatia dotyczy naczyń wieńcowych oraz obwodowych. Retinopatia cukrzycowa jest najczęstszym powikłaniem z grupy mikroangiopatii, charakteryzującym się zamknięciem drobnych naczyń siatkówki wraz z ich przeciekiem. Mimo intensywnych badań patomechanizm prowadzący do rozwoju i progresji retinopatii cukrzycowej nie został do końca poznany. Techniki badania używane w ocenie retinopatii cukrzycowej zazwyczaj obrazują naczynia gałki ocznej oraz siatkówkę. W tym celu wykorzystywane są angiografi a fl uoresceinowa, optyczna koherentna tomografi a siatkówki, badanie ultrasonografi czne w prezentacji B, perymetria, cyfrowa fotografi a siatkówki. Istnieje wiele prac analizujących zależności pomiędzy zmianami w krążeniu pozagałkowym a progresją retinopatii cukrzycowej, oceniane za pomocą ultrasonografi i dopplerowskiej. Obrazowanie techniką kolorowego dopplera jest nieinwazyjną metodą, pozwalającą na pomiary prędkości przepływów w małych naczyniach oczodołu. Najczęściej oceniane są: tętnica oczna, będąca pierwszym odgałęzieniem tętnicy szyjnej wewnętrznej, tętnica i żyła środkowa siatkówki, tętnice rzęskowe tylne. Analiza zaburzeń hemodynamicznych w naczyniach pozagałkowych może dostarczyć wiedzy na temat krążenia w cukrzycy oraz pomóc odpowiedzieć na pytanie, czy istnieje związek między progresją retinopatii cukrzycowej a zmianami w przepływach w naczyniach oczodołowych. Niniejsza praca zawiera przegląd piśmiennictwa dotyczącego badań nad przepływami w naczyniach oczodołowych u pacjentów z retinopatią cukrzycową.

12

The way it was and the way it should be - the new approach to the treatment of nonproliferative diabetic retinopathy

63%

Misiuk-Hojło M. , Kręcicka J.

OphthaTherapy

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2021

|

tom 8

|

nr 4

233-237

PL

Cukrzyca została uznana przez Światową Organizację Zdrowia za epidemię XXI w. Retinopatia cukrzycowa należy do głównych komplikacji cukrzycy i wiodących przyczyn utraty wzroku wśród aktywnych zawodowo dorosłych. Jest to mikroangiopatia, w której dochodzi do uszkodzenia głównie drobnych naczyń, ponieważ są one najbardziej wrażliwe na hiperglikemię. W ramach działań prewencyjnych kluczowe są adekwatne monitorowanie metaboliczne cukrzycy, w tym wyrównane wartości hemoglobiny glikowanej, ciśnienia tętniczego oraz lipidogramu. Niezwykle ważne są również regularne kontrole okulistyczne i okresowe badania obrazowe siatkówki. Od lat naukowcy poszukują terapii umożliwiającej zahamowanie zmian naczyniowych w cukrzycy. W maju 2021 r. Polskie Towarzystwo Okulistyczne opublikowało stanowisko grupy eksperckiej w zakresie stosowania sulodeksydu jako leczenia wspomagającego w łagodnej oraz średnio zaawansowanej retinopatii cukrzycowej. Sulodeksyd chroni śródbłonek naczyń, wpływa na czynność jego komórek, ma właściwości profibrynolityczne, przeciwzapalne i wazoregulujące. Cechy te czynią go obiecującym czynnikiem protekcyjnym w pierwszych fazach retinopatii cukrzycowej.

EN

Diabetes has been declared an epidemic of the XXIst century by the World Health Organisation. Diabetic retinopathy, one of its main complications, is a leading cause of vision impairment among professionally active adults. Diabetic retinopathy is a microangiopathy that affects mainly small vessels, due to their highest vulnerability to hyperglycemia. Preventive measures involve mainly strict diabetic follow-ups, balanced level of glycated hemoglobin, blood pressure values and analysis of lipids. Regular ophthalmological check-ups and imaging tests of the retina are also extremely significant. For many years scientists have searched for a therapy to suppress vascular changes in diabetes. In May 2021 a stance on sulodexide use as a supporting treatment in mild and intermediate diabetic retinopathy was published by the Polish Society of Ophthalmology. Sulodexide protects vascular endothelium, contributes to endothelial cells’ function and has profibrinolytic, anti-inflammatory and vasoregulatory properties. These features make it a promising protective agent in the early stages of diabetic retinopathy.

13

Automatic detection of exudates from fundus eye images based on mathematical morphology for diabetic retinopathy monitoring

63%

Stąpor K. , Świtoński A.

Studia Informatica

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2005

|

tom Vol. 26, nr 1

5-14

EN

In this paper the method for automatic segmentation of exudates from fundus eye images is proposed. The method is composed of the following steps: 1) preprocessing, 2) finding marker image and 3) geodesic reconstraction. The mean sensitivity is 95%.

PL

W artykule przedstawiono metodę segmentacji wysięków na cyfrowych obrazach dna oka. Metoda składa się z następujących kroków: 1) przetwarzanie wstępne, 2) znajdowanie obrazu znaczników, 3) rekonstrukcja geodezyjna. Średnia czułość metody wynosi 95%.

14

Automatic detection of early symptoms of diabetic retinopathy from fundus eye images using mathematical morphology

63%

Stąpor K. , Świtoński A.

Studia Informatica

|

2004

|

tom Vol. 25, nr 3

29-39

EN

In this paper the new method for automatic segmentation of microaneurysms from fundus eye images is proposed. It relies on methods from mathematical grayscale morphology. The new HitAndMiss transformation has been defined forthe detection of markers of watershed transformation.

PL

W artykule przedstawiono nową metodę automatycznej segmentacji mikroaneuryzmatów na cyfrowych obrazach dna oka. Zaproponowana metoda F wykorzystuje narzędzia wieloodcieniowej morfologii matematycznej. Zdefiniowano |: nowe przekształcenie trafi-nie-trafi dla detekcji markerów transformaty wododziało-i wej.

15

New Binary Hausdorff Symmetry measure based seeded region growing for retinal vessel segmentation

63%

Panda R. , Puhan N. B. , Panda G.

|

tom Vol. 36, no. 1

119--129

EN

Automated retinal vessel segmentation plays an important role in computer-aided diagnosis of serious diseases such as glaucoma and diabetic retinopathy. This paper contributes, (1) new Binary Hausdorff Symmetry (BHS) measure based automatic seed selection, and (2) new edge distance seeded region growing (EDSRG) algorithm for retinal vessel segmentation. The proposed BHS measure directly provides a binary symmetry decision at each pixel without the computation of continuous symmetry map and image thresholding. In a multiscale mask, the BHS measure is computed using the distance sets of opposite direction angle bins with sub-pixel resolution. The computation of the BHS measure from the Hausdorff distance sets involves point set matching based geometrical interpretation of symmetry. Then, we design a new edge distance seeded region growing (EDSRG) algorithm with the acquired seeds. The performance evaluation in terms of sensitivity, specificity and accuracy is done on the publicly available DRIVE, STARE and HRF databases. The proposed method is found to achieve state-of-the-art vessel segmentation accuracy in three retinal databases; DRIVE- sensitivity (0.7337), specificity (0.9752), accuracy (0.9539); STARE-sensitivity (0.8403), specificity (0.9547), accuracy (0.9424); and HRF-sensitivity (0.8159), specificity (0.9525), accuracy (0.9420).

16

Robust and accurate optic disk localization using vessel symmetry line measure in fundus images

63%

Panda R. , Puhan N. B. , Panda G.

Biocybernetics and Biomedical Engineering

|

2017

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tom Vol. 37, no. 3

466--476

EN

Accurate optic disk (OD) localization is an important step in fundus image based computer-aided diagnosis of glaucoma and diabetic retinopathy. Robust OD localization becomes more challenging with the presence of common pathological variations which could alter its overall appearance. This paper presents a novel OD localization method by incorporating salient visual cues of retinal vasculature: (1) global vessel symmetry, (2) vessel component count and (3) local vessel symmetry inside OD region. In the proposed method, a new vessel symmetry line (VSL) measure is designed to demarcate the lines that divide the retinal vasculature into approximately similar halves. The initial OD center location is computed using the highest number of major blood vessel components in the skeleton image. The final OD center localization involves an iterative center of mass computation to exploit the local vessel symmetry in the OD region of interest. The proposed method shows effectiveness in diseased retinas having diverse symptoms like bright lesions, hemorrhages, and tortuous vessels that create potential ambiguity for OD localization. A total of ten publicly available retinal image databases are considered for extensive evaluation of the proposed method. The experimental results demonstrate high average OD detection accuracy of 99.49%, while achieving state-of-the-art OD localization error in all databases.

17

Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifier

63%

Narhari B. B. , Murlidhar B. K. , Sayyad A. D. , Sable G. S.

Bio-Algorithms and Med-Systems

|

2021

|

tom Vol. 17, no. 1

9--23

EN

Objectives: The focus of this paper is to introduce an automated early Diabetic Retinopathy (DR) detection scheme from colour fundus images through enhanced segmentation and classification strategies by analyzing blood vessels. Methods: The occurrence of DR is increasing from the past years, impacting the eyes due to a sudden rise in the glucose level of blood. All over the world, half of the people who are under age 70 are severely suffered from diabetes. The patients who are affected by DR will lose their vision during the absence of early recognition of DR and appropriate treatment. To decrease the growth and occurrence of loss of vision, the early detection and timely treatment of DR are desirable. At present, deep learning models have presented better performance using retinal images for DR detection. In this work, the input retinal fundus images are initially subjected to pre-processing that undergoes contrast enhancement by Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Further, the optimized binary thresholding-based segmentation is done for blood vessel segmentation. For the segmented image, Tri-level Discrete Level Decomposition (Tri-DWT) is performed to decompose it. In the feature extraction phase, Local Binary Pattern (LBP), and Gray-Level Co-occurrence Matrices (GLCMs) are extracted. Next, the classification of images is done through the combination of two algorithms, one is Neural Network (NN), and the other Convolutional Neural Network (CNN). The extracted features are subjected to NN, and the triDWT-based segmented image is subjected to CNN. Both the segmentation and classification phases are enhanced by the improved meta-heuristic algorithm called Fitness Ratebased Crow Search Algorithm (FR-CSA), in which few parameters are optimized for attaining maximum detection accuracy. Results: The proposed DR detection model was implemented in MATLAB 2018a, and the analysis was done using three datasets, HRF, Messidor, and DIARETDB. Conclusions: The developed FR-CSA algorithm has the best detection accuracy in diagnosing DR.

18

A generalized method for the segmentation of exudates from pathological retinal fundus images

63%

Kaur J. , Mittal D.

|

2018

|

tom Vol. 38, no. 1

27--53

EN

Diabetic retinopathy, an asymptomatic complication of diabetes, is one of the leading causes of blindness in the world. The exudates, abnormal leaked fatty deposits on retina, are one of the most prevalent and earliest clinical signs of diabetic retinopathy. In this paper, a generalized exudates segmentation method to assist ophthalmologists for timely treatment and effective planning in the diagnosis of diabetic retinopathy is developed. The main contribution of the proposed method is the reliable segmentation of exudates using dynamic decision thresholding irrespective of associated heterogeneity, bright and faint edges. The method is robust in the sense that it selects the threshold value dynamically irrespective of the large variations in retinal fundus images from varying databases. Since no performance comparison of state of the art methods is available on common database, therefore, to make a fair comparison of the proposed method, this work has been performed on a diversified database having 1307 retinal fundus images of varying characteristics namely: location, shapes, color and sizes. The database comprises of 649 clinically acquired retinal fundus images from eye hospital and 658 retinal images from publicly available databases such as STARE, MESSIDOR, DIARETDB1 and e-Optha EX. The segmentation results are validated by performing two sets of experiments namely: lesion based evaluation criteria and image based evaluation criteria. Experimental results at lesion level show that the proposed method outperforms other existing methods with a mean sensitivity/specificity/accuracy of 88.85/96.15/93.46 on a composite database of retinal fundus images. The segmentation results for image-based evaluation with a mean sensitivity/specificity/accuracy of 94.62/ 98.64/96.74 respectively prove the clinical effectiveness of the method. Furthermore, the significant collective performance of these experiments on clinically as well as publicly available standard databases proves the generalization ability and the strong candidature of the proposed method in the real-time diagnosis of diabetic retinopathy.

19

Retinal vasculature segmentation and measurement framework for color fundus and SLO images

63%

Pachade S. , Porwal P. , Kokare M. , Giancardo L. , Meriaudeau F.

Biocybernetics and Biomedical Engineering

|

2020

|

tom Vol. 40, no. 3

865--900

EN

The change in vascular geometry is an indicator of various health issues linked with vision and cardiovascular risk factors. Early detection and diagnosis of these changes can help patients to select an appropriate treatment option when the disease is in its primary phase. Automatic segmentation and quantification of these vessels would decrease the cost and eliminate inconsistency related to manual grading. However, automatic detection of the vessels is challenging in the presence of retinal pathologies and non-uniform illumination, two common occurrences in clinical settings. This paper presents a novel framework to address the issue of retinal blood vessel detection and width measurement under these challenging circumstances and also on two different imaging modalities: color fundus imaging and Scanning Laser Ophthalmoscopy (SLO). In this framework, initially, vessel enhancement is done using linear recursive filtering. Then, a unique combination of morphological operations, background estimation, and iterative thresholding are applied to segment the blood vessels. Further, vessel diameter is estimated in two steps: firstly, vessel centerlines are extracted using the graph-based algorithm. Then, vessel edges are localized from the image profiles, by utilizing spline fitting to obtain vascular orientations and then finding the zero-crossings. Extensive experiments have been carried out on several publicly accessible datasets for vessel segmentation and diameter measurement, i.e., DRIVE, STARE, IOSTAR, RC-SLO and REVIEW dataset. Results demonstrate the competitive and comparable performance than earlier methods. The encouraging quantitative and visual performance of the proposed framework makes it an important component of a decision support system for retinal images.

20

Application of telemedicine technique in screening for diabetic retinopathy

51%

Ładyżyński P. , Wójcicki J. M. , Chihara K.

Biocybernetics and Biomedical Engineering

|

2007

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tom Vol. 27, no. 1-2

253-263

EN

Diabetic retinopathy is one of the most serious long-term microvascular complications of diabetes. It is a sight-threatening, chronic ocular disorder, which is currently the most common cause of blindness in people of working age in western societies. Slit lamp biomicroscopy and seven-field stereoscopic 30o fundus photography (with dilated pupil) represent the current gold standard techniques for detecting diabetic retinopathy. These methods are too expensive for widespread use in the screening process, even in the most developed countries. At present, retinopathy is usually detected by the direct ophthalmoscopic examination. Simple wide angle color fundus photography is another much more accurate and widely used method. The introduction of digital imaging cameras made the application of the telemedicine technique possible. First feasibility studies concerning application of the telemedicine technique in screening for diabetic retinopathy were reported in the late 1990s. These early systems used mobile fundus cameras or e-mail messages for transmission of digital fundus photographs between distant locations in a store–and–forward mode. Then, more advanced systems followed, which either based on proprietary software solutions or on the latest advances of the internet technology. One of such systems has been developed in co-operation between NAIST and IBBE PAS. The developed DRWeb system consists of the image acquisition station, the database and communication server and the image grading station. Two modes of operation are possible: telediagnosis – when all the mentioned above stages are conducted independently, one after another and teleconsultation – when real time interaction between the physician and the expert is possible. The system is supplemented by an algorithm ensuring that the quality of the transmitted digital images is good enough for a successful making of the diagnosis.