scholarly journals An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
George Michael Saleh ◽  
James Wawrzynski ◽  
Silvestro Caputo ◽  
Tunde Peto ◽  
Lutfiah Ismail Al Turk ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Detection System ◽  
Detection Algorithm ◽  
Rapid Identification ◽  
Automated Detection ◽  
Automated System ◽  
Racial Groups ◽  
Fundus Images ◽  
Screening Service ◽  
Wide Range

Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs) is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system’s performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races.

Mathematical morphology for microaneurysm detection in fundus images

2019 ◽  
Vol 30 (5) ◽  
pp. 1135-1142 ◽  
Author(s):  
Shilpa Joshi ◽  
PT Karule
Keyword(s):  
Diabetic Retinopathy ◽  
Detection System ◽  
Automated Detection ◽  
Morphological Processing ◽  
Candidate Selection ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Time Saving ◽  
Increased Risk ◽  
Sight Loss

Aim: Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss. Methods: The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set. Results: The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database. Conclusion: In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.

An Automated Detection System for Most L-α-Amino Acids

1969 ◽  
Vol 15 (2) ◽  
pp. 154-161 ◽  
Author(s):  
K Van Dyke ◽  
C Szustkiewicz
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Detection System ◽  
Automated System ◽  
Acid Oxidase ◽  
Enzymatic Reactions ◽  
Amino Acid Oxidase ◽  
Automated Method ◽  
Wide Range

Abstract An automated system for the determination of the L-α form of the majority of amino acids is presented. The method is based upon oxidative deamination of the amino acid coupled with oxidation of o-dianisidine by hydrogen peroxide. This procedure can be used comparatively for the determination of a mixture of L-α-amino acids or for the majority of separated L-α-amino acids (especially in conjunction with column separations from urine and blood which give falsely positive identification with ninhydrin detection). The stereospecific nature of the L-α-amino acid oxidase enables the investigator to quantitate the amount of L-α-amino acid in the presence of the D-α form. From an academic viewpoint, the extreme sensitivity and wide range of the detection system make it advantageous for the study of the enzyme itself. This automated method also may be employed to follow enzymatic reactions—e.g., those catalyzed by peptidases or racemases. The methodology is extremely convenient with good reagent stability and is much more sensitive than manometric technics.

Automated detection of diabetic retinopathy in blurred digital fundus images

2012 ◽  
Author(s):  
Eman M. Shahin ◽  
Taha E. Taha ◽  
W. Al-Nuaimy ◽  
S. El Rabaie ◽  
Osama F. Zahran ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Automated Detection ◽  
Fundus Images

CURVELET FUSION ENHACEMENT BASED EVALUATION OF DIABETIC RETINOPATHY BY THE IDENTIFICATION OF EXUDATES IN OPTIC COLOR FUNDUS IMAGES

2016 ◽  
Vol 28 (06) ◽  
pp. 1650046
Author(s):  
V. Ratna Bhargavi ◽  
Ranjan K. Senapati
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Detection System ◽  
Clinical Signs ◽  
Posterior Part ◽  
Curvelet Transform ◽  
Optic Disk ◽  
Support Vector ◽  
Fundus Images ◽  
Statistical Measures

Rapid growth of Diabetes mellitus in people causes damage to posterior part of eye vessel structures. Diabetic retinopathy (DR) is an important hurdle in diabetic people and it causes lesion formation in retina due to retinal vessel structures damage. Bright lesions (BLs) or exudates are initial clinical signs of DR. Early BLs detection can help avoiding vision loss. The severity can be recognized based on number of BLs formed in the color fundus image. Manually diagnosing a large amount of images is time consuming. So a computerized DR grading and BLs detection system is proposed. In this paper for BLs detection, curvelet fusion enhancement is done initially because bright objects maps to largest coefficients in an image by utilizing the curvelet transform, so that BLs can be recognized in the retina easily. Then optic disk (OD) appearance is similar to BLs and vessel structures are barriers for lesion exact detection and moreover OD falsely classified as BLs and that increases false positives in classification. So these structures are segmented and eliminated by thresholding techniques. Various features were obtained from detected BLs. Publicly available databases are used for DR severity testing. 260 fundus images were used for the performance evaluation of proposed work. The support vector machine classifier (SVM) used to separate fundus images in various levels of DR based on feature set extracted. The proposed system that obtained the statistical measures were sensitivity 100%, specificity 95.4% and accuracy 97.74%. Compared to existing state-of-art techniques, the proposed work obtained better results in terms of sensitivity, specificity and accuracy.

scholarly journals A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy

2013 ◽  
Vol 46 (10) ◽  
pp. 2740-2753 ◽  
Author(s):  
Meysam Tavakoli ◽  
Reza Pourreza Shahri ◽  
Hamidreza Pourreza ◽  
Alireza Mehdizadeh ◽  
Touka Banaee ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Fluorescein Angiography ◽  
Automated Detection ◽  
Fundus Images ◽  
Complementary Method

scholarly journals Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network

2018 ◽  
Vol 11 (3) ◽  
pp. 1083
Author(s):  
Jeyapriya J ◽  
K S Umadevi ◽  
R Jagadeesh Kannan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Automated Detection ◽  
Convolution Neural Network ◽  
Retinal Images ◽  
Fundus Images ◽  
Catching Up

The diagnosing features for Diabetic Retinopathy (DR) comprises of features occurring in and around the regions of blood vessel zone which will result into exudes, hemorrhages, microaneurysms and generation of textures on the albumen region of eye balls. In this study we presenta probabilistic convolution neural network based algorithms, utilized for the extraction of such features from the retinal images of patient’s eyeballs. The classifications proficiency of various DR systems is tabulated and examined. The majority of the reported systems are profoundly advanced regarding the analyzed fundus images is catching up to the human ophthalmologist’s characterization capacities.

Decision Support System for Diagnosing Diabetic Retinopathy from Color Fundus Images

2020 ◽  
Vol 64 (2) ◽  
pp. 20502-1-20502-10
Author(s):  
Duygu Çelik Ertuğrul ◽  
Yıltan Bitirim ◽  
Basmah Yakoub Anber
Keyword(s):  
Diabetic Retinopathy ◽  
State Of The Art ◽  
Medical Condition ◽  
Measurement Techniques ◽  
Automated System ◽  
Eye Disease ◽  
Data Sets ◽  
Fundus Images ◽  
Diabetic Eye Disease ◽  
Color Fundus Images

Abstract Diabetic Retinopathy (DR) is a medical condition, also known as diabetic eye disease, which is vision-threatening damage to the retina of the eye caused by diabetes. As the technology advances, researchers are becoming more interested in intelligent medical diagnosis systems to assist screening of DR in earlier stages. In this study, variety of state-of-the-art procedures are used to extract the anatomic segments and lesions from the color fundus images. In addition, an automated system is proposed for the detection of anatomic segments and lesions by grading approach to help clinical diagnosis of the DR analysis. Four publicly available databases of color fundus images and various appropriate measurement techniques are used to compare quantitatively the performance of the proposed system. The experiments conducted on DIARETDB0, DIARETDB1, STARE, and HRF data sets have proved that accuracy, sensitivity, and specificity of the proposed system are comparable or superior to state-of-the-art methods.

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