Automated Identification of Diabetic Retinal Exudates in Digital Color Images

Osareh, Alireza

doi:10.1201/9781420009781.ch14

Cited by 39 publications

(87 citation statements)

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“…The boundary of the OD is approximated as a circle. This approach is considered accurate enough to remove the OD as a candidate EX region [20]. …”

Section: Segmentationmentioning

confidence: 99%

“…The performance was assessed in terms of an image-based criterion and a lesion-based criterion (pixel resolution) [20]. The image-based criterion accounted for the ability of the algorithm to separate pathological images from normal ones on the basis of the presence or absence of EXs.…”

Section: Comparison Of Nn Classifiersmentioning

confidence: 99%

“…As a consequence, even if EXs detection is inaccurate, specificity values would be high. The positive predictive value (PPV l ), which measures the probability that a detected region is really an EX, was regarded as a more significant criterion to evaluate the performance of the system [20]. We also used the property if EXs of usually appearing in groups in retinal images to improve the image-based specificity (SP i ) of the system without compromising its image-based sensitivity (SE i ).…”

Section: Comparison Of Nn Classifiersmentioning

confidence: 99%

“…Linear discriminant analysis was subsequently used to differentiate among lesion types. Neural network (NN) based classifiers have also been used [18][19][20]. A multilayer perceptron (MLP) was used in [18] to determine the presence of bright lesions in image regions of size 20x20 pixels.…”

Section: Introductionmentioning

confidence: 99%

“…A support vector machine (SVM) was used afterwards to determine if a segmented region is an EX or another type of bright region. A similar approach was used in [20], where MLP and SVM NNs were analysed and compared.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Neural network based detection of hard exudates in retinal images

García

Sánchez

López

et al. 2009

Computer Methods and Programs in Biomedicine

151

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“…The boundary of the OD is approximated as a circle. This approach is considered accurate enough to remove the OD as a candidate EX region [20]. …”

Section: Segmentationmentioning

confidence: 99%

Section: Comparison Of Nn Classifiersmentioning

confidence: 99%

Section: Comparison Of Nn Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Neural network based detection of hard exudates in retinal images

García

Sánchez

López

et al. 2009

Computer Methods and Programs in Biomedicine

151

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Detection of Hard Exudates in Retinal Images Using a Radial Basis Function Classifier

et al. 2009

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Diabetic retinopathy (DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates (EXs), in retinal images can contribute to the diagnosis and screening of the disease. The aim of this study was to automatically detect these lesions in fundus images. To achieve this goal, each image was normalized and the candidate EX regions were segmented by a combination of global and adaptive thresholding. Then, a group of features was extracted from image regions and the subset which best discriminated between EXs and retinal background was selected by means of logistic regression (LR). This optimal subset was subsequently used as input to a radial basis function (RBF) neural network. To improve the performance of the proposed algorithm, some noisy regions were eliminated by an innovative postprocessing of the image. The main novelty of the paper is the use of LR in conjunction with RBF and the proposed postprocessing technique. Our database was composed of 117 images with variable color, brightness and quality. The database was divided into a training set of 50 images (from DR patients) and a test set of 67 images (40 from DR patients and 27 from healthy retinas). Using a lesion-based criterion (pixel resolution), a mean sensitivity of 92.1% and a mean positive predictive value of 86.4% were obtained. With an image-based criterion, a mean sensitivity of 100%, mean specificity of 70.4% and mean accuracy of 88.1% were achieved. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.

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Automated Identification of Exudates and Optic Disc Based on Inverse Surface Thresholding

2011

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This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms a method based on watershed segmentation.

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Automated Identification of Diabetic Retinal Exudates in Digital Color Images

Cited by 39 publications

References 0 publications

Neural network based detection of hard exudates in retinal images

Neural network based detection of hard exudates in retinal images

Detection of Hard Exudates in Retinal Images Using a Radial Basis Function Classifier

Automated Identification of Exudates and Optic Disc Based on Inverse Surface Thresholding

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