Advances in Image Processing Techniques for Drusens Detection and Quantification in Fundus Images

Mora, André; Vieira, Pedro; Fonseca, José

doi:10.1007/978-3-642-11628-5_32

Cited by 2 publications

(5 citation statements)

References 13 publications

(14 reference statements)

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“…As shown in Table 1, our results compare favourably with the results obtained by other algorithms found in the literature to detect exudates [1, 2], drusens [3, 4] and MA [5, 6], especially for MA pathology. The main advantages of our approach are that it is simple, fast and effective.…”

Section: Data and Simulation Resultssupporting

confidence: 84%

“…We have presented a new automated system for pathologies detection in retina digital images based on the analysis of the image by means of the CCWT to obtain phase angle information which is fed to SVM. The simulation results show that our method gives results that compare favourably with the results reported in [1–6]. This could be explained mainly by the fact that phase angles contain valuable information that allows discriminating between normal and abnormal retina images.…”

Section: Resultssupporting

confidence: 74%

“…Automatic digital image processing systems are becoming increasingly important in health care, since they allow efficient processing of images and objective medical diagnostics. In recent years, a particular interest has been devoted to the design of an automated computer‐based system for the screening of retina digital images [1] to detect the presence of exudates [2, 3], drusen [4, 5] and microaneurysm (MA) [6, 7]. In the problem of exudates detection [2]; contrast enhancement fuzzy C‐means and support vector machines (SVMs) were used to detect and classify bright lesions.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed system achieved a sensitivity of 83%. A gradient‐based segmentation algorithm based on labelling the maximum gradient path was adopted in [5] to isolate drusens. Then, they were fitted by Gaussian functions to compute the area affected.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles

Lahmiri

Gargour

Gabrea

2014

Healthcare Technology Letters

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An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.

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Section: Data and Simulation Resultssupporting

confidence: 84%

Section: Resultssupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles

Lahmiri

Gargour

Gabrea

2014

Healthcare Technology Letters

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“…Drusen affected area detection using intensity gradient based segmentation. Gaussian function model has been suggested by Mora A et al [6]. Wavelet transforms in combination with Fuzzy C Means clustering has been reported by Rama Prasath A et al [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images

Sheela¹,

Basavaraj²

2018

IJET

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Human eye can be affected by different types of diseases. Age-Related Macular Degeneration (AMD) is one of the such diseases, and it mainly occurs after 50 years of age. This disease is characterized by the occurrence of yellow spots called as Drusen. In this work, an automated method for the detection of drusen in Fundus image has been developed, and it has been tested on 70 images consisting of 30 normal images and 40 images with drusen. Performance of the Support Vector Machine (SVM) and K Nearest Neighbor (KNN) classifier has been evaluated using Data's reduction using Principle Component Analysis (PCA) and Data's selection using Genetic Algorithm (GA).Performance evaluation has been done in terms of accuracy, sensitivity, specificity, misclassification rate, positive predictive rate, negative predictive rate and Youden’s Index. The proposed method has achieved highest accuracy of 98.7% when data selection using Genetic Algorithm has been applied.

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Advances in Image Processing Techniques for Drusens Detection and Quantification in Fundus Images

Cited by 2 publications

References 13 publications

Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles

Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles

Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images

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