Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine

Mansour, Romany F.; Abdelrahim, Elsaid Md.; Al-Johani, Amna S.

doi:10.4236/jilsa.2013.53015

Cited by 11 publications

(16 citation statements)

References 19 publications

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“…PCA is used because it converts the set of features into a reduced number of uncorrelated features. PCA ensures that principal components not only correspond to maximum variance, but also ensures that resulting set of features in the subspace are uncorrelated while retaining most of the information content [47], [48]. This guaranteed uncorrelation improves the predictive performance of resulting features.…”

Section: E Template Generationmentioning

confidence: 99%

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

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Section: E Template Generationmentioning

confidence: 99%

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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“…Robust exudates detection and segmentation from color retinal images for mass screening of diabetic retinopathy is proposed in [9]. Identification of diabetic retinal exudates in digital colour images using support vector machine is proposed in [10]. ROIbased segmentation and morphological reconstruction for exudate detection are presented in [11].…”

Section: Related Workmentioning

confidence: 99%

“…The role of the classifier is to implement a decision rule that will indicate to which class a given pattern belongs. Some efforts have already been made to automatically predict the risk level [3][4][5][6][7][8]10,[12][13][14][15][16][17]. Risk level classification algorithms are in continuous development and improvement.…”

Section: Risk Level Classificationmentioning

confidence: 99%

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

Janaki

Geetha²

2019

Polish Journal of Medical Physics and Engineering

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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.

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“…Premature symptoms of such sicknesses are pointed out by features of retinal blood vessel like length, diameter, angle, and tortuosity (Martinez-Perez et al, 2002). Retinal blood vessel is the only part of the blood circulation that could be directly observed and studied in detail (Mansour et al, 2013 normality and to identify or supervise retinal irregularities and defects like modifications in thickness and tortuosity of the retinal blood vessel are pointers for increased risk levels of diabetic retinopathy (Owen et al, 2011). Thus retinal irregularities and defects are mainly prominent in diabetic retinopathy (DR) which is a vascular impediment of diabetes mellitus and is a major reason of vision loss among diabetic patients.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Filter techniques for segmenting retinal blood vessels

Al-Marghilnai

Mansour

2017

Int. j. adv. appl. sci.

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Retinal fundus image is generally used to examine the diabetic retinopathy symptoms, by analysing blood vessel segmentation and also access the pathologies of the eye. Retinal blood vessel details can be mined from retinal fundus images through image processing. Processing involves three stages, Pre-processing, Segmentation, and Post-processing. Among the different segmentation algorithms existing, the wavelet filter method has been shown to be highly advantageous in distinguishing blood vessels effectively. Under this method, the objects in noisy background can be segmented and hence sort out the image from the background in a finer way. Retinal images obtained from retinal databases like DRIVE and STARE can be analysed using the wavelet filter algorithm. Herein wavelet filter method will be evaluated for efficient segmentation of the retinal blood vessels using retinal images obtained from databases.

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Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine

Cited by 11 publications

References 19 publications

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

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

Wavelet Filter techniques for segmenting retinal blood vessels

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