Automated diagnosis of Age-related macular degeneration from color retinal fundus images

Priya, R.; Aruna, P.

doi:10.1109/icectech.2011.5941690

Cited by 25 publications

(17 citation statements)

References 2 publications

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“…WMD, also known as choroidal neovascularization, is characterised by abnormal growth of the choroidal vascular structure into or around the macula. These new vessels are weak and easily bleed [41]. Achieving good performance in segmenting thin vessels suggests it is better to improve the overall accuracy by pursuing higher sensitivity while maintaining the same or sacrificing only a small fraction of specificity.…”

Section: Experimental Results and Evaluationmentioning

confidence: 99%

Retinal vessel segmentation using multi-scale textons derived from keypoints

Zhang

Fisher

Wang

2015

Computerized Medical Imaging and Graphics

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Highlights We present a retinal vessel segmentation approach that uses textons. Vessel textons are derived from responses of a multi-scale Gabor filter bank. Page 2 of 33A c c e p t e d M a n u s c r i p tRetinal vessel segmentation using multi-scale textons derived from keypoints 2  We train on keypoint descriptors instead of labelled ground truth. We show our unsupervised approach performs well compared to previous work. Our method outperforms other unsupervised approaches on the Drive data set. AbstractThis paper presents a retinal vessel segmentation algorithm which uses a texton dictionary to classify vessel/non-vessel pixels. However, in contrast to previous work where filter parameters are learnt from manually labelled image pixels our filter parameters are derived from a smaller set of image features that we call keypoints. A Gabor filter bank, parameterised empirically by ROC analysis, is used to extract keypoints representing significant scale specific vessel features using an approach inspired by the SIFT algorithm. We first determine keypoints using a validation set and then derive seeds from these points to initialise a k-means clustering algorithm which builds a texton dictionary from another training set. During testing we use a simple 1-NN classifier to identify vessel/non-vessel pixels and evaluate our system using the DRIVE database. We achieve average values of sensitivity, specificity and accuracy of 78.12%, 96.68% and 95.05% respectively. We find that clusters of filter responses from keypoints are more robust than those derived from hand-labelled pixels. This, in turn yields textons more representative of vessel/non-vessel classes and mitigates problems arising due to intra and inter-observer variability.

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Section: Experimental Results and Evaluationmentioning

confidence: 99%

Retinal vessel segmentation using multi-scale textons derived from keypoints

Zhang

Fisher

Wang

2015

Computerized Medical Imaging and Graphics

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“…Drusen area were analyzed and drusen are classified as large drusen, medium and small drusen in [29,34]. A number of methods have been proposed in [35][36][37][38][39][40] which are focused on drusen pixel level classification to segment regions in the image as drusen and non drusen, and analyze the accuracy as sensitivity and specificity of the method with hand labelled ground truth images.Our study only found two publications -one for automatic detection of dry and wet AMD [41] and one for soft and hard drusen classification [29] which are very important information for automated measurement of AMD severity. In addition, these studies were not validated against large dataset.…”

Section: Drusen Detection Techniquesmentioning

confidence: 99%

“…Priya and Aruna [41] proposed a method for dry and wet AMD detection using Discrete Wavelet Transform (DWT) and Probabilistic Neural Network (PNN). DWT is used to obtain the coefficients in horizontal, vertical and diagonal directions.…”

Section: Texture Based Segmentationmentioning

confidence: 99%

A Review of Disease Grading and Remote Diagnosis for Sight Threatening Eye Condition: Age Related Macular Degeneration

Bhuiyan

Xiao²,

Kanagasingam³

2014

J Comput Sci Syst Biol

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Age-related macular degeneration (AMD), a retinal disorder is the leading cause of vision loss in elderly people in developed countries. Currently, colour fundus imaging is used as a gold standard to assess individual for initial screening of AMD. In this paper, we examine the existing techniques for AMD screening using colour fundus imaging. The aim of the paper is to analyse and review the automated and semi-automated methods for detecting AMD pathologies such as drusen and geographic atrophy (GA) using colour fundus imaging. We intend to provide a brief description, highlighting the key points of the performance measurement and a framework for the existing research. We emphasize that to date; existing drug treatment can stop the progression of AMD and there is no drug or treatment to cure AMD. Therefore, the only feasible option is to prevent the incidence of AMD and avoid this fatal eye disease as well as unnecessary vision loss. For this, a mass screening system that enables remote diagnosis of AMD should be implemented for taking preventative measures in the early stage of AMD. Considering this, we discuss the open problems for automated AMD identification systems using colour fundus imaging and provided future directions for remote diagnosis and screening systems using colour fundus imaging and telemedicine platform.

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“…The algorithm achieved 95.6% accuracy but the technique was limited to the drusens located at macular region. In [9] probabilistic neural network was used to classify the disease as wet or dry. [10] Proposed a method to detect drusen by applying Gabor filter on an image.…”

Section: Introductionmentioning

confidence: 99%

Drusen exudate lesion discrimination in colour fundus images

Waseem

Akram

Ahmed

2014

2014 14th International Conference on Hybrid Intelligent Systems

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Automatic screening and diagnosis of ocular disease through fundus images are in place and considered worldwide. One of the leading sight loosing disease known as age related macular degeneration (AMD) has many proposed automatic screening systems. These systems detect yellow bright lesion and through the number of lesion and their size the disease is graded as advance and earlier stage. It becomes difficult for these systems to differentiate drusens from exudates another bright lesion associated with Diabetic retinopathy. These two lesions look similar on retinal surface. Differentiating these two lesions can improve the performance of any automatic system. In this paper we proposed a novel approach to discriminate these lesions. The approach consists of two stage procedure. The first stage after pre-processing detects all bright pixels from the image. The suspicious pixels are removed from the detected region. On the second stage bright regions are classified as drusen and exudates through Support Vector Machine (SVM). Proposed method was evaluated on publically available dataset STARE. The system achieve 92% accuracy.

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Automated diagnosis of Age-related macular degeneration from color retinal fundus images

Cited by 25 publications

References 2 publications

Retinal vessel segmentation using multi-scale textons derived from keypoints

Retinal vessel segmentation using multi-scale textons derived from keypoints

A Review of Disease Grading and Remote Diagnosis for Sight Threatening Eye Condition: Age Related Macular Degeneration

Drusen exudate lesion discrimination in colour fundus images

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