A Bag of Words approach for discriminating between retinal images containing exudates or drusen

Grinsven, Mark J. J. P. van; Chakravarty, Arunava; Sivaswamy, Jayanthi; Theelen, Thomas; Ginneken, Bram van; Sánchez, Clara I.

doi:10.1109/isbi.2013.6556806

Cited by 17 publications

(13 citation statements)

References 11 publications

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“…In particular, we compare different low-level features and compare our proposed method using sparse coding with BoVW approaches used in [26,24].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The BoVW approach is used by Grinsven et al [24] and by Sadek et al [26] to create a dictionary and represent an image by a histogram of visual words occurrences. Table 6 shows the results of the comparison for the normal class.…”

Section: Comparison With Bag-of-visual-words Methodsmentioning

confidence: 99%

“…The system achieves a sensitivity of 95% and a specificity of 88% using a validation set of 300 images. Grinsven et al [24] proposed an algorithm to automatically retrieve and classify images with bright lesions, namely drusen and exudates, using the BoVW approach. The algorithm initially partitions the image into a fixed number of square patches and extracts a set of features from each patch.…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, the BoVW approach [24] needs a prior knowledge about the location of the optic disk and the macula, while the method described in [25] requires manual annotation of unhealthy regions. The correct classification rate of the method of Ujjwal et al [27] depends on the output of the visual saliency detector, and the method in [26] requires the removal of blood vessels.…”

Section: Related Workmentioning

confidence: 99%

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Discrimination of retinal images containing bright lesions using sparse coded features and SVM

Sidibé

Sadek

Mériaudeau

2015

Computers in Biology and Medicine

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Diabetic Retinopathy (DR) is a chronic progressive disease of the retinal microvasculature which is among the major causes of vision loss in the world. The diagnosis of DR is based on the detection of retinal lesions such as microaneurysms, exudates and drusen in retinal images acquired by a fundus camera. However, bright lesions such as exudates and drusen share similar appearances while being signs of different diseases. Therefore, discriminating between different types of lesions is of interest for improving screening performances. In this paper, we propose to use sparse coding techniques for retinal images classification. In particular, we are interested in discriminating between retinal images containing either exudates or drusen, and normal images free of lesions. Extensive experiments show that dictionary learning techniques can capture strong structures of retinal images and produce discriminant descriptors for classification. In particular, using a linear SVM with the obtained sparse coded features, the proposed method achieves superior performance as compared with the popular Bag-of-Visual-Word approach for image classification. Experiments with a dataset of 828 retinal images collected from various sources show that the proposed approach provides excellent discrimination results for normal, drusen and exudates images. It achieves a sensitivity and a specificity of 96.50% and 97.70% for the normal class; 99.10% and 100% for the drusen class; and 97.40% and 98.20% for the exudates class with a medium size dictionary of 100 atoms.

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“…In particular, we compare different low-level features and compare our proposed method using sparse coding with BoVW approaches used in [26,24].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Comparison With Bag-of-visual-words Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Discrimination of retinal images containing bright lesions using sparse coded features and SVM

Sidibé

Sadek

Mériaudeau

2015

Computers in Biology and Medicine

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“…Van Grinsven et.al. [18] proposed a bag of visual words approach to characterize the fundus image. They implemented decomposition of image as patches.…”

Section: Introductionmentioning

confidence: 99%

Bright Lesion Detection in Color Fundus Images Based on Texture Features

Bhargavi¹,

Senapati²

2016

Bulletin EEI

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In this paper a computer aided screening system for the detection of bright lesions or exudates using color fundus images is proposed. The proposed screening system is used to identify the suspicious regions for bright lesions. A texture feature extraction method is also demonstrated to describe the characteristics of region of interest. In final stage the normal and abnormal images are classified using Support vector machine classifier. Our proposed system obtained the effective detection performance compared to some of the state-of-art methods.

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Automated diagnosis system for age‐related macular degeneration using hybrid features set from fundus images

Khalid

Akram²,

Shehryar

et al. 2020

Int J Imaging Syst Tech

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Impairment to macula can cause loss of central vision. There are various macular disorders that can affect macular region and if not treated at an early stage can cause irreversible central vision loss. Age‐related macular degeneration (AMD) disorder is one of the most threading macular disorder. Bright lesion, drusens presence in macular region is known as the hallmark of AMD disorder. This bright lesion differentiation from other bright lesion like exudates is important for accurate diagnosis of AMD. Focus of this article is automated diagnosis of affected macular region by applying a hybrid features set containing textural, color, and structural/shape features for more accurate detection of AMD at an early stage using fundus images. These features also help to distinguish drusens from exudates. The proposed algorithm at first stage, detect macular region from input fundus image and then perform features extraction based on textural pattern, edge, and structural properties of macular region to classify abnormal macula from normal macula. For classification, we have used support vector machine (SVM), K‐nearest neighbor and neural networks but SVM classifier achieves high accuracy. The proposed algorithm is tested on publicly available STARE and locally available AFIO datasets. Attained sensitivity, specificity, and accuracy of our proposed system are 97.5%, 95% and 95.45%, respectively, when applied on STARE dataset. When we have applied our proposed system on AFIO dataset, we have attained sensitivity, specificity, and accuracy of 93.3%, 92% and 92.34%, respectively.

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A Bag of Words approach for discriminating between retinal images containing exudates or drusen

Cited by 17 publications

References 11 publications

Discrimination of retinal images containing bright lesions using sparse coded features and SVM

Discrimination of retinal images containing bright lesions using sparse coded features and SVM

Bright Lesion Detection in Color Fundus Images Based on Texture Features

Automated diagnosis system for age‐related macular degeneration using hybrid features set from fundus images

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