Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image

Singh, Anushikha; Dutta, Malay Kishore; Parthasarathi, M.; Uher, Václav; Bürget, Radim

doi:10.1016/j.cmpb.2015.10.010

Cited by 173 publications

(85 citation statements)

References 12 publications

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“…Huiqi Li et al extracted local features from slit-lamp images and considered the nuclear cataract grading task as a support vector regression [12]. In addition, there are still some reasonable CAD methods based on other ocular images achieving effective results [13–15]. …”

Section: Introductionmentioning

confidence: 99%

Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

et al. 2017

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Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.

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Section: Introductionmentioning

confidence: 99%

Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

et al. 2017

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“…To attain the meaningful data, extra competent segmentation way is required. A little of the segmentation ways are edge detection, wavelet feature (Singh, Dutta, ParthaSarathi, Uher, & Burget, 2016), watershed algorithm, and clustering.…”

Section: Morphological Processing Methodologiesmentioning

confidence: 99%

Fundus image classification methods for the detection of glaucoma: A review

Saba

Bokhari

Sharif

et al. 2018

Microscopy Res & Technique

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Glaucoma is a neurodegenerative illness and is considered as a standout amongst the most widely recognized reasons for visual impairment. Nerve's degeneration is an irretrievable procedure, so the diagnosis of the illness at an early stage is an absolute requirement to stay away from lasting loss of vision. Glaucoma effected mainly because of increased intraocular pressure, if it is not distinguished and looked early, it can result in visual impairment. There are not generally evident side effects of glaucoma; thus, patients attempt to get treatment just when the seriousness of malady is advanced altogether. Determination of glaucoma often comprises of review of the basic crumbling of the nerve in conjunction with the examination of visual function capacity. This article shows the persistent illustration of glaucoma, its side effects, and the potential people inclined to this malady. The essence of this article is on different classification methods being utilized and proposed by various scientists for the identification of glaucoma. This article audits a few division and segmentation methodologies that are exceptionally useful for recognizable proof, identification, and diagnosis of glaucoma. The research related to the findings and the treatment is likewise evaluated in this article.

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“…Intel Core-i7 5820K, 64GB RAM GeForce GTX 770 GPU [9] Keras API for TensorFlow library --- [11] Caffe framework Tesla K20C graphics card [12] MATLAB Intel Xeon CPU E3-1225, 3.3GHz, 16GB RAM GPU [14] MATLAB CPU: 2.3 GHz, 4Gb RAM [1] Tensorflow library --- [15] MATLAB Intel Xeon 2.20 GHz (E5-2650 v4), 512GB RAM [16] Keras API for TensorFlow library Theano backend…”

Section: Hardware and Software Environmentsmentioning

confidence: 99%

Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Elloumi

Akil

Boudegga

2019

Real-Time Image Processing and Deep Learning 2019

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Ocular pathology detection from fundus images presents an important challenge on health care. In fact, each pathology has different severity stages that may be deduced by verifying the existence of specific lesions. Each lesion is characterized by morphological features. Moreover, several lesions of different pathologies have similar features. We note that patient may be affected simultaneously by several pathologies. Consequently, the ocular pathology detection presents a multiclass classification with a complex resolution principle. Several detection methods of ocular pathologies from fundus images have been proposed. The methods based on deep learning are distinguished by higher performance detection, due to their capability to configure the network with respect to the detection objective. This work proposes a survey of ocular pathology detection methods based on deep learning. First, we study the existing methods either for lesion segmentation or pathology classification. Afterwards, we extract the principle steps of processing and we analyze the proposed neural network structures. Subsequently, we identify the hardware and software environment required to employ the deep learning architecture. Thereafter, we investigate about the experimentation principles involved to evaluate the methods and the databases used either for training and testing phases. The detection performance ratios and execution times are also reported and discussed.

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Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image

Cited by 173 publications

References 12 publications

Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

Fundus image classification methods for the detection of glaucoma: A review

Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

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