Autonomous Glaucoma detection from fundus image using cup to disc ratio and hybrid features

Salam, Anum Abdul; Akram, M. Usman; Wazir, Kamran; Anwar, Syed Muhammad; Majid, Muhammad

doi:10.1109/isspit.2015.7394362

Cited by 32 publications

(22 citation statements)

References 8 publications

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“…In literature, there are methods proposed for the diagnosis of diseases such as fatty liver [30], prostate cancer [29], dry eye [31], Alzheimer [32], and breast cancer [33]. In [34], hybrid features are used for the detection glaucoma in fundus images. The optic disc is localized by employing support vector machine trained using local features extracted from the vessels [35].…”

Section: Computer Aided Detection or Diagnosismentioning

confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval.

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Section: Computer Aided Detection or Diagnosismentioning

confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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“…Rise of IOP is one of the most serious causes of glaucoma leading to blindness all over the world. It is known as the silent thief of vision because it can sneak up into any patient [1]. The blindness caused by IOP is irreversible as the optic nerve dies [2].…”

Section: Introductionmentioning

confidence: 99%

A smart intraocular pressure risk assessment framework using frontal eye image analysis

Aloudat

Faezipour

El-Sayed

2018

J Image Video Proc.

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Intraocular pressure (IOP) in general refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions/symptoms that may lead to certain diseases such as glaucoma and therefore must be closely monitored. While the pressure in the eye increases, different parts of the eye may become affected until the eye parts are damaged. An effective way to prevent rise in eye pressure is by early detection. A new smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images. The framework monitors the status of IOP risk by analyzing frontal eye images using image processing and machine learning techniques. A database of images collected from Princess Basma Hospital in Jordan was used in this work. The database contains 400 eye images: 200 images with normal IOP and 200 high eye pressure case images. The framework extracts five features from the frontal eye image: the pupil and iris diameter ratio, mean redness level of the sclera, red area percentage of the sclera, and two other features measured from the extracted contour of the sclera (contour height and contour area). Once the features were extracted, a neural network is trained and tested to obtain the status of the patients in terms of eye pressure. The framework detects the status of IOP (normal or high IOP) and produces evidence of the relationship between the five extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques using frontal eye images.

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“…Glaucoma is a group of disease associated with human eye that causes permanent blindness without any symptoms and warnings. Initially, glaucoma increases Intraocular Pressure (IOP) [2] [9] [10] and if not diagnosed at this stage, it automatically destroys the optic nerve and eventually leads to blindness. Optic nerve is located at the back of eye which carries visual information from eye to brain.…”

Section: Introductionmentioning

confidence: 99%

Improved Automatic Detection of Glaucoma Using Cup-to-Disk Ratio and Hybrid Classifiers

Prasad¹,

Vibha²,

Venugopal³

2018

IJIVP

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Glaucoma is one of the most complicated disorder in human eye that causes permanent vision loss gradually if not detect in early stage. It can damage the optic nerve without any symptoms and warnings. Different automated glaucoma detection systems were developed for analyzing glaucoma at early stage but lacked good accuracy of detection. This paper proposes a novel automated glaucoma detection system which effectively process with digital colour fundus images using hybrid classifiers. The proposed system concentrates on both Cup-to Disk Ratio (CDR) and different features to improve the accuracy of glaucoma. Morphological Hough Transform Algorithm (MHTA) is designed for optic disc segmentation. Intensity based elliptic curve method is used for separation of optic cup effectively. Further feature extraction and CDR value can be estimated. Finally, classification is performed with combination of Naive Bayes Classifier and K Nearest Neighbour (KNN). The proposed system is evaluated by using High Resolution Fundus (HRF) database which outperforms the earlier methods in literature in various performance metrics.

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Autonomous Glaucoma detection from fundus image using cup to disc ratio and hybrid features

Cited by 32 publications

References 8 publications

Medical Image Analysis using Convolutional Neural Networks: A Review

Medical Image Analysis using Convolutional Neural Networks: A Review

A smart intraocular pressure risk assessment framework using frontal eye image analysis

Improved Automatic Detection of Glaucoma Using Cup-to-Disk Ratio and Hybrid Classifiers

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