ICA analysis of retina images for glaucoma classification

Fink, Flemming Kobberøe; Wörle, Klaus; Gruber, Peter J.; Tomé, Ana Maria; Sáez, Juan Manuel; Puntonet, Carlos G.; Lang, Elmar

doi:10.1109/iembs.2008.4650253

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…Before 2003, UK programmes used a descriptive DR grading system based on the Europe DR screening protocol. In 2006, 96 screening programmes started in England [ 44 ], which covered 350,000 people. Later, a new DR screening pathway based on the grading system, which used primary and secondary as two-level grading and an arbitrator as arbitration in another level if necessary, was introduced [ 40 ].…”

Section: Dr Screening Programmes In the Worldmentioning

confidence: 99%

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Teleophthalmology in Preventive Medicine

Michelson¹

2015

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Section: Dr Screening Programmes In the Worldmentioning

confidence: 99%

“…A further appearance-based method for glaucoma detection has been proposed by Fink et al [44]. This method utilizes the independent component analysis (ICA) [45] on images captured with a confocal laser ophthalmoscope (Heidelberg retina tomograph (HRT)).…”

Section: Independent Component Analysismentioning

confidence: 99%

Teleophthalmology in Preventive Medicine

Michelson¹

2015

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“…Disk size is used by Hoffmann et al, (2007) for assessment to diagnose and manage glaucoma. Fink et al, (2008) uses independent component analysis (ICA) for feature extraction from color fundus images and k-nearest neighbour classifier (KNN) for glaucoma classification [19]. ICA finds the basis images which represent the independent feature characteristic of the observed retina images of the papilla.…”

Section: Techniques For Glaucoma Assessmentmentioning

confidence: 99%

Techniques of Glaucoma Detection From Color Fundus Images: A Review

Nath¹,

Dandapat²

2012

IJIGSP

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Glaucoma is a generic name for a group of diseases which causes progressive optic neuropathy and vision loss due to degeneration of the optic nerves. Optic nerve cells act as transducer and convert light signal entered into the eye to electrical signal for visual processing in the brain. The main risk factors of glaucoma are elevated intraocular pressure exerted by aqueous humour, family history of glaucoma (hereditary) and diabetes. It causes damages to the eye, whether intraocular pressure is high, normal or below normal. It causes the peripheral vision loss. There are different types of glaucoma. Some glaucoma occurs suddenly. So, detection of glaucoma is essential for minimizing the vision loss. Increased cup area to disc area ratio is the significant change during glaucoma. Diagnosis of glaucoma is based on measurement of intraocular pressure by tonometry, visual field examination by perimetry and measurement of cup area to disc area ratio from the color fundus images. In this paper the different signal processing techniques are discussed for detection and classification of glaucoma.

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“…This method suffers the main problem that the well-known small sample size problem. By contrast, as a multivariate analysis method, independent component analysis (ICA) is a significant kind of blind signal separation and has already applied to structural [9] [11] and functional brain images [13] [14]. It is also able to probe into PET datasets to provide some useful information about the relationship among voexls.…”

Section: Introductionmentioning

confidence: 97%

Automated classification of positron emission tomography images with mild cognitive impairment based on voxel of interest and neuropsychological test results

Yang

2013

2013 Ninth International Conference on Natural Computation (ICNC)

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Alzheimer's disease (AD) is the most common form of dementia with the unknown pathogenesis and pathologies. It brings about serious social problems. As predementia AD, mild cognitive impairment (MCI) subjects are usually overlooked because of the cryptic features of the occurrence and development of the disease. To detect MCI subjects from healthy controls as early and accurately as possible is of great importance and urgency to delay or prevent the onset of AD. In the paper, we propose a novel systematic method combining voxel of interest in positron emission tomography (PET) images and neuropsychological test results for automated classification of MCI subjects versus healthy controls (HC) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The method includes four steps: pre-processing, extracting independent components, selecting voxels of interest, and classifying using a support vector machine classifier. PET images were obtained from ADNI database including 91 HC and 105 MCI patients with baseline diagnosis of MCI. As a result, we achieved good discrimination between MCI patients and HC with the averaged classification accuracy of 95.58%, sensitivity of 94.33%, and specificity of 97.04%. The experimental results show that the proposed method can successfully distinguish MCI from HC, and it is able to obtain higher classification accuracy of MCI versus HC than using only independent components or neuropsychological test results.

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ICA analysis of retina images for glaucoma classification

Cited by 17 publications

References 6 publications

Teleophthalmology in Preventive Medicine

Teleophthalmology in Preventive Medicine

Techniques of Glaucoma Detection From Color Fundus Images: A Review

Automated classification of positron emission tomography images with mild cognitive impairment based on voxel of interest and neuropsychological test results

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