A Bayesian framework for glaucoma progression detection using Heidelberg Retina Tomograph images

Belghith, Akram; Bowd, Christopher; Balasubramanian, Madhusudhanan; Weinreb, Robert N.; Zangwill, Linda M.

doi:10.14569/ijacsa.2013.040934

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Another method called the proper orthogonal decomposition [5] indirectly utilizes the spatial relationship among voxels by controlling the family-wise Type I error rate. The Markov random field (MRF) model was used in [6] to model the inter/intra observations dependency allowing a better glaucoma progression detection rate. However, the HRT imaging technique is limited by its lower resolution (i.e.…”

Section: Introductionmentioning

confidence: 99%

Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression

Belghith

Bowd

Medeiros

et al. 2015

Artificial Intelligence in Medicine

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Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer. Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. Three dimensional (3D) spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, is now the standard of care for diagnosing and monitoring progression of numerous eye diseases. Method This paper aims to detect changes in multi-temporal 3D SD-OCT ONH images using a hierarchical fully Bayesian framework and then to differentiate between changes reflecting random variations or true changes due to glaucoma progression. To this end, we propose the use of kernel-based support vector data description (SVDD) classifier. SVDD is a well-known one-class classifier that allows us to map the data into a high-dimensional feature space where a hypersphere encloses most patterns belonging to the target class. Results The proposed glaucoma progression detection scheme using the whole 3D SD-OCT images detected glaucoma progression in a significant number of cases showing progression by conventional methods (78%), with high specificity in normal and non-progressing eyes (93% and 94% respectively). Conclusion The use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.

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Section: Introductionmentioning

confidence: 99%

Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression

Belghith

Bowd

Medeiros

et al. 2015

Artificial Intelligence in Medicine

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“…32 Gonioscopy images have also been used for automated glaucoma detection based on ACA using the SVM classifier and achieved 88% accuracy. 33…”

Section: Non-structural Analysismentioning

confidence: 99%

A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage

Khalil

Akram

Khalid

et al. 2021

Int J Imaging Syst Tech

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Glaucoma ocular disease is the second topmost reason for irreversible visual impairment around the world. This malady can be cured and permanent blindness can be prevented by timely diagnosis and treatment. This study is an attempt to analyse the current status of automated glaucoma diagnosis systems.Existing systems have been analysed on the base of ophthalmic imaging technology, capability to detect glaucoma at initial or latter stages, detection strategy and performance. Analysis revealed several research gaps mainly in automated glaucoma detection based on Fundus and Optical Coherence Tomography (OCT) ophthalmic imaging technologies. More accurate diagnosis of glaucoma at early and curable stages is possible by bridging the identified gaps.

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“…Model parametrelerini hesaplamak için Monte Carlo Markov Zinciri prosedürü kullanılmıştır. Daha sonra mevcut glokom ilerleyişini algılama metotları için önerilen çerçevenin teşhis performansı karşılaştırılmıştır [6].…”

Section: Introductionunclassified

Veri İndirgeme ve Sınıflandırma Teknikleri ile Glokom Hastalığı Teşhisi

Şatır

Azboy²,

Aydin³

et al. 2016

El-Cezeri Fen Ve Mühendislik Dergisi

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Glokom göz içi basıncının sıklıkla yükselmesi nedeniyle görme sinirinin zarar görmesidir. Kendini hastalığın en son aşamalarında fark ettiren sinsi bir hastalık olan glokom, geç tanı konulduğunda görme sinirinde onarılması mümkün olmayan ciddi tahribatlar oluşturabilmektedir. Bu çalışmadaki amaç az nitelikle glokom hastalığının teşhis edilmesidir. Çalışmada Hacıefendioğlu'nun yüksek lisans tezinden alınan hasta bilgileri veri kümesi olarak kullanılmıştır. Bu veri kümesindeki nitelik sayısı, kaba kümeler yöntemleri ile 8'den 5'e düşürülmüştür. İndirgenen veriler Karar Ağaçları ve Yapay Sinir Ağları kullanılarak sınıflandırılmış ve ardından da Çapraz Doğrulama ile performans değerlendirilmesi gerçekleştirilmiştir.

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A Bayesian framework for glaucoma progression detection using Heidelberg Retina Tomograph images

Cited by 4 publications

References 19 publications

Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression

Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression

A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage

Veri İndirgeme ve Sınıflandırma Teknikleri ile Glokom Hastalığı Teşhisi

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