Diabetic Macular Edema

Hirashima, Takafumi; Hagihara, Masanori; Oh, Hideyasu

doi:10.1007/978-3-319-49840-9_12

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Cited by 3 publications

(2 citation statements)

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“…1). Early detection of retinal abnormalities is considerably important in preventing DME and subsequent loss of vision problem [4].…”

Section: Introductionmentioning

confidence: 99%

Classification of dry age‐related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

Mousavi

Kafieh

Rabbani

2020

IET Image Processing

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“…1). Early detection of retinal abnormalities is considerably important in preventing DME and subsequent loss of vision problem [4].…”

Section: Introductionmentioning

confidence: 99%

Classification of dry age‐related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

Mousavi

Kafieh

Rabbani

2020

IET Image Processing

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“…1). Early detection of retinal abnormalities is considerably important in preventing DME and subsequent loss of vision problem [4]. 6% of diabetic people are affected by DME which can resulted in more than 20 million cases worldwide [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of dry age-related macular degeneration and diabetic macular edema from optical coherence tomography images using dictionary learning

Mousavi¹,

Kafieh²,

Rabbani³

2019

Preprint

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Age-related Macular Degeneration (AMD) and DiabeticMacular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudate are the most significant signs of these diseases. With the aim of automatic classification of DME, AMD and normal subjects from Optical Coherence Tomography (OCT) images, we proposed a classification algorithm. The two important issues intended in this approach are, not utilizing retinal layer segmentation which by itself is a challenging task and attempting to identify diseases in their early stages, where the signs of diseases appear in a small fraction of B-Scans. We used a histogram of oriented gradients (HOG) feature descriptor to well characterize the distribution of local intensity gradients and edge directions. In order to capture the structure of extracted features, we employed different dictionary learning-based classifiers. Our dataset consists of 45 subjects: 15 patients with AMD, 15 patients with DME and 15 normal subjects. The proposed classifier leads to an accuracy of 95.13%, 100.00%, and 100.00% for DME, AMD, and normal OCT images, respectively, only by considering the 4% of all B-Scans of a volume which outperforms the state of the art methods.

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Macular Thickness Changes in Diabetic Versus Non Diabetic Patients After Uncomplicated Phacoemulsification

Hassanin

Mohamed

Ibrahim

2021

Al-Azhar Medical Journal

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Diabetic Macular Edema

Cited by 3 publications

References 32 publications

Classification of dry age‐related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

Classification of dry age‐related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

Classification of dry age-related macular degeneration and diabetic macular edema from optical coherence tomography images using dictionary learning

Macular Thickness Changes in Diabetic Versus Non Diabetic Patients After Uncomplicated Phacoemulsification

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