Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection

Agurto, Carla; Murray, Víctor; Barriga, E. Simon; Murillo, Sergio; Pattichis, Marios S.; Davis, Herbert; Russell, Stephen R.; Abràmoff, Michael D.; Solíz, Peter

doi:10.1109/tmi.2009.2037146

Cited by 235 publications

(111 citation statements)

References 25 publications

Supporting

Mentioning

108

Contrasting

Unclassified

Order By: Relevance

“…The only important information is the appearance of each region [9]. When creating a visual dictionary from a training set, the most representative regions for a given lesion is identified.…”

Section: Methodsmentioning

confidence: 99%

Data fusion for multi-lesion Diabetic Retinopathy detection

Jelinek

Pires

Goldenstein

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Data fusion for multi-lesion Diabetic Retinopathy detection

Jelinek

Pires

Goldenstein

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

View full text Add to dashboard Cite

show abstract

“…The algorithm for vessel detection consists in contrast enhancement, application of the morphological top-hat-transform and a postfiltering step in order to distinguish the vessels from other blood containing features (see figure 4). In [11], the use of multiscale amplitude-modulation frequencymodulation (AM-FM) methods for discriminating between normal and pathological retinal images has been proposed. The region types included: microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns.…”

Section: Top-hat: ;mentioning

confidence: 99%

Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques

Singh¹,

Tripathi²

2010

IJCA

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a common retinal complication associated with diabetes. It is a major cause of blindness in middle as well as older age groups. Therefore early detection through regular screening and timely intervention will be highly beneficial in effectively controlling the progress of the disease. Since the ratio of people afflicted with the disease to the number of eye specialist who can screen these patients is very high, there is a need of automated diagnostic system for diabetic retinopathy changes in the eye so that only diseased persons can be referred to the specialist for further intervention and treatment.Various aspects and stages of retinopathy are analyzed by examining the colored retinal images. Microaneurysms are small saccular pouches caused by local distension of capillary walls and appear as small red dots. Their walls are thin and rupture easily to cause hemorrhages. Hard exudates are yellow lipid deposits which appear as bright yellow lesions. The bright circular region from where the blood vessels emanate is called the optic disk. The fovea defines the center of the retina, and is the region of highest visual acuity. The spatial distribution of exudates and microaneurysms and hemorrhages, especially in relation to the fovea can be used to determine the severity of diabetic retinopathy.Image analysis tools can be used for automated detection of these various features and stages of Diabetes Retinopathy and can be referred to the specialist accordingly for intervention, thus making it a very effective tool for effective screening of Diabetic Retinopathy patients. DR patients require frequent, at least six monthly screening of vast number of patients and automating the process will go a long way in relieving the burden on the specialist and reducing the most common cause of preventable blindness.

show abstract

“…Support vector machine is used to categorize a vessel segment as normal or abnormal. Agruto et al (6) proposed a multiscale amplitude modulation-frequency modulation (AM-FM) based method for discriminating between normal vessels and neovascularization. Jelinek et al (7) examined the vascular pattern characteristics to detect PDR on fluorescein angiogram images.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of New vessels for Classification of Proliferative Diabetic Retinopathy

Haq

Akram²,

Saijo³

2014

Proceedings of the 2nd International Conference on Intelligent Systems and Image Processing 2014

View full text Add to dashboard Cite

Optic disc is an important anatomical feature of the retinal image and helps in the diagnosis of eye diseases which appears as a bright circular or elliptic shape in fundus image. Accurate detection of the position of optic disc is useful for the diagnosis of new vessels on optic disc. Proliferative diabetic retinopathy is an advance stage of diabetic retinopathy and causes severe visual impairments. Early detection of the diabetic retinopathy is very important for the protection of patient's vision. Automated systems for screening of proliferative diabetic retinopathy should accurately detect the blood vessels to identify vascular abnormalities. In the proposed method the vascular pattern of normal and abnormal vessels is extracted using Gabor wavelet and segmenting the blood vessels using active contour method. Support vector machine is used for the classification of neovascularization. The proposed method is evaluated using publicly available retinal image databases and results show that the proposed method identifies the vascular abnormalities with high accuracy.

show abstract

Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection

Cited by 235 publications

References 25 publications

Data fusion for multi-lesion Diabetic Retinopathy detection

Data fusion for multi-lesion Diabetic Retinopathy detection

Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques

Automated Detection of New vessels for Classification of Proliferative Diabetic Retinopathy

Contact Info

Product

Resources

About