UB ScholarWorks

A Multiple Retinal Normal and Abnormal Anatomical Structures Segmentation Using Hybrid Morphological and Fuzzy Local Adaptive Thresholding

Show simple item record

dc.contributor.author Almotiri, Jasem
dc.contributor.author Elleithy, Khaled M.
dc.contributor.author Elleithy, Abdelrahman
dc.date.accessioned 2018-05-04T13:18:01Z
dc.date.available 2018-05-04T13:18:01Z
dc.date.issued 2018-03-23
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2235
dc.description.abstract Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures. en_US
dc.language.iso en_US en_US
dc.subject Diagnosis en_US
dc.subject Image segmentation system en_US
dc.subject Retinal fundus en_US
dc.title A Multiple Retinal Normal and Abnormal Anatomical Structures Segmentation Using Hybrid Morphological and Fuzzy Local Adaptive Thresholding en_US
dc.type Other en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ScholarWorks


Advanced Search

Browse

My Account