Macular Lesions Extraction Using Active Appearance Method

Kubíček, Jan; Bryjova, Iveta; Penhaker, Marek

doi:10.1007/978-3-319-29236-6_42

Cited by 2 publications

(1 citation statement)

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“…where E int is the internal energy of the curve, E image is the energy of the image and E con determines the external limitations (Kubicek et al, 2016a(Kubicek et al, , 2016b.…”

Section: Figure 19 Roi Including Retinal Lesionmentioning

confidence: 99%

Modelling and time evaluation of optical disc and retinal lesions

Kubíček¹,

Timkovič²,

Penhaker³

et al. 2018

IJDET

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In the clinical ophthalmology, the retinal image analysis is a routine procedure with a target of the time considering the retinal lesions over the time. Unfortunately, there is an absence of the clinical software instruments providing a precise tracking the retinal lesions from the image records. Thus, the retinal image analysis is carried out by skilled physicians, but without any objective software feedback. We aim to propose a procedure having ambitions to an automatic and autonomous extraction of the retinal lesion from the retinal images, and its time evaluation. The retinal lesions are considered on a base of the optical disc which is simultaneously segmented from the retinal images. It is clinically supposed that the optical disc has stable geometrical features over the time contrarily geometrical features of the retinal lesions are timedeveloped. The proposed methodology for a time modelling of the retinal lesions comprises three essential procedures. The optical disc and the retinal lesions segmentation on a base of the time evolving curves ensure indication areas of these retinal objects. Consequently, the binary classification is used with a target of an extraction of a respective model of the optical disc and the retinal lesions.

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“…where E int is the internal energy of the curve, E image is the energy of the image and E con determines the external limitations (Kubicek et al, 2016a(Kubicek et al, , 2016b.…”

Section: Figure 19 Roi Including Retinal Lesionmentioning

confidence: 99%