retinal image

ret·i·nal im·age

a real image formed on the retina.
References in periodicals archive ?
Computer based retinal image analysis was first implemented in 1974 [1] and it is now becoming a mainstream technique for quick and accurate detection of retinal diseases such as diabetic retinopathy (DR) and glaucoma [2].
This pyramid was tested on the Fundus Image Registration Dataset consisting of 134 retinal image pairs.
However, in our case, we speculate that astigmatism may have caused reduced retinal image quality from birth (blurred vision) that, consequently, lead to astigmatism-related amblyopia.
To perform this detection in an automated system, the optical disc region must be extracted from retinal image through segmentation process.
OD location candidates are identified by using RGB red channel, color retinal image more tends to be saturated.
Insufficient information may be sensitive to other structures in retinal image (as shown in Figure 1) such as OD and bright and dark lesions in pathological image [26], resulting in segmentation performance degradation.
The study of 142,018 images from 20,258 consecutive patients reviewed three CE marked automated retinal image analysis software solutions for effectiveness and cost- effectiveness.
The partnership includes sharing knowledge to further research in the field of retinal image analysis and help develop non-invasive and convenient health monitoring and early disease identification.
This automatic detection of DR requires important features from the digital retinal image, i.e.
Morphological methods examine the geometric vessel-like structure of retinal image by probing it with small patterns called structuring elements (SE) of predefined size and shape.
This research proposes on automatically finding the location of Optic Disc (OD) in the retinal image using the Hough transform.