Ophthalmic diseases such as glaucoma are associated with progressive changes in the structure
of the optic disc (OD) and parapapillary atrophy (PPA). These structural changes may therefore
have relevance to other systemic diseases. The size and location of OD and PPA can be used
as registration landmarks for monitoring changes in features of the fundus of the eye. Retinal
vessel evaluation, for example, can be used as a biomarker for the effects of multiple systemic
diseases, or co-morbidities. This thesis presents the first computer-aided measuring tool that
detects and quantifies the progression of PPA automatically on a 2D retinal fundus image in
the presence of image noise. An automated segmentation system is described that can detect
features of the optic nerve. Three novel approaches are explored that extract the PPA and OD
region approximately from a 2D fundus image. The OD region is segmented using (i) a combination
of active contour and morphological operations, (ii) a modified Chan-Vese algorithm
and (iii) a combination of edge detection and ellipse fitting methods. The PPA region is identified
from the presence of bright pixels in the temporal zone of the OD, and segmented using
a sequence of techniques, including a modified Chan-Vese approach, thresholding, scanning
filter and multi-seed region growing methods. The work demonstrates for the first time how the
OD and PPA regions can be identified and quantified from 2D fundus images using a standard
fundus camera
