[Thesis]. Manchester, UK: The University of Manchester; 2020.
Inspired by the morphological study of conditions that can cause blindness: glaucoma
and diabetic retinopathy, this research project aims to offer more precise pattern
segmentation on retinal images for assisting diagnosis. Specifically, colour and texture
features were proposed to improve the pattern segmentation.
The research project started with the analysis of intensity changes in the retinal
image, leading to the proposal of a method for optic nerve head segmentation using
the colour features. Due to the unbalanced intensity and sensitivity to noise, the
texture feature was used to handle local noise and offer precise segmentation results,
by the Binary Robust Independent Elementary
Features (BRIEF). Moreover, BRIEF was enhanced by extending it to all colour channels,
called CBrief, resulting in a texture descriptor whose performance is comparable with
the state-of-the-art. In testing the performance of segmentation, CBrief achieved
Accuracy = 93.4%, Sensitivity = 72.6%, and Specificity = 95.1% in the texture synthesised
vascular test. However, CBrief failed to extract the colour-texture feature from retinal
images. In order to investigate the texture in retinal images, the deep texture descriptor,
FVCNN, was applied. The result showed that deep texture descriptor could help in distinguishing
the optic nerve head, blood vessels, and background.
To draw the conclusion, with the study of colour and texture, the new colour-texture
descriptor CBrief was proposed and achieved outstanding performance in texture classification
and segmentation. However, due to the subtlety of the texture contained in retinal
images, it is hard to extract useful texture information. However, the result of FV-CNN
suggested the potential of using deep texture information on the deep segmentation