by Haleem, MS, Han, L, van Hemert, J and Li, B
Abstract:
Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention. This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis.
Reference:
Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review (Haleem, MS, Han, L, van Hemert, J and Li, B), In Comput Med Imaging Graph, Elsevier Science, volume 37, 2013.
Bibtex Entry:
@article{HaleemHan2013kq,
abstract = {Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.
This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis.
},
author = {Haleem, MS and Han, L and van Hemert, J and Li, B},
date = {2013-10-01},
date-added = {2013-12-30 13:52:26 +0000},
date-modified = {2013-12-30 15:06:50 +0000},
isbn = {0895-6111},
journal = {Comput Med Imaging Graph},
keywords = {retinal imaging; medical},
number = {7},
pages = {581--96},
publisher = {Elsevier Science},
title = {Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0895611113001468?showall=true},
volume = {37},
year = {2013},
bdsk-url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0895611113001468?showall=true}}