PMID- 29218460
OWN - NLM
STAT- MEDLINE
DCOM- 20180827
LR  - 20191008
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Print)
IS  - 0148-5598 (Linking)
VI  - 42
IP  - 1
DP  - 2017 Dec 7
TI  - A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation 
      to Aid Glaucoma Diagnosis.
PG  - 20
LID - 10.1007/s10916-017-0859-4 [doi]
LID - 20
AB  - This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) 
      for automatic boundary detection of optic disc and cup to aid automatic glaucoma 
      diagnosis. The novelty of our approach consists of two aspects: 1) automatic 
      detection of initial optimum object boundary based on a Region Classification 
      Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge 
      Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular 
      points) based on iterative force field calculations with contours obtained from 
      the RCM by minimising energy function (an approach that does not require 
      predefined geometric templates to guide auto-segmentation). Such an approach 
      provides robustness in capturing a range of variations and shapes. We have 
      conducted a comprehensive comparison between our approach and the 
      state-of-the-art existing deformable models and validated it with publicly 
      available datasets. The experimental evaluation shows that the proposed approach 
      significantly outperforms existing methods. The generality of the proposed 
      approach will enable segmentation and detection of other object boundaries and 
      provide added value in the field of medical image processing and analysis.
FAU - Haleem, Muhammad Salman
AU  - Haleem MS
AUID- ORCID: 0000-0001-5946-6567
AD  - School of Computing, Mathematics and Digital Technology, Manchester Metropolitan 
      University, Manchester, M1 5GD, UK. m.haleem@mmu.ac.uk.
FAU - Han, Liangxiu
AU  - Han L
AD  - School of Computing, Mathematics and Digital Technology, Manchester Metropolitan 
      University, Manchester, M1 5GD, UK.
FAU - Hemert, Jano van
AU  - Hemert JV
AD  - Optos Plc, Queensferry House, Carnegie Business Campus, Enterprise Way, 
      Dunfermline, Scotland, KY11 8GR, UK.
FAU - Li, Baihua
AU  - Li B
AD  - Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, 
      UK.
FAU - Fleming, Alan
AU  - Fleming A
AD  - Optos Plc, Queensferry House, Carnegie Business Campus, Enterprise Way, 
      Dunfermline, Scotland, KY11 8GR, UK.
FAU - Pasquale, Louis R
AU  - Pasquale LR
AD  - Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical 
      School, Boston, MA, USA.
FAU - Song, Brian J
AU  - Song BJ
AD  - Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical 
      School, Boston, MA, USA.
LA  - eng
GR  - K12 EY016335/EY/NEI NIH HHS/United States
GR  - EP/J50063X/1/Engineering and Physical Sciences Research Council/
PT  - Journal Article
DEP - 20171207
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - Algorithms
MH  - Glaucoma/*diagnosis
MH  - Humans
MH  - Image Processing, Computer-Assisted/*methods
MH  - *Machine Learning
MH  - Optic Disk/*diagnostic imaging
MH  - Pattern Recognition, Automated/*methods
PMC - PMC5719827
OTO - NOTNLM
OT  - Computer-aided retinal disease diagnosis
OT  - Glaucoma
OT  - Machine learning
OT  - Medical image processing and analysis
COIS- The authors declare that they have no conflict of interest. All the datasets used 
      in this manuscript are publicly available datasets (RIM-ONE [47] and Drishti-GS 
      datasets [48], already in the public domain). There is no issue with Ethical 
      approval and Informed consent.
EDAT- 2017/12/09 06:00
MHDA- 2018/08/28 06:00
PMCR- 2017/12/07
CRDT- 2017/12/09 06:00
PHST- 2017/09/18 00:00 [received]
PHST- 2017/11/07 00:00 [accepted]
PHST- 2017/12/09 06:00 [entrez]
PHST- 2017/12/09 06:00 [pubmed]
PHST- 2018/08/28 06:00 [medline]
PHST- 2017/12/07 00:00 [pmc-release]
AID - 10.1007/s10916-017-0859-4 [pii]
AID - 859 [pii]
AID - 10.1007/s10916-017-0859-4 [doi]
PST - epublish
SO  - J Med Syst. 2017 Dec 7;42(1):20. doi: 10.1007/s10916-017-0859-4.