PMID- 28283997
OWN - NLM
STAT- MEDLINE
DCOM- 20171010
LR  - 20181113
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Linking)
VI  - 41
IP  - 4
DP  - 2017 Apr
TI  - Decision Support System for Detection of Papilledema through Fundus Retinal 
      Images.
PG  - 66
LID - 10.1007/s10916-017-0712-9 [doi]
AB  - A condition in which the optic nerve inside the eye is swelled due to increased 
      intracranial pressure is known as papilledema. The abnormalities due to 
      papilledema such as opacification of Retinal Nerve Fiber Layer (RNFL), dilated 
      optic disc capillaries, blurred disc margins, absence of venous pulsations, 
      elevation of optic disc, obscuration of optic disc vessels, dilation of optic 
      disc veins, optic disc splinter hemorrhages, cotton wool spots and hard exudates 
      may result in complete vision loss. The ophthalmologists detect papilledema by 
      means of an ophthalmoscope, Computed Tomography (CT), Magnetic Resonance Imaging 
      (MRI) and ultrasound. Rapid development of computer aided diagnostic systems has 
      revolutionized the world. There is a need to develop such type of system that 
      automatically detects the papilledema. In this paper, an automated system is 
      presented that detects and grades the papilledema through analysis of fundus 
      retinal images. The proposed system extracts 23 features from which six textural 
      features are extracted from Gray-Level Co-occurrence Matrix (GLCM), eight 
      features from optic disc margin obscuration, three color based features and seven 
      vascular features are extracted. A feature vector consisting of these features is 
      used for classification of normal and papilledema images using Support Vector 
      Machine (SVM) with Radial Basis Function (RBF) kernel. The variations in retinal 
      blood vessels, color properties, texture deviation of optic disc and its 
      peripapillary region, and fluctuation of obscured disc margin are effectively 
      identified and used by the proposed system for the detection and grading of 
      papilledema. A dataset of 160 fundus retinal images is used which is taken from 
      publicly available STARE database and local dataset collected from Armed Forces 
      Institute of Ophthalmology (AFIO) Pakistan. The proposed system shows an average 
      accuracy of 92.86% for classification of papilledema and normal images. It also 
      shows an average accuracy of 97.85% for classification of already classified 
      papilledema images into mild and severe papilledema. The proposed system is a 
      novel step towards automated detection and grading of papilledema. The results 
      showed that the technique is reliable and can be used as clinical decision 
      support system.
FAU - Akbar, Shahzad
AU  - Akbar S
AD  - Department of Computer Science, COMSATS Institute of Information Technology, Wah 
      Cantonment, Pakistan. shahzadakbarbzu@gmail.com.
FAU - Akram, Muhammad Usman
AU  - Akram MU
AD  - Department of Computer Engineering, College of E&ME, National University of 
      Sciences and Technology, Islamabad, Pakistan.
FAU - Sharif, Muhammad
AU  - Sharif M
AD  - Department of Computer Science, COMSATS Institute of Information Technology, Wah 
      Cantonment, Pakistan.
FAU - Tariq, Anam
AU  - Tariq A
AD  - Department of Computer Engineering, College of E&ME, National University of 
      Sciences and Technology, Islamabad, Pakistan.
FAU - Yasin, Ubaid Ullah
AU  - Yasin UU
AD  - Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan.
LA  - eng
PT  - Journal Article
DEP - 20170310
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - *Fundus Oculi
MH  - Humans
MH  - Image Interpretation, Computer-Assisted/*methods
MH  - Pakistan
MH  - Papilledema/*diagnosis/*diagnostic imaging
MH  - *Support Vector Machine
OTO - NOTNLM
OT  - Mild papilledema
OT  - Optic nerve
OT  - Papilledema
OT  - Severe papilledema
OT  - Support vector machine
EDAT- 2017/03/12 06:00
MHDA- 2017/10/11 06:00
CRDT- 2017/03/12 06:00
PHST- 2016/12/26 00:00 [received]
PHST- 2017/02/22 00:00 [accepted]
PHST- 2017/03/12 06:00 [entrez]
PHST- 2017/03/12 06:00 [pubmed]
PHST- 2017/10/11 06:00 [medline]
AID - 10.1007/s10916-017-0712-9 [pii]
AID - 10.1007/s10916-017-0712-9 [doi]
PST - ppublish
SO  - J Med Syst. 2017 Apr;41(4):66. doi: 10.1007/s10916-017-0712-9. Epub 2017 Mar 10.