TY - JOUR
T1 - A novel hybrid approach for automated detection of retinal detachment using ultrasound images
AU - Koh, Joel En Wei
AU - Raghavendra, U.
AU - Gudigar, Anjan
AU - Ping, Ooi Chui
AU - Molinari, Filippo
AU - Mishra, Samarth
AU - Mathavan, Sinnakaruppan
AU - Raman, Rajiv
AU - Acharya, U. Rajendra
PY - 2020/5
Y1 - 2020/5
N2 - Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis.
AB - Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis.
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U2 - 10.1016/j.compbiomed.2020.103704
DO - 10.1016/j.compbiomed.2020.103704
M3 - Article
C2 - 32250849
AN - SCOPUS:85082645467
SN - 0010-4825
VL - 120
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 103704
ER -