TY - GEN
T1 - Using ultrasound image analysis to evaluate the role of elastography imaging in the diagnosis of carotid atherosclerosis
AU - Xenikou, Monika Filitsa
AU - Golemati, Spyretta
AU - Gastounioti, Aimilia
AU - Tzortzi, Marianna
AU - Moraitis, Nektarios
AU - Charalampopulos, Georgios
AU - Liasis, Nicolaos
AU - Dedes, Athanasios
AU - Besias, Nicolaos
AU - Nikita, Konstantina S.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risk held by CA for both patient safety and state economies. CA is typically diagnosed and assessed using duplex ultrasonography (US). Elastrography Imaging (EI) is a promising US technique for quantifying tissue elasticity (ES). In this work, we investigated the association between ES of carotid atherosclerotic lesions, derived from EI, and texture indices, calculated from US image analysis. US and EI images of 23 atherosclerotic plaques (16 patients) were analyzed. Texture features derived from US image analysis (Gray-Scale Median (GSM), plaque area (A) and co-occurrence-matrixderived features) were calculated. Statistical analysis revealed associations between US texture features and EI measured indices. This result indicates accordance in US and EI techniques and states the promising role of EI in diagnosis of CA.
AB - Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risk held by CA for both patient safety and state economies. CA is typically diagnosed and assessed using duplex ultrasonography (US). Elastrography Imaging (EI) is a promising US technique for quantifying tissue elasticity (ES). In this work, we investigated the association between ES of carotid atherosclerotic lesions, derived from EI, and texture indices, calculated from US image analysis. US and EI images of 23 atherosclerotic plaques (16 patients) were analyzed. Texture features derived from US image analysis (Gray-Scale Median (GSM), plaque area (A) and co-occurrence-matrixderived features) were calculated. Statistical analysis revealed associations between US texture features and EI measured indices. This result indicates accordance in US and EI techniques and states the promising role of EI in diagnosis of CA.
UR - http://www.scopus.com/inward/record.url?scp=84953315859&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2015.7319836
DO - 10.1109/EMBC.2015.7319836
M3 - Conference contribution
C2 - 26737736
AN - SCOPUS:84953315859
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 6313
EP - 6316
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -