Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches.
The degree of stenosis in the carotid artery can be predicted using automated carotid lumen diameter (LD) measured from B-mode ultrasound images. Systolic velocity-based methods for measurement of LD are subjective. With the advancement of high resolution imaging, image-based methods have started to emerge. However, they require robust image analysis for accurate LD measurement. This paper presents two different algorithms for automated segmentation of the lumen borders in carotid ultrasound images. Both algorithms are modeled as a two stage process. Stage one consists of a global-based model using scale-space framework for the extraction of the region of interest. This stage is common to both algorithms. Stage two is modeled using a local-based strategy that extracts the lumen interfaces. At this stage, the algorithm-1 is modeled as a region-based strategy using a classification framework, whereas the algorithm-2 is modeled as a boundary-based approach that uses the level set framework. Two sets of databases (DB), Japan DB (JDB) (202 patients, 404 images) and Hong Kong DB (HKDB) (50 patients, 300 images) were used in this study. Two trained neuroradiologists performed manual LD tracings. The mean automated LD measured was 6.35 ± 0.95 mm for JDB and 6.20 ± 1.35 mm for HKDB. The precision-of-merit was: 97.4 % and 98.0 % w.r.t to two manual tracings for JDB and 99.7 % and 97.9 % w.r.t to two manual tracings for HKDB. Statistical tests such as ANOVA, Chi-Squared, T-test, and Mann-Whitney test were conducted to show the stability and reliability of the automated techniques.
Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan.
Tadashi
T
Araki
Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India.|Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA.
P Krishna
PK
Kumar
Mira Loma, Sacramento, CA, USA.|Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
Harman S
HS
Suri
Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan.
Nobutaka
N
Ikeda
Radiology Department, Brain & Mind Research Institute, Weill Cornell Medical College, New York, NY, USA.
Ajay
A
Gupta
Department of Radiology, University of Cagliari, Cagliari, Italy.
Luca
L
Saba
Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India.|Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA.
Jeny
J
Rajan
Department of Radiology, University of Cagliari, Cagliari, Italy.
Francesco
F
Lavra
Department of Medicine, Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA.
Aditya M
AM
Sharma
CorVasc Vascular Laboratory, Indianapolis, IN, USA.
Shoaib
S
Shafique
Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus.
Andrew
A
Nicolaides
UC Davis Vascular Center, University of California, Davis, CA, USA.
John R
JR
Laird
Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA. jasjit.suri@atheropoint.com.|Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. jasjit.suri@atheropoint.com.|Department of Electrical Engineering, University of Idaho, Moscow, ID, USA. jasjit.suri@atheropoint.com.
Jasjit S
JS
Suri
D000368:Aged; D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D016893:Carotid Stenosis / Q000175:diagnosis* / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D014463:Ultrasonography / Q000379:methods*
B-mode ultrasound; Boundary-based; Carotid artery; Classification; Level set segmentation; Lumen diameter; Region-based; Scale-space