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.

[1]  Demetri Terzopoulos,et al.  Deformable models , 2000, The Visual Computer.

[2]  J. Beckman,et al.  Carotid artery disease , 2011, BMJ.

[3]  Jasjit S. Suri,et al.  Evaluation of Carotid Wall Thickness by using Computed Tomography and Semiautomated Ultrasonographic Software , 2011 .

[4]  Petia Radeva,et al.  Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos , 2016, Journal of Medical Systems.

[5]  J. S. Suri,et al.  Plaque Imaging: Pixel to Molecular Level , 2005 .

[6]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[7]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[8]  Rafael Verdú,et al.  Segmentation of the Common Carotid Artery Walls Based on a Frequency Implementation of Active Contours , 2013, Journal of Digital Imaging.

[9]  A. Laine,et al.  Ultrasound Imaging , 2012, Springer US.

[10]  Ali Emrouznejad,et al.  Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres , 2015, Journal of Medical Systems.

[11]  Aurélio J. C. Campilho,et al.  Automatic detection of the carotid lumen axis in B-mode ultrasound images , 2014, Comput. Methods Programs Biomed..

[12]  U. Rajendra Acharya,et al.  Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. , 2012, Ultrasonics.

[13]  V. Filardi,et al.  Carotid Artery Stenosis near a Bifurcation Investigated by Fluid Dynamic Analyses , 2013, The neuroradiology journal.

[14]  Elisa Cuadrado Godia,et al.  Carotid Artery Distensibility , 2007, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[15]  Emmanouil G. Sifakis,et al.  Robust Carotid Artery Recognition in Longitudinal B-Mode Ultrasound Images , 2014, IEEE Transactions on Image Processing.

[16]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[17]  João Manuel R. S. Tavares,et al.  A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images , 2013, Expert Syst. Appl..

[18]  Jeny Rajan,et al.  Carotid inter‐adventitial diameter is more strongly related to plaque score than lumen diameter: An automated tool for stroke analysis , 2016, Journal of clinical ultrasound : JCU.

[19]  João Manuel R. S. Tavares,et al.  Toward hemodynamic diagnosis of carotid artery stenosis based on ultrasound image data and computational modeling , 2014, Medical & Biological Engineering & Computing.

[20]  Xin Yang,et al.  Ultrasound Common Carotid Artery Segmentation Based on Active Shape Model , 2013, Comput. Math. Methods Medicine.

[21]  Filippo Molinari,et al.  Fully Automated Dual‐Snake Formulation for Carotid Intima‐Media Thickness Measurement , 2012, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[22]  H. Schima,et al.  Effect of continuous arterial blood flow in patients with rotary cardiac assist device on the washout of a stenosis wake in the carotid bifurcation: a computer simulation study. , 2007, Journal of biomechanics.

[23]  Kecheng Liu,et al.  Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review , 2002, IEEE Transactions on Information Technology in Biomedicine.

[24]  Emmanouil G. Sifakis,et al.  Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. , 2007, Ultrasound in medicine & biology.

[25]  Jasjit S. Suri,et al.  Atherosclerosis disease management , 2011 .

[26]  J. Suri,et al.  An Integrated Approach to Computer‐Based Automated Tracing and Its Validation for 200 Common Carotid Arterial Wall Ultrasound Images , 2010, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[27]  Christos P. Loizou,et al.  Integrated System for the Complete Segmentation of the Common Carotid Artery Bifurcation in Ultrasound Images , 2013, AIAI.

[28]  Nilanjan Dey,et al.  Effect of Watermarking on Diagnostic Preservation of Atherosclerotic Ultrasound Video in Stroke Telemedicine , 2016, Journal of Medical Systems.

[29]  KerstinJensen-Urstad,et al.  Carotid Artery Diameter Correlates With Risk Factors for Cardiovascular Disease in a Population of 55-Year-Old Subjects , 1999 .

[30]  Stefan Klein,et al.  Lumen Segmentation and Motion Estimation in B-Mode and Contrast-Enhanced Ultrasound Images of the Carotid Artery in Patients With Atherosclerotic Plaque , 2015, IEEE Transactions on Medical Imaging.

[31]  Chris L de Korte,et al.  Vascular ultrasound for atherosclerosis imaging , 2011, Interface Focus.

[32]  Filippo Molinari,et al.  Comparison between manual and automated analysis for the quantification of carotid wall by using sonography. A validation study with CT. , 2012, European journal of radiology.

[33]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[34]  U. Rajendra Acharya,et al.  Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods , 2012, Comput. Methods Programs Biomed..

[35]  Kecheng Liu,et al.  A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II , 2002, IEEE Transactions on Information Technology in Biomedicine.

[36]  Filippo Molinari,et al.  Association of automated carotid IMT measurement and HbA1c in Japanese patients with coronary artery disease. , 2013, Diabetes research and clinical practice.

[37]  F. Lizzi.  Ultrasound Imaging , 1991, Proceedings Technology Requirements for Biomedical Imaging.

[38]  João Manuel R. S. Tavares,et al.  Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images , 2013, Medical Imaging.

[39]  Jasjit S. Suri,et al.  Handbook of Biomedical Image Analysis , 2005 .

[40]  M. Jensen-Urstad,et al.  Carotid artery diameter correlates with risk factors for cardiovascular disease in a population of 55-year-old subjects. , 1999, Stroke.

[41]  POLITECNICO DI TORINO.  Hypothesis Validation of Far-Wall Brightness in Carotid-Artery Ultrasound for Feature-Based IMT Measurement Using a Combination of Level-Set Segmentation and Registration , .

[42]  Jasjit S. Suri,et al.  Inter-Greedy Technique for Fusion of Different Segmentation Strategies Leading to High-Performance Carotid IMT Measurement in Ultrasound Images , 2011, Journal of Medical Systems.

[43]  Hua Guo,et al.  Measurement of common carotid artery lumen dynamics during the cardiac cycle using magnetic resonance TrueFISP cine imaging , 2008, Journal of magnetic resonance imaging : JMRI.

[44]  Robert M. Haralick,et al.  Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms , 2000, Pattern Analysis & Applications.

[45]  Wendy S Post,et al.  Incident Stroke Is Associated With Common Carotid Artery Diameter and Not Common Carotid Artery Intima-Media Thickness , 2014, Stroke.

[46]  Jasjit S. Suri,et al.  Ultrasound-Based Automated Carotid Lumen Diameter/Stenosis Measurement and its Validation System , 2016 .

[47]  Sameer Singh,et al.  Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part I): A State-of-the-Art Review , 2002, Pattern Analysis & Applications.

[48]  Jasjit S. Suri,et al.  Multi-Modality Atherosclerosis Imaging and Diagnosis , 2013 .

[49]  Ayman El-Baz,et al.  Stochastic Modeling for Medical Image Analysis , 2015 .

[50]  Guang Zeng,et al.  Carotid artery recognition system: a comparison of three automated paradigms for ultrasound images. , 2011, Medical physics.

[51]  Kecheng Liu,et al.  A review on MR vascular image processing algorithms: acquisition and prefiltering: part I , 2002, IEEE Transactions on Information Technology in Biomedicine.

[52]  David L. Wilson,et al.  Handbook of Biomedical Image Analysis: Volume 1: Segmentation Models Part A (Topics in Biomedical EngineeringInternational Book Series) , 2005 .