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Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches

  • Patient Facing Systems
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Abstract

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.

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Authors and Affiliations

  1. Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan

    Tadashi Araki

  2. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India

    P. Krishna Kumar & Jeny Rajan

  3. Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA

    P. Krishna Kumar, Jeny Rajan & Jasjit S. Suri

  4. Mira Loma, Sacramento, CA, USA

    Harman S. Suri

  5. Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA

    Harman S. Suri & Jasjit S. Suri

  6. Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan

    Nobutaka Ikeda

  7. Radiology Department, Brain & Mind Research Institute, Weill Cornell Medical College, New York, NY, USA

    Ajay Gupta

  8. Department of Radiology, University of Cagliari, Cagliari, Italy

    Luca Saba & Francesco Lavra

  9. Department of Medicine, Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA

    Aditya M. Sharma

  10. CorVasc Vascular Laboratory, Indianapolis, IN, USA

    Shoaib Shafique

  11. Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus

    Andrew Nicolaides

  12. UC Davis Vascular Center, University of California, Davis, CA, USA

    John R. Laird

  13. Department of Electrical Engineering, University of Idaho, Moscow, ID, USA

    Jasjit S. Suri

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  1. Tadashi Araki
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Correspondence to Jasjit S. Suri.

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Dr. Jasjit S. Suri has a relationship with AtheroPoint™, Roseville, CA, USA which is dedicated to Atherosclerosis Disease Management including Stroke and Cardiovascular imaging.

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This article is part of the Topical Collection on Patient Facing Systems

Appendices

Appendix-A

Statistical analysis tables on Japan database

Table 7 Mann-Whitney test (Region-based technique)
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Table 8 Mann-Whitney test (Boundary-based technique)
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Table 9 Student’s T- test (Region-based technique)
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Table 10 Student’s T- test (Boundary-based technique)
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Table 11 ANOVA test (Region-based technique)
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Table 12 ANOVA test (Boundary-based technique)
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Table 13 Chi-Square test for LD (Region-based technique)
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Table 14 Chi-Square test for LD (Boundary-based Technique)
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Appendix-B

Statistical analysis tables on Hong Kong database (HKDB)

Table 15 Mann-Whitney test (Region-based technique)
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Table 16 Mann-Whitney test (Boundary-based Technique)
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Table 17 Student’s T- test (Region-based technique)
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Table 18 Student’s T- test (Boundary-based Technique)
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Table 19 ANOVA test for LD (Region-based technique)
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Table 20 ANOVA test for LD (Boundary-based technique)
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Table 21 Chi-Square test for LD (Region-based technique)
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Table 22 Chi-Square test for LD (Boundary-based Technique)
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Araki, T., Kumar, P.K., Suri, H.S. et al. Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches. J Med Syst 40, 182 (2016). https://doi.org/10.1007/s10916-016-0543-0

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