Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm

  • Luca Saba
  • Pankaj K. Jain
  • Harman S. Suri
  • Nobutaka Ikeda
  • Tadashi Araki
  • Bikesh K. Singh
  • Andrew Nicolaides
  • Shoaib Shafique
  • Ajay Gupta
  • John R. Laird
  • Jasjit S. Suri
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.


Atherosclerosis Carotid artery Stroke risk Machine learning Principal component analysis Far Near Performance evaluation 


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Luca Saba
    • 1
  • Pankaj K. Jain
    • 2
  • Harman S. Suri
    • 3
  • Nobutaka Ikeda
    • 4
  • Tadashi Araki
    • 5
  • Bikesh K. Singh
    • 6
  • Andrew Nicolaides
    • 7
    • 8
  • Shoaib Shafique
    • 9
  • Ajay Gupta
    • 10
  • John R. Laird
    • 11
  • Jasjit S. Suri
    • 3
    • 12
  1. 1.Department of RadiologyUniversity of CagliariCagliariItaly
  2. 2.Point-of-Care DevicesGlobal Biomedical Technologies, Inc.RosevilleUSA
  3. 3.Monitoring and Diagnostic Division, AtheroPoint™RosevilleUSA
  4. 4.Cardiovascular Medicine, National Center for Global Health and MedicineTokyoJapan
  5. 5.Division of Cardiovascular MedicineToho University Ohashi Medical CenterTokyoJapan
  6. 6.Department of Biomedical EngineeringNIT RaipurRaipurIndia
  7. 7.Vascular Screening and Diagnostic CentreLondonUK
  8. 8.Vascular Diagnostic CentreUniversity of CyprusNicosiaCyprus
  9. 9.CorVasc Vascular LaboratoryIndianapolisUSA
  10. 10.Brain and Mind Research Institute and Department of RadiologyWeill Cornell Medical CollegeNew YorkUSA
  11. 11.UC Davis Vascular CentreUniversity of CaliforniaDavisUSA
  12. 12.Department of Electrical EngineeringUniversity of Idaho (Affl.)PocatelloUSA

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