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

  • Image & Signal Processing
  • Published:
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Abstract

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.

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Authors and Affiliations

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

    Luca Saba

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

    Pankaj K. Jain

  3. Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA

    Harman S. Suri & Jasjit S. Suri

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

    Nobutaka Ikeda

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

    Tadashi Araki

  6. Department of Biomedical Engineering, NIT Raipur, Raipur, Chhattisgarh, India

    Bikesh K. Singh

  7. Vascular Screening and Diagnostic Centre, London, England, UK

    Andrew Nicolaides

  8. Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus

    Andrew Nicolaides

  9. CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA

    Shoaib Shafique

  10. Brain and Mind Research Institute and Department of Radiology, Weill Cornell Medical College, New York, NY, USA

    Ajay Gupta

  11. UC Davis Vascular Centre, University of California, Davis, CA, USA

    John R. Laird

  12. Department of Electrical Engineering, University of Idaho (Affl.), Pocatello, ID, USA

    Jasjit S. Suri

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  1. Luca Saba
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This article is part of the Topical Collection on Image & Signal Processing

Appendices

Appendix 1: Experimental results

Table 1 Distribution of high risk (HR) and low risk (LR) images in our population based on LD threshold
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Table 2 Kernel optimization results for automatedfar, near and combined wall, K = 10 and 20 PCA cutoff
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Table 3 Fixed data size results of the automated far, near and combined wall accuracy (mean of all LDs)
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Table 4 Fixed data size results of the manual far, near and combined wall accuracy (mean of all LDs)
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Table 5 Changing data size experiment for automated far, near and combined wall: accuracy for K5, K10 and JK over mean of 16 LD threshold
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Table 6 Changing data size experiment for manual far, near and combined wall: accuracy for K = 5,K10 and JK overmean of 16 LD threshold
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Table 7 POM no for far, near and combined wall and three protocol K = 5, 10 and JK with 5% error
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Table 8 Reliability vs. data size for far, near and combined wall for K = 10 mean of 16 LD threshold
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Table 9 Feature retaining power for far, near and combined wall, K = 10, LD = 6.2 mm
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Table 10 Number of features selected at each PCA cutoff for far and near wall (auto) 6.2 mm LD threshold
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Table 11 Comparison of various tissue characterization techniques from literature against our proposed work. (ACC = Accuracy; Sn: Sensitivity; Sp: Specificity)
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Appendix 2: Grayscale features

Table 12 Features of gray level co-occurrence matrix (GLCM)
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Table 13 Features of gray level run length matrix
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Saba, L., Jain, P.K., Suri, H.S. et al. Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm. J Med Syst 41, 98 (2017). https://doi.org/10.1007/s10916-017-0745-0

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  • DOI: https://doi.org/10.1007/s10916-017-0745-0

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