Assessment of Regional Myocardial Function via Statistical Features in MR Images

  • Mariam Afshin
  • Ismail Ben Ayed
  • Kumaradevan Punithakumar
  • Max W. K. Law
  • Ali Islam
  • Aashish Goela
  • Ian Ross
  • Terry Peters
  • Shuo Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Early and accurate detection of Left Ventricle (LV) regional wall motion abnormalities significantly helps in the diagnosis and follow-up of cardiovascular diseases. We present a regional myocardial abnormality detection framework based on image statistics. The proposed framework requires a minimal user interaction, only to specify initial delineation and anatomical landmarks on the first frame. Then, approximations of regional myocardial segments in subsequent frames were systematically obtained by superimposing the initial delineation on the rest of the frames. The proposed method exploits the Bhattacharyya coefficient to measure the similarity between the image distribution within each segment approximation and the distribution of the corresponding user-provided segment. Linear Discriminate Analysis (LDA) is applied to find the optimal direction along which the projected features are the most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed method can be used as a promising diagnostic support tool to assist clinicians.

Keywords

Linear Discriminant Analysis Decision Boundary Regional Segment Regional Wall Motion Abnormality Linear Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mariam Afshin
    • 1
    • 3
  • Ismail Ben Ayed
    • 2
  • Kumaradevan Punithakumar
    • 2
  • Max W. K. Law
    • 1
    • 2
  • Ali Islam
    • 4
  • Aashish Goela
    • 5
  • Ian Ross
    • 5
  • Terry Peters
    • 1
    • 3
  • Shuo Li
    • 1
    • 2
  1. 1.University of Western OntarioLondonCanada
  2. 2.GE HealthcareLondonCanada
  3. 3.Robarts Research InstituteLondonCanada
  4. 4.St. Joseph’s Health CareLondonCanada
  5. 5.London Health Science CenterLondonCanada

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