title: An integrated index for automated detection of infarcted myocardium from cross-sectional echocardiograms using texton-based features (Part 1).
abstract: Cross-sectional view echocardiography is an efficient non-invasive diagnostic tool for characterizing Myocardial Infarction (MI) and stages of expansion leading to heart failure. An automated computer-aided technique of cross-sectional echocardiography feature assessment can aid clinicians in early and more reliable detection of MI patients before subsequent catastrophic post-MI medical conditions. Therefore, this paper proposes a novel Myocardial Infarction Index (MII) to discriminate infarcted and normal myocardium using features extracted from apical cross-sectional views of echocardiograms. The cross-sectional view of normal and MI echocardiography images are represented as textons using Maximum Responses (MR8) filter banks. Fractal Dimension (FD), Higher-Order Statistics (HOS), Hu's moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyi's Entropy (REnt), Shannon's Entropy (ShEnt), and Kapur's Entropy (KEnt) features are extracted from textons. These features are ranked using t-test and fuzzy Max-Relevancy and Min-Redundancy (mRMR) ranking methods. Then, combinations of highly ranked features are used in the formulation and development of an integrated MII. This calculated novel MII is used to accurately and quickly detect infarcted myocardium by using one numerical value. Also, the highly ranked features are subjected to classification using different classifiers for the characterization of normal and MI LV ultrasound images using a minimum number of features. Our current technique is able to characterize MI with an average accuracy of 94.37%, sensitivity of 91.25% and specificity of 97.50% with 8 apical four chambers view features extracted from only single frame per patient making this a more reliable and accurate classification.
authors.affiliation: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore. Electronic address: vidya.2kus@gmail.com.
authors.forename: Vidya K
authors.initials: VK
authors.lastname: Sudarshan
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.affiliation: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
authors.forename: E Y K
authors.initials: EY
authors.lastname: Ng
authors.affiliation: Department of Cardiology, National Heart Centre, Singapore.
authors.forename: Ru San
authors.initials: RS
authors.lastname: Tan
authors.affiliation: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
authors.forename: Siaw Meng
authors.initials: SM
authors.lastname: Chou
authors.affiliation: University 2020 Foundation, MA 01532, USA.
authors.forename: Dhanjoo N
authors.initials: DN
authors.lastname: Ghista
mesh_terms: D003430:Cross-Sectional Studies; D004452:Echocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D009203:Myocardial Infarction / Q000000981:diagnostic imaging*; D009206:Myocardium
keywords: Echocardiography; Energy; Entropy; Myocardial infarction; Texton