Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study

K Sudarshan Vidya; E Y K Ng; U Rajendra Acharya; Siaw Meng Chou; Ru San Tan; Dhanjoo N Ghista

doi:10.1016/j.compbiomed.2015.03.033

Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study

Comput Biol Med. 2015 Jul:62:86-93. doi: 10.1016/j.compbiomed.2015.03.033. Epub 2015 Apr 10.

Authors

K Sudarshan Vidya¹, E Y K Ng², U Rajendra Acharya³, Siaw Meng Chou², Ru San Tan⁴, Dhanjoo N Ghista⁵

Affiliations

¹ 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.
² School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
³ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore.
⁴ Department of Cardiology, National Heart Centre, Singapore.
⁵ University 2020 Foundation, MA 01532, USA.

PMID: 25912990
DOI: 10.1016/j.compbiomed.2015.03.033

Abstract

Myocardial Infarction (MI) or acute MI (AMI) is one of the leading causes of death worldwide. Precise and timely identification of MI and extent of muscle damage helps in early treatment and reduction in the time taken for further tests. MI diagnosis using 2D echocardiography is prone to inter-/intra-observer variability in the assessment. Therefore, a computerised scheme based on image processing and artificial intelligent techniques can reduce the workload of clinicians and improve the diagnosis accuracy. A Computer-Aided Diagnosis (CAD) of infarcted and normal ultrasound images will be useful for clinicians. In this study, the performance of CAD approach using Discrete Wavelet Transform (DWT), second order statistics calculated from Gray-Level Co-Occurrence Matrix (GLCM) and Higher-Order Spectra (HOS) texture descriptors are compared. The proposed system is validated using 400 MI and 400 normal ultrasound images, obtained from 80 patients with MI and 80 normal subjects. The extracted features are ranked based on t-value and fed to the Support Vector Machine (SVM) classifier to obtain the best performance using minimum number of features. The features extracted from DWT coefficients obtained an accuracy of 99.5%, sensitivity of 99.75% and specificity of 99.25%; GLCM have achieved an accuracy of 85.75%, sensitivity of 90.25% and specificity of 81.25%; and HOS obtained an accuracy of 93.0%, sensitivity of 94.75% and specificity of 91.25%. Among the three techniques presented DWT yielded the highest classification accuracy. Thus, the proposed CAD approach may be used as a complementary tool to assist cardiologists in making a more accurate diagnosis for the presence of MI.

Keywords: DWT; GLCM; HOS; Myocardial Infarction; Texture; Ultrasound image.

MeSH terms

Diagnosis, Computer-Assisted / methods*
Echocardiography, Doppler / methods*
Female
Humans
Male
Myocardial Infarction / diagnostic imaging*
Signal Processing, Computer-Assisted*
Support Vector Machine*