Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study. (1st July 2015)

• Record Type: Journal Article
• Authors: Vidya, K. Sudarshan; Ng, E.Y.K; Acharya, U. Rajendra; Chou, Siaw Meng; Tan, Ru San; Ghista, Dhanjoo N.
• Is Part Of: Computers in biology and medicine. Volume 62(2015)
• Page Start: 86
• Page End: 93
• Publication Date: 2015-07-01
• Subjects: Ultrasound image -- Myocardial Infarction -- Texture -- DWT -- GLCM -- HOS; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.03.033
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: Classification of normal and MI subjects using ultrasound images. Features are extracted using DWT, GLCM and HOS methods. Ranked features are subjected to SVM classifier. Achieved classification accuracy of 99.5% using the DWT method.