Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study
Introduction
Myocardial Infarction (MI), the most common complication of coronary heart disease (CHD) affect millions of people worldwide, is an irreversible damage of the heart muscles caused by partial or complete blockage in the coronary artery. The clinical presentation and outcome depend on the location of the blockages, severity and duration of MI. MI progresses with time, highlighting the need for continual monitoring for recognition of underlying or forthcoming left ventricular (LV) remodelling and related complications [1]. The most important issue in the management of MI patients is to detect myocardial viability for reperfusion treatment, and the degree and type of LV remodelling [51]. Early reperfusion treatment and quantification of LV remodelling (e.g. using LV shape descriptors) of MI patients potentially improves LV function, survival and reverse of LV remodelling [51]. Therefore, (i) identification of myocardial viability and detection of myocardial damage immediately after MI; (ii) detection of infarct expansion and the extent of decline in LV function are early requirements for optimal treatment and management.
After an acute phase of MI, cardiac imaging can provide a wealth of valuable details for patient management. Given the variety of imaging options, the final selection would depend on the availability, cost and risks involved. Two dimensional (2D) echocardiography possesses several advantages – such as portability, low cost, widespread availability, no need for radiation and contrast agents. The clinical choice therefore differs depending on resources, patient characteristics and, individual experience of the physician.
An echocardiogram obtained as early as possible in the acute phase of MI helps to exclude acute mechanical complications by regional and global LV function assessment. In case of emergency such as when patients experience sudden deterioration, hypotension, acute heart failure or a new murmur, urgent echocardiography is mandatory [22].
Fig. 1 shows the typical 2D echocardiography images of normal and MI patients in the parasternal short axis mid-left ventricular view captured during diastole and systole. Not only does the 2D echocardiography allow direct and adequate visualisation of real time regional wall motion abnormalities during chronic and acute phases of MI, it also allows accurate localisation of infarcted areas. In addition, the wall motion score index (WMSI) evaluated by 2D echocardiography, an index of LV function, is a good indicator of prognosis in-hospital and after the discharge [45,29].
The evaluation of the echocardiography image features by the echo technologists or cardiologists is important to diagnose MI, infarct expansion, extent of muscle damage and LV remodelling [37]. The accurate visual interpretation of the image features requires experience and training. In addition, the interpretation is always vulnerable to intra- and inter-observer variability [17]. And also the echocardiography images are limited by the low resolution and artefacts which hinders the visual identification of subtle changes. Therefore, computer-aided diagnosis (CAD) approach could be useful in order to provide the cardiologists an objective interpretation of echocardiogram provided that a proper model and implementation are found. The importance of cardiac disease has inspired the implementation of state-of-the-art of clinical imaging methods and signal analysis to assist in diagnosis and clinical planning [47]. CAD is an emerging concept developed by combining the knowledge of medicine and image processing in clinical settings. To understand the philosophy of CAD, it is necessary to follow a particular protocol. A typical protocol for a CAD system consists of different stages, namely (i) image acquisition and pre-processing, (ii) segmentation/region-of-interest (ROI) selection, (iii) image feature extraction and (iv) classification. Previous researchers, Fujita et al. [24]) and Ginneken et al. [26]), have reviewed and summarised the latest development and application of CAD system. The advanced image processing techniques (e.g. DWT, first and second-order statistics and HOS) based CAD approaches in ultrasound images [3], [10], [50], [28], [12], including detection of abnormalities, e.g. atherosclerosis, LV dysfunction, coronary plaque and MI [36], [5], [2], [4], [12] may aid doctors to expedite screening large populations of abnormalities and to facilitate for proper treatment.
Few first-order statistics, such as intensity, skewness, kurtosis and entropy are calculated from the texture, and then used to classify normal and MI subjects [49], [38], [34]. The features extracted from the sub bands of DWT are used for the diagnosis of MI using ultrasound images [39], [40]. These features extracted using first/second-order statistics and DWT texture descriptors are able to identify the minute changes of patterns in the echocardiography images. The HOS is a nonlinear method which [14] can capture interaction among its frequency components and phase coupling. Hence, in this work the performance of texture descriptors DWT, second-order statistics computed from the GLCM and HOS in identification of MI using echocardiography images are evaluated.
Over the years, investigators developed various types of CAD systems for identification of lesions and distinctive diagnosis of detected lesions based on classification between malignant and benign lesions [31], [20]. Among them, few CAD systems for identification of lesions like breast lesions on mammograms are successfully applied in clinical situations [33], [25]. To date, no significant efforts have been made to employ CAD approaches for diagnosis of MI or other cardiac abnormalities using ultrasound images. The application of CAD concept in echocardiography for the detection of MI is a new field that has not been studied adequately and is still in its infancy for potential full application. Hence, there is a need to explore the application of CAD scheme on echocardiogram images for the detection of MI. In our previous review paper [50], we have comprehensively discussed the various echocardiography image analysis methods used for the automated identification of MIs.
In this research paper, performance of CAD of MI using echocardiography images with DWT, GLCM and HOS methods are evaluated. These three texture descriptors are used to extract important echocardiography image features for developing an automatic detection of MI.
The proposed system is shown in Fig. 2. In this system, normal and MI echocardiography images are subjected to pre-processing using adaptive histogram equalisation. Following steps are carried out on the pre-processed echocardiography images:
- (a)
Features are extracted;
- i.
on the DWT coefficients,
- ii.
based on second-order statistics computed from GLCM, and
- iii.
from bispectrum coefficients of HOS.
- i.
- (b)
These three set of extracted features are ranked separately using t-value.
- (c)
The highly ranked features from each method are fed to the classifier support vector machine (SVM) one by one separately, to get the highest classification accuracy using minimum number of features.
Section snippets
Image acquisition and pre-processing
The echocardiogram image data required for the study were collected from National Heart Centre, Singapore using ultrasound scanner (ProSound α 10, ALOKA Hitachi, Japan). For this work, a total of 160 subjects (80 MI and 80 non-MI) were enroled. Echocardiography video sequences captured in the parasternal short axis mid-left ventricular view were analysed. The age of the subjects (both male and female) ranges from 21 to 75 years. Among MI subjects, the echocardiography data used are from the
Feature extraction
The feature extraction from textural image is one of the important steps in an automated CAD system. The image features can be extracted using texture analysis [35], [46] and other methods. In this present work, three texture descriptors (DWT, GLCM and HOS) are used to analyse the echocardiography image features. Brief descriptions of those methods are explained in the following sections.
Results
In this current work, DWT, GLCM and HOS are used to extract features from echocardiography images to identify the differentiation between normal and MI cases. Our experiment findings are discussed in the following sections.
Discussion
In this present work, the performance of DWT, GLCM and HOS in CAD of MI using echocardiography images are compared. The results obtained clearly show that these three methods are able to pick up minute changes in the echocardiography images effectively and hence yielded high accuracy. DWT helps to quantify sudden changes in the pixels and hence the entropy value is lower for MI compared to normal class. This indicates that the high frequency components are less in MI cases. Entropy computed
Conclusion
Accurate and early identification of MI using echocardiography images is the focus of cardiologists for early treatment and prevention of post-MI complications and death. Current visual identification of echocardiography features and manual interpretation of those features is time-consuming, labour intensive and prone to inter-observer variability. Therefore, computer-aided approach may help in automatic and accurate identification of MI.
In this paper, the performance of the CAD approach based
Conflict of interest
There is no conflict of interest in this work.
Aknowledgement
Authors thank Mr Lim Wei Jie Eugene and JW Koh for running the codes and compiling the results.
References (51)
- et al.
Understanding symptomatology of atherosclerotic plaque by image-based tissue characterisation
Comput. Methods Programs Biomed.
(2013) - et al.
Atherosclerotic risk stratification strategy for carotid arteries using texture-based features
Ultrasound Med. Biol.
(2012) - et al.
Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms
Acad. Radiol.
(2005) - et al.
Application of higher order statistics/spectra in biomedical signals—a review
Med. Eng. Phys.
(2010) - et al.
Value and limitations of two-dimensional echocardiography in predicting myocardial infarct size
Am. J. Cardiol.
(1991) - et al.
Detection of acute myocardial infarction with digital image processing of two dimensional echocardiograms
Am. Heart J.
(1992) Guidelines for the management of patients with acute myocardial infarction: executive summary: a report of the American College of Cardiology/American Association Task Force on practice guidelines 9Committee on management of acute myocardial infarction)
Circulation
(1996)- et al.
Plaque tissue characterisation and classification in ultrasound carotid scans: a paradigm for vascular feature amalgamation
IEEE Trans. Instrum. Meas.
(2013) - U.R. Acharya, O. Faust, S. Vinithasree, A.P.C. Alvin, G. Krishnamurthy, J.C.R. Seabra, J. Sanches, J.S. Suri...
- et al.
Application of higher order spectra for the identification of diabetes retinopathy stages
J. Med. Syst.
(2008)
GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization
Technol. Cancer Res. Treat.
Ovarian tumor characterisation and classification using ultrasound – a new online paragdigm
J. Digit. Imaging
An introduction to wavelets
IEEE Comput. Sci. Eng.
Application of texture analysis in echocardiography for myocardial infarction tissue
J. Teknol.
Guinness, gosset, fisher, and small samples
Stat. Sci.
Pattern recognition using invariants defined from higher order spectra—1-D inputs
IEEE Trans. Signal Process.
Cardiac health diagnosis using higher order spectra and support vector machine
Open Med. Inform. J.
Cardiac state diagnosis using higher order spectra of heart rate variability
J. Med. Eng. .Technol.
Effects of inter- and intra-observer variability on echocardiographic measurements in awake cats
J. Vet. Med.
Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers
AJR Am. J. Roentgenol.
Cardiac imaging after myocardial infarction
Eur. Heart J.
Introduction to Statistical Pattern Recognition
Cited by (67)
Early myocardial infarction detection over multi-view echocardiography
2024, Biomedical Signal Processing and ControlEndoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review
2023, Biocybernetics and Biomedical EngineeringMedical image analysis
2022, Deep Learning for Robot Perception and CognitionAutomated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features
2021, Computers in Biology and MedicineAnalysis of activation maps through global pooling measurements for texture classification
2021, Information Sciences