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Efficient Myocardial Segmentation using Local Phase Quantization (LPQ) and Automatic Segmentation Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 151 - Number 10 |
| Year of Publication: 2016 |
| Authors: Gayathri A., R. Kavitha |
10.5120/ijca2016911917
|
Gayathri A., R. Kavitha . Efficient Myocardial Segmentation using Local Phase Quantization (LPQ) and Automatic Segmentation Technique. International Journal of Computer Applications. 151, 10 ( Oct 2016), 12-17. DOI=10.5120/ijca2016911917
Abstract
The low and high arrhythmic risk of myocardial infarction is classified based on size, location, and textural information of scarred myocardium. These features are extracted from late gadolinium (LG) enhanced cardiac magnetic resonance images (MRI) of post-MI patients. The risk level caused by features are evaluated by using various classifiers including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest classifier. Here, high risk patients are separated from low risk patients based on the decision made by Left Ventricular Ejection Fraction (LVEF) and biomarkers based on scar characteristics. However, additional image processing techniques are needed to have clear visibility for differentiating scar texture between two risk groups. In order to maintain balanced risk groups, synthetic minority over-sampling technique (SMOTE) is used in existing system. But accuracy is limited further because of imbalance risk groups and manual segmentation of classifier. So to improve accuracy, proposed method uses automatic segmentation and Local Phase Quantization (LPQ).
References
- Issac N.Bankman, "Hand Book of medical image processing and analysis", Elsevier Inc. , Isbn 978-0-12-373904-9, 2nd edition,2009.
- B. Chanda, D. D. Majumder, Digital Image Processing and Analysis , PHI Learning Private Limited, New Delhi, Isbn 978 -81-203 -4325-2, 2nd Edition, 2011.
- Palaniappan. S, Awang.R, “Intelligent heart disease prediction system using data mining techniques”. In Computer Systems and Applications, AICCSA,IEEE/ACS International Conference on(pp. 108-115), 2008.
- Ali, M. T., & Sabri, S. “Utility of cardiac MRI in the assessment of myocardial viability evaluating its role using 3-Tesla machine in correlation with SPECT”, 2016.
- Knaapen, P., De Haan, S., Hoekstra, O. S., Halbmeijer, R., Appelman, Y. E., Groothuis, J. G. J., & Götte, M. J. W, “Cardiac PET-CT: advanced hybrid imaging for the detection of coronary artery disease” Netherlands Heart Journal, 18(2), 90-98,2010.
- Jabbar, M. A., Chandra, P., & Deekshatulu, B. L, “Prediction of risk score for heart disease using associative classification and hybrid feature subset selection”, In ISDA (pp. 628-634), 2012.
- Alizadehsani, R., Habibi, J., Hosseini, M. J., Mashayekhi, H., Boghrati, R., Ghandeharioun & Sani, Z. A, “ A data mining approach for diagnosis of coronary artery disease” Computer methods and programs in biomedicine, 111(1), 52-61, 2013.
- Grbic, S., Ionasec, R., Mansi, T., Georgescu, B., Vega-Higuera, F., Navab, N., & Comaniciu, D, “Advanced intervention planning for Transcatheter Aortic Valve Implantations (TAVI) from CT using volumetric models”, IEEE 10th International Symposium on Biomedical Imaging (pp. 1424-1427), 2013.
- Jabbar, M. A., Deekshatulu, B. L., & Chandra, P, “Heart disease prediction using lazy associative classification”, In Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), International Multi-Conference on (pp. 40-46). IEEE, 2013.
- Sonawane, J. S., & Patil, D. R, “Prediction of heart disease using learning vector quantization algorithm”, In IT in Business, Industry and Government (CSIBIG), (pp. 1-5). IEEE, 2014.
- Ilayaraja, M., & Meyyappan, T, “Efficient Data Mining Method to Predict the Risk of Heart Diseases Through Frequent Itemsets”, Procedia Computer Science, 70, 586-592,2015.
- Jonnagaddala, J., Liaw, S. T., Ray, P., Kumar, M., Chang, N. W., & Dai, H. J, “Coronary artery disease risk assessment from unstructured electronic health records using text mining”, Journal of biomedical informatics, 58, S203-S210, 2015.
- Lakshmi, K. P., & Reddy, C. R. K, “Fast rule-based heart disease prediction using associative classification mining”, In Computer, Communication and Control (IC4), International Conference on (pp. 1-5),IEEE,2015.
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