Abstract
Cardiac magnetic resonance (CMR) imaging is an important technique for cardiac diagnosis. Measuring the scar in myocardium is important to cardiologists to assess the viability of the heart. Delayed enhancement (DE) images are acquired after about 10 minutes following injecting the patient with contrast agent so the infracted region appears brighter than its surroundings. A common method to segment the infarction from DE images is based on intensity Thresholding. This technique performed poorly for detecting small infarcts in noisy images. In this work we aim to identify the best threshold value to segment the infarction in case of segmentation using simple Threshold and propose a modified technique to improve the segmentation in noisy images. Our proposed technique is based on enhancing Thresholding using k-means clustering. We test our proposed model using computer simulated and real images with different contrast-to-noise ratio (CNR). We used F-score, which is a combined measure of the precision and sensitivity, to determine the performance of the proposed technique versus simple Thresholding. The results show that the proposed technique outperforms existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pham, D.L., Xu, C., Prince, J.L.: Current Methods in Medical Image Segmentation. Annual Reviews 2, 315–338 (2000)
Hsu, L.-Y., Natanzon, A., Kellman, P., Hirsch, G.A., Aletras, A.H., Arai, A.E.: Quantitative Myocardial Infarction on Delayed Enhancement MRI. Part I: Animal Validation of an Automated Feature Analysis and Combined Thresholding Infarct Sizing Algorithm. J. Magn. Reson. Imaging 23(3), 298–308 (2006)
Kolipaka, A., Chatzimavroudis, G.P., White, R.D., ODonnell, T.P., Randolph, M.: Segmentation of non-viable myocardium in delayed enhancement magnetic resonance images. The International Journal of Cardiovascular Imaging (formerly Cardiac Imaging) 21, 303–311 (2005)
Doublier, C., Couprie, M., Garot, J., Hamam, Y.: Computer assisted segmentation, quantification and visualization of an infarcted myocardium from MRI images. Biomedsim. 3, 151–156 (2003)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, pp. 526–528. Wiley, New York (1973)
Hood, M.: Overview of delayed myocardial enhancement in cardiac MRI (February 12, 2009), http://www.ismrm.org/smrt/09/mock_clinical_poster_Mo.pdf
Sijbers, J., Den, D.A.J., Van, D.D., Raman, E.: Estimation of signal and noise from Rician distributed data. In: Proc. Int. Conf. Signal Proc. and Comm., Spain, pp. 140–142 (1998)
Gonzalez, R.C., Wood, R.E.: Digital Image Processing, pp. 147–175. Prentice-Hall, Englewood Cliffs (2002)
Beitzel, S.M.: On Understanding And Classifying Web Queries, Chicago, Illinois (May 2006)
Zumel, N.: Statistics to English Translation, Part 1: Accuracy Measures (November 3, 2009), http://win-vector.com/dfiles/StatisticsToEnglishPart1_Accuracy.pdf
Marina, S., Nathalie, J., Stan, S.: Beyond Accuracy, F-score and ROC: a Family of Discriminant Measures for Performance Evaluation. In: Australian Conference on Artificial Intelligence (2006)
Sievers, B., Rehwald, W.G., Albert, T.S., Patel, M.R., Parker, M.A., Kim, R.J., Judd, R.M.: Respiratory Motion and Cardiac Arrhythmia Effects on Diagnostic Accuracy of Myocardial Delayed-enhanced MR Imaging in Canines. Radiology 247, 106–114 (2008)
Bernhard, K., Michael, F., Tobias, H., Uwe, H., Albertus, S., Claus, C., Stephan, M.: Assessment of myocardial viability using delayed enhancement magnetic resonance imaging at 3.0 tesla. Investigative Radiology 41, 661–667 (2006)
Thompson, M.: November 21 (2003), http://www.phys.cwru.edu/courses/p431/notes-2003/node123.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Metwally, M.K., El-Gayar, N., Osman, N.F. (2010). Improved Technique to Detect the Infarction in Delayed Enhancement Image Using K-Mean Method. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13775-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-13775-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13774-7
Online ISBN: 978-3-642-13775-4
eBook Packages: Computer ScienceComputer Science (R0)