Abstract
Rodent models of myocardial infarction (MI) have been extensively used in biomedical research towards the implementation of novel regenerative therapies. Permanent ligation of the left anterior descending (LAD) coronary artery is a commonly used method for inducing MI both in rat and mouse. Post-mortem evaluation of the heart, particularly the MI extension assessment performed on histological sections, is a critical parameter for this experimental setting. MI extension, which is defined as the percentage of the left ventricle affected by the coronary occlusion, has to be estimated by identifying the infarcted- and the normal-tissue in each section. However, because it is a manual procedure it is time-consuming, arduous and prone to bias. Herein, we introduce semi-automatic and automatic approaches to perform segmentation which is then used to obtain the infarct extension measurement. Experimental validation is performed comparing the proposed approaches with manual annotation and a total error not exceeding 8% is reported in all cases.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Degabriele, N., Griesenbach, U., Sato, K.: Critical appraisal of the mouse model of myocardial infarction. Experimental Physiology 89(4), 497–505 (2004)
Takagawa, J., Zhang, Y., Wong, M.: Myocardial infarct size measurement in the mouse chronic infarction model: comparison of area- and length-based approaches. Journal of Applied Physiology 102, 2104–2111 (2007)
Alattar, M., Osman, N., Fahmy, A.: Myocardial segmentation using constrained multi-seeded region growing. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6112, pp. 89–98. Springer, Heidelberg (2010)
Wu, Q., Merchant, F., Castleman, K.: Microscope Image Processing, ch. 7. Elsevier, Amsterdam (1996)
Gonzalez, R., Woods, R., Eddins, S.: Digital Image Processing Using MATLAB, ch. 9. Pearson Education, London (2004)
Sharma, N., Aggarwal, L.: Automated medical image segmentation techniques. Journal of Medical Physics 35(1), 3–14 (2010)
Pham, D., Xu, C.: A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–338 (1998)
Hamarneh, G., Li, X.: Watershed segmentation using prior shape and appearance knowledge. Image and Vision Comp. 27(1-2), 59–68 (2009)
Khadir, S., Ahamed, R.: Moving toward region-based image segmentation techniques: A study. Journal of Theoretical and Applied Information Technology 5(1), 1–7 (2009)
Ahmed, M., Mohamad, D.: Segmentation of brain mr images for tumor extraction by combining kmeans clustering and perona-malik anisotropic diffusion model. International Journal of Image Processing 2(1), 1–8 (2010)
Mayer, A., Greenspan, H.: An adaptive mean-shift framework for MRI brain segmentation. IEEE Trans. on Medical Imaging 28(8), 1–12 (2009)
Dabov, K., Foi, A., Katkovnik, V.: Image denoising by sparse 3d transform-domain collaborative filtering. IEEE Trans. on Image Processing 16(8), 1–16 (2007)
Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Systems, Man and Cybernetics 9(1), 62–66 (1979)
Marcuzzo, M., Quelhas, P., Campilho, A., Mendonça, A.M., Campilho, A.: Automated arabidopsis plant root cell segmentation based on svm classification and region merging. Computers in Biology and Medicine 39(9), 1–9 (2009)
Bleau, A., Leon, J.: Watershed-based segmentation and region merging. Computer Vision and Image Understanding 77(3), 317–370 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Esteves, T., Valente, M., Nascimento, D.S., Pinto-do-Ó, P., Quelhas, P. (2011). Automatic and Semi-automatic Analysis of the Extension of Myocardial Infarction in an Experimental Murine Model. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)