



@book {2224,
	title = {Automatic and semi-automatic analysis of the extension of myocardial infarction in an experimental murine model},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {6669 LNCS},
	year = {2011},
	note = {Conference code: 85429Cited By (since 1996): 1Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-21257-4_19Language of Original Document: EnglishCorrespondence Address: Esteves, T.; Departamento de Engenharia Electrot{\'e}cnica e de Computadores, Faculdade de Engenharia, Universidade Do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; email: meb09026@fe.up.ptReferences: Degabriele, N., Griesenbach, U., Sato, K., Critical appraisal of the mouse model of myocardial infarction (2004) Experimental Physiology, 89 (4), pp. 497-505;Takagawa, J., Zhang, Y., Wong, M., Myocardial infarct size measurement in the mouse chronic infarction model: Comparison of area- and length-based approaches (2007) Journal of Applied Physiology, 102, pp. 2104-2111; 
Alattar, M., Osman, N., Fahmy, A., Myocardial segmentation using constrained multi-seeded region growing (2010) LNCS, 6112, pp. 89-98. , Campilho, A., Kamel, M. (eds.) ICIAR 2010 Springer, Heidelberg; 
Wu, Q., Merchant, F., Castleman, K., (1996) Microscope Image Processing, , ch. 7. Elsevier, Amsterdam; 
Gonzalez, R., Woods, R., Eddins, S., (2004) Digital Image Processing Using MATLAB, , ch. 9. Pearson Education, London; 
Sharma, N., Aggarwal, L., Automated medical image segmentation techniques (2010) Journal of Medical Physics, 35 (1), pp. 3-14; 
Pham, D., Xu, C., A survey of current methods in medical image segmentation (1998) Annual Review of Biomedical Engineering, 2, pp. 315-338; 
Hamarneh, G., Li, X., Watershed segmentation using prior shape and appearance knowledge (2009) Image and Vision Comp, 27 (1-2), pp. 59-68; 
Khadir, S., Ahamed, R., Moving toward region-based image segmentation techniques: A study (2009) Journal of Theoretical and Applied Information Technology, 5 (1), pp. 1-7; 
Ahmed, M., Mohamad, D., Segmentation of brain mr images for tumor extraction by combining kmeans clustering and perona-malik anisotropic diffusion model (2010) International Journal of Image Processing, 2 (1), pp. 1-8; 
Mayer, A., Greenspan, H., An adaptive mean-shift framework for MRI brain segmentation (2009) IEEE Trans. on Medical Imaging, 28 (8), pp. 1-12; 
Dabov, K., Foi, A., Katkovnik, V., Image denoising by sparse 3d transform-domain collaborative filtering (2007) IEEE Trans. on Image Processing, 16 (8), pp. 1-16; 
Otsu, N., A threshold selection method from gray level histograms (1979) IEEE Trans. Systems, Man and Cybernetics, 9 (1), pp. 62-66; 
Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A., Automated arabidopsis plant root cell segmentation based on svm classification and region merging (2009) Computers in Biology and Medicine, 39 (9), pp. 1-9; 
Bleau, A., Leon, J., Watershed-based segmentation and region merging (2000) Computer Vision and Image Understanding, 77 (3), pp. 317-370
},
	month = {2011///},
	pages = {151 - 158},
	address = {Las Palmas de Gran Canaria},
	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. {\textcopyright} 2011 Springer-Verlag.},
	keywords = {Extension evaluations, Image analysis, Image segmentation, Infarct extension evaluation, k-means, Mammals, meanshift, otsu, Pattern recognition, region growing, Tissue, watershed},
	isbn = {03029743 (ISSN); 9783642212567 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79959927807\&partnerID=40\&md5=360bb21a8c1c3a5fdd394c647fcadbbc},
	author = {Esteves, T. and Valente, M. and Nascimento, D.S. and Pinto-Do-{\'O}, P. and Quelhas, P.}
}
