Watershed-based segmentation and region merging
Computer Vision and Image Understanding
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Watershed segmentation using prior shape and appearance knowledge
Image and Vision Computing
Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging
Computers in Biology and Medicine
Myocardial segmentation using constrained multi-seeded region growing
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
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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.