BackgroundThe myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium.MethodsIn this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function.ResultsIn the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image.ConclusionThe probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardium).
The Late Gadolinium (LG) enhancement in Cardiac Magnetic Resonance (CMR) imaging is used to increase the intensity of scarred area in myocardium for thorough examination. Automatic segmentation of scar is important because scar size is largely responsible in changing the size, shape and functioning of left ventricle and it is a preliminary step required in exploring the information present in scar. We have proposed a new technique to segment scar (infarct region) from non-scarred myocardium using intensity-based texture analysis. Our new technique uses dictionary-based texture features and dc-values to segment scarred and non-scarred myocardium using Maximum Likelihood Estimator (MLE) based Bayes classification. Texture analysis aided with intensity values gives better segmentation of scar from myocardium with high sensitivity and specificity values in comparison to manual segmentation by expert cardiologists.
This paper presents a novel method for the identification of myocardial regions associated with increased risk of life threatening arrhythmia in patients with healed myocardial infarction assessed by late enhanced gadolinium magnetic resonance images. A probability mapping technique is used to create images where each pixel value corresponds to the probability of that pixel representing damaged myocardium. Cardiac segments are defined as the set of pixel positions associated with probability values between a lower and an upper threshold. From the corresponding pixels in the original images several features are calculated. The features studied here are the relative size and entropy values based on histograms with varying number of bins. Features calculated for a specific cardiac segment are compared between patients with high and low risk of arrhythmia. The results from comparing a large number of cardiac segments indicate that the entropy measure has a better localisation property compared to the relative size of the myocardial damage, and that the localisation is more focused for fewer number of bins in the entropy calculation.
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