The cardiac magnetic resonance (CMR) images from a group of patients with myocardial scars and implanted cardioverter-defibrillator (ICD) are divided into a group with low risk of arrhythmias (late incidents) and a group with high risk of arrhythmias (early incidents). Several hundred quantitative features describing sizes, statistics and textures of the segmented and defined areas of the images are computed from manually segmented images in an exploratory analysis. The method used to determine decision regions to discriminate the patients with low risk of arrhythmias from the patient with high risk of arrhythmias is a maximum likelihood estimation based Bayes classifiers described in [1]. The results presented can be interpreted as hypothesis of which features, and combinations of features, that might have discriminative power. A major hypothesis that arises is that there are important textural information in the scarred and non-scarred areas.