In this paper, we present a technique for quantitative discrimination of Duchenne Muscular Dystrophy (DMD). Our ultrasound image data is generated with a novel force-controlled ultrasound acquisition system that allows precise ultrasound image acquisition at a predetermined force. We use the texture of ultrasound images, as calculated by the Canny edge detector, as the input image feature for our analysis algorithm. After statistically sieving through the edge detection parameters on our training set, we identify the set of parameters significant within a threshold. Decision trees are then trained on these significant parameters over a training dataset with cross-validation, and evaluated on accuracy, precision, specificity and sensitivity on a separate test dataset. We discuss the performance of our system, by muscle groups, on data collected with our device in a recent clinical study. Using depth of the image as a proxy for image regions, we evaluate the extent to which the performance of our system is robust to region-of-interest selection. Our method holds significant promise for automated assessment of Duchenne Muscular Dystrophy using force-controlled ultrasound image acquisition in a reliable and robust manner.