Learning-Based Object Detection in Cardiac MR Images

An automated method for left ventricle detection in MR cardiac images is presented. Ventricle detection is the #rst step in a fully automatedsegmentation system usedtocompute volumetric information about the heart. Our methodisbasedonlearning the gray level appearance of the ventricle by maximizing the discrimination between positive and negative examples in a training set. The main di#erences from previously reported methods arefeature de#nition and solution to the optimization problem involved in the learning process. Our method was trained on a set of 1,350 MR cardiac images from which 101,250 positive examples and 123,096 negative examples were generated. The detection results on a test set of 887 di#erent images demonstrate an excellent performance: 98# detection rate, a false alarm rate of 0:05# of the number of windows analyzed #10 false alarms per image# and a detection time of 2 seconds per 256 # 256 image on a Sun Ultra 10 for an 8-scale search. The false alarms are eventually eliminatedby aposition#scale consistency check along all the images that represent the same anatomical slice. I