Segmentation of abdominal organs from magnetic resonance data sets is an essential task for several medical procedures and analysis. When this process is done manually, it requires an expert radiologist's time and experience. Classification of organs by using several features for segmentation is a known technique. In recent years, for different application fields it is shown that hierarchical classification systems are more successful than composite feature-single classifier systems. The success of hierarchical systems in medical image segmentation is directly related to the reduction of false positive error in every level of the hierachy. In this paper, 3 different algorithms of Adaboost (Adaptive Boosting) method: Real, gentle and modest are compared in terms of their false positive error performance. Performance metrics are obtained for each organ and the dependence of false positive error on the selected algorithm is examined.