In this paper, we propose a novel machine learning approach for interactive lesion segmentation on CT and MRI images.
Our approach consists of training process and segmenting process. In training process, we train AdaBoosted histogram
classifiers to classify true boundary positions and false ones on the 1-D intensity profiles of lesion regions. In segmenting
process, given a marker indicating a rough location of a lesion, the proposed solution segments its region automatically
by using the trained AdaBoosted histogram classifiers. If there are imperfects in the segmented result, based on one
correct location designated by the user, the solution does the segmentation again and gives a new satisfied result. There
are two novelties in our approach. The first is that we use AdaBoost in the training process to learn diverse intensity
distributions of lesion regions, and utilize the trained classifiers successfully in segmenting process. The second is that
we present a reliable and user-friendly way in segmenting process to rectify the segmented result interactively. Dynamic
programming is used to find a new optimal path. Experimental results show our approach can segment lesion regions
successfully, despite the diverse intensity distributions of the lesion regions, marker location variability and lesion region
shape variability. Our framework is also generic and can be applied for blob-like target segmentation with diverse
intensity distributions in other applications.