Learning to detect lesion boundaries in breast ultrasound images

This paper presents a novel method for automatic lesion detection in breast ultrasound images; the method performs multi-stage learning of lesion-specific boundaries represented by a bag of robust features. The proposed method can be seen as an edge pruning procedure that leaves only object-specific edges and filters out the rest. It can be combined with segmentation algorithms that rely on edge information. We show an example of such combination with one of the state-of-art segmentation algorithms; our method yields improved segmentation results. The proposed method is tested on a set of 400 breast ultrasound images, with the goal to automatically detect lesion boundaries. However, we believe that our method can be used by radiologists as an assistance tool during examination routine, in which case it may help to better localize lesions and document the findings. The performance of our method is compared to a state-of-art object boundary classification algorithm; we show that our method outperforms it in different tests.