Automated detection of major thoracic structures with a novel online learning method

This paper presents a novel on-line learning method for automatically detecting anatomic structures in medical images. Conventional off-line learning requires collecting all representative samples before the commencement of training. Our presented approach eliminates the need for storing historical training samples and is capable of continuously enhancing its performance with new samples. We evaluate our approach with three distinct thoracic structures, demonstrating that our approach yields competing performance to the off-line approach. This demonstrated performance is attributed to our novel on-line learning structure coupled with histogram as weaker learner.