Learning-Based Vessel Segmentation in Mammographic Images
Erkang Cheng, Shawn McLaughlin, Vasileios Megalooikonomou, Predrag R. Bakić, Andrew D. A. Maidment, Haibin Ling · 2011
In this paper we propose using a learning-based method for vessel segmentation in mammographic images. To capture the large variation in vessel patterns not only across subjects, but also within a subject, we create a feature pool containing local, Gabor and Haar features extracted from mammographic images generating a feature space of very high dimension. We also employ a huge number of training samples, which essentially contains the pixels in the training images. To deal with the very high dimensional feature space and the huge number of training samples, we apply a forest with boosting trees for vessel segmentation. Specifically, we use the standard AdaBoost algorithm for each tree in the forest. The randomness is encoded, when training each AdaBoost tree, using randomly sampled training set (pixels) and randomly selected features from the whole feature pool. The proposed method is tested using a real dataset with 20 anonymous mammographic images. The effectiveness of the proposed features and classifiers is demonstrated in the experiments where we compare different approaches and feature combinations. In the paper, we also present full analysis of different types of features.