The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Finding Curvilinear Structures in Mammograms
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Classification of linear structures in mammograms using random forests
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
How many trees in a random forest?
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A supervised learning based approach to detect crohn's disease in abdominal MR volumes
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
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Detecting and classifying curvilinear structure is important in many image interpretation tasks. We focus on the challenging problem of detecting such structure in mammograms and deciding whether it is normal or abnormal. We adopt a discriminative learning approach based on a Dual-Tree Complex Wavelet representation and random forest classification. We present results of a quantitative comparison of our approach with three leading methods from the literature and with learning-based variants of those methods. We show that our new approach gives significantly better results than any of the other methods, achieving an area under the ROC curve Az = 0.923 for curvilinear structure detection, and Az = 0.761 for distinguishing between normal and abnormal structure (spicules). A detailed analysis suggests that some of the improvement is due to discriminative learning, and some due to the DT-CWT representation, which provides local phase information and good angular resolution.