Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database guided detection of anatomical landmark points in 3D images of the heart
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Learning image context for segmentation of prostate in CT-guided radiotherapy
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Texture anisotropy in 3-D images
IEEE Transactions on Image Processing
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We propose a supervised learning approach for detecting landmarks in cardiac images from different views. A set of candidate landmark points are obtained using morphological operations and graph cut segmentation. The final landmarks are determined using random forests (RF) classifiers which were trained on low level features derived from the neighborhood of annotated landmarks on training images. We use features like intensity, texture, shape asymmetry and context information for landmark detection. Experimental results on the STACOM LV landmark detection challenge dataset show that our approaching is promising with room for further improvement.