Discriminative Joint Context for Automatic Landmark Set Detection from a Single Cardiac MR Long Axis Slice

Cardiac magnetic resonance (MR) imaging has advanced to become a powerful diagnostic tool in clinical practice. Automatic detection of anatomic landmarks from MR images is important for structural and functional analysis of the heart. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, context, around the target. Conventional context is associated with each individual landmark to encode local shape and appearance evidence. We extend this concept to a landmark set, where multiple landmarks have connections at the semantic level, e.g., landmarks belonging to the same anatomy. We propose a joint context approach to construct contextual regions between landmarks. A discriminative model is learned to utilize inter-landmark features for landmark set detection as an entirety. This helps resolve ambiguities of individual landmark detection results. A probabilistic boosting tree is used to learn a dis...

• Type: Journal
• Year: 2009
• Where: FIMH
• Authors: Xiaoguang Lu, Bogdan Georgescu, Arne Littmann, Edgar Mueller, Dorin Comaniciu
• Keywords: Detection, FIMH 2009, Landmark, Landmark Detection, Medical Imaging