Siemens Aktiengesellschaft

Alexander Schwing

Dorin Comaniciu

Yefeng Zheng

Shaohua Kevin Zhou

Michael Kelm

Martin Harder

Alexey Tsymbal

Maria Jimena Costa

A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.

Image processing using random forest classifiers

A method and apparatus for detecting multiple anatomical landmarks in a 3D volume. A first anatomical landmark is detected in a 3D volume using marginal space learning (MSL). Locations of remaining anatomical landmarks are estimated in the 3D volume based on the detected first anatomical landmark using a learned geometric model relating the anatomical landmarks. Each of the remaining anatomical landmarks is then detected using MSL in a portion of the 3D volume constrained based on the estimated location of each remaining landmark. This method can be used to detect the anatomical landmarks of the crista galli (CG), tip of the occipital bone (OB), anterior of the corpus callosum (ACC), and posterior of the corpus callosum (PCC) in a brain magnetic resonance imaging (MRI) volume.

Method and system for anatomic landmark detection using constrained marginal space learning and geometric inference

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Zurich, Switzerland

Alexander Schwing