Region detection in medical images using HOG classifiers and a body landmark network

Marius Erdt; Oliver Knapp; Klaus Drechsler; Stefan Wesarg

doi:10.1117/12.2007384

28 February 2013 Region detection in medical images using HOG classifiers and a body landmark network

Marius Erdt, Oliver Knapp, Klaus Drechsler, Stefan Wesarg

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867004 (2013) https://doi.org/10.1117/12.2007384
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Automatic detection of anatomical structures and regions in 3D medical images is important for several computer aided diagnosis tasks. In this work, a new method for simultaneous detection of multiple anatomical areas is proposed. The method consists of two steps: first, single rectangular region candidates are detected independently using 3D variants of Histograms of Oriented Gradients (HOG) features. These features are robust against small changes between regions in rotation and scale which typically occur between different individuals. In a second step, the positions of the detected candidates are refined by incorporating a body landmark network that exploits anatomical relations between different structures. The landmark network consists of a principle component based statistical modeling of the relative positions between the detected regions in training images. The method has been evaluated on thoracic/abdominal CT images of the portal venous phase. In 216 CT images, eight different structures have been trained. Results show an increase in performance using the combination of HOGs and the landmark network in comparison to using independent classifiers without anatomical relations.

Citation Download Citation

Marius Erdt, Oliver Knapp, Klaus Drechsler, and Stefan Wesarg "Region detection in medical images using HOG classifiers and a body landmark network", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867004 (28 February 2013); https://doi.org/10.1117/12.2007384

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