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Region Detection in Medical Images Using HOG Classifiers and a Body Landmark Network

Erdt, Marius and Knapp, Oliver and Drechsler, Klaus and Wesarg, Stefan (2013):
Region Detection in Medical Images Using HOG Classifiers and a Body Landmark Network.
SPIE Press, Bellingham, In: Medical Imaging 2013: Computer-Aided Diagnosis. Part One, In: Proceedings of SPIE; 8670, DOI: 10.1117/12.2007384,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2013
Creators: Erdt, Marius and Knapp, Oliver and Drechsler, Klaus and Wesarg, Stefan
Title: Region Detection in Medical Images Using HOG Classifiers and a Body Landmark Network
Language: English
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.

Series Name: Proceedings of SPIE; 8670
Publisher: SPIE Press, Bellingham
Uncontrolled Keywords: Region detection, Body landmarks
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Medical Imaging 2013: Computer-Aided Diagnosis. Part One
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1117/12.2007384
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