Evaluation of a nonrigid motion compensation technique based on spatiotemporal features for small lesion detection in breast MRI

Authors:
F. Steinbruecker;A. Meyer-Baese;T. Schlossbauer;D. Cremers
Affiliations:
Department of Computer Science, Technical University of Munich, Garching, Germany;Department of Scientific Computing, Florida State University, Tallahassee, FL;Institute for Clinical Radiology, University of Munich, Munich, Germany;Department of Computer Science, Technical University of Munich, Garching, Germany
Venue:
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Year:
2012

Citing 3
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Highly Accurate Optic Flow Computation with Theoretically Justified Warping

International Journal of Computer Vision
Geometric moment invariants

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Adaptive Grid Generation Based Non-rigid Image Registration using Mutual Information for Breast MRI

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The classification of valid and invalid beats of three-dimensional nystagmus eye movement signals using machine learning methods

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Abstract

Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.