Automated classification of LV regional wall motion based on spatio-temporal profiles from cardiac cine magnetic resonance imaging

Juan Mantilla; Mireille Garreau; Jean-Jacques Bellanger; José Luis Paredes

doi:10.1117/12.2035517

19 November 2013 Automated classification of LV regional wall motion based on spatio-temporal profiles from cardiac cine magnetic resonance imaging

Juan Mantilla, Mireille Garreau, Jean-Jacques Bellanger, José Luis Paredes

Author Affiliations +

Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 892204 (2013) https://doi.org/10.1117/12.2035517
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico

Abstract

Assessment of the cardiac Left Ventricle (LV) wall motion is generally based on visual inspection or quantitative analysis of 2D+t sequences acquired in short-axis cardiac cine-Magnetic Resonance Imaging (MRI). Most often, cardiac dynamic is globally analized from two particular phases of the cardiac cycle. In this paper, we propose an automated method to classify regional wall motion in LV function based on spatio-temporal pro les and Support Vector Machines (SVM). This approach allows to obtain a binary classi cation between normal and abnormal motion, without the need of pre-processing and by exploiting all the images of the cardiac cycle. In each short- axis MRI slice level (basal, median, and apical), the spatio-temporal pro les are extracted from the selection of a subset of diametrical lines crossing opposites LV segments. Initialized at end-diastole phase, the pro les are concatenated with their corresponding projections into the succesive temporal phases of the cardiac cycle. These pro les are associated to di erent types of information that derive from the image (gray levels), Fourier, Wavelet or Curvelet domains. The approach has been tested on a set of 14 abnormal and 6 healthy patients by using a leave-one-out cross validation and two kernel functions for SVM classi er. The best classi cation performance is yielded by using four-level db4 wavelet transform and SVM with a linear kernel. At each slice level the results provided a classi cation rate of 87.14% in apical level, 95.48% in median level and 93.65% in basal level.

Citation Download Citation

Juan Mantilla, Mireille Garreau, Jean-Jacques Bellanger, and José Luis Paredes "Automated classification of LV regional wall motion based on spatio-temporal profiles from cardiac cine magnetic resonance imaging", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 892204 (19 November 2013); https://doi.org/10.1117/12.2035517

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