SVM-based classification of LV wall motion in cardiac MRI with the assessment of STE

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

doi:10.1117/12.2073674

28 January 2015 SVM-based classification of LV wall motion in cardiac MRI with the assessment of STE

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

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 92870N (2015) https://doi.org/10.1117/12.2073674
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

Abstract

In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI), taking as reference, strain information obtained from 2D Speckle Tracking Echocardiography (STE). Without the need of pre-processing and by exploiting all the images acquired during a cardiac cycle, spatio-temporal profiles are extracted from a subset of radial lines from the ventricle centroid to points outside the epicardial border. Classical Support Vector Machines (SVM) are used to classify features extracted from gray levels of the spatio-temporal profile as well as their representations in the Wavelet domain under the assumption that the data may be sparse in that domain. Based on information obtained from radial strain curves in 2D-STE studies, we label all the spatio-temporal profiles that belong to a particular segment as normal if the peak systolic radial strain curve of this segment presents normal kinesis, or abnormal if the peak systolic radial strain curve presents hypokinesis or akinesis. For this study, short-axis cine- MR images are collected from 9 patients with cardiac dyssynchrony for which we have the radial strain tracings at the mid-papilary muscle obtained by 2D STE; and from one control group formed by 9 healthy subjects. The best classification performance is obtained with the gray level information of the spatio-temporal profiles using a RBF kernel with 91.88% of accuracy, 92.75% of sensitivity and 91.52% of specificity.

Citation Download Citation

Juan Mantilla, Mireille Garreau, Jean-Jacques Bellanger, and José Luis Paredes "SVM-based classification of LV wall motion in cardiac MRI with the assessment of STE", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 92870N (28 January 2015); https://doi.org/10.1117/12.2073674

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