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.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.