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.
In this paper, a new approach for Confocal Microscopy (CM) based on the framework of compressive sensing is
developed. In the proposed approach, a point illumination and a random set of pinholes are used to eliminate
out-of-focus information at the detector. Furthermore, a Digital Micromirror Device (DMD) is used to efficiently
scan the 2D or 3D specimen but, unlike the conventional CM that uses CCD detectors, the measured data in
the proposed compressive confocal microscopy (CCM) emerge from random sets of pinhole illuminated pixels
in the specimen that are linearly combined (projected) and measured by a single photon detector. Compared
to conventional CM or programmable array microscopy (PAM), the number of measurements needed for nearly
perfect reconstruction in CCM is significantly reduced. Our experimental results are based on a testbed that uses
a Texas Instruments DMD (an array of 1024×768; 13.68×13.68 μm2 mirrors) for computing the linear projections
of illuminated pixels and a single photon detector is used to obtain the compressive sensing measurement. The
position of each element in the DMD is defined by the compressed sensing measurement matrices. Threedimensional
image reconstruction algorithms are developed that exploit the inter-slice spatial image correlation
as well as the correlation between different 2D slices. A comprehensive performance comparison between several
binary projection patterns is shown. Experimental and simulation results are provided to illustrate the features
of the proposed systems.
Conference Committee Involvement (1)
Tenth International Symposium on Medical Information Processing and Analysis
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