Coronary heart disease (CHD) is a global epidemic that is the leading cause of death
worldwide. CHD can be detected by measuring and scoring the regional and global motion of
the left ventricle (LV) of the heart. This works describes a novel automatic technique which can
detect the regional wall motion abnormalities of the LV from echocardiograms. Given a sequence
of endocardial contours extracted from LV ultrasound images, the sequence of contours moving
through time can be interpreted as a three-dimensional (3D) surface. From the 3D surfaces, we
compute several geometry-based features (shape-index values, curvedness, surface normals, etc.)
to obtain histograms-based similarity functions that are optimally combined using a mathematical
programming approach to learn a kernel function designed to classify normal vs. abnormal
heart wall motion. In contrast with other state-of-the-art methods, our formulation also generates
sparse kernels. Kernel sparsity is directly related to the computational cost of the kernel evaluation,
which is an important factor when designing classifiers that are part of a real time system.
Experimental results on a set of echocardiograms collected in routine clinical practice at one hospital
demonstrate the potential of the proposed approach
Keywords: Coronary heart disease, echocardiograms, ultrasound images, histograms, kernel functions
