KEYWORDS: 3D modeling, 3D image processing, Data modeling, Image segmentation, Statistical modeling, Transducers, Echocardiography, Ultrasonography, Data acquisition, Performance modeling
This paper presents a novel model based segmentation technique for quantification of Left Ventricular (LV) function
from sparse single-beat 3D echocardiographic data acquired with a Fast Rotating Ultrasound (FRU) transducer. This
transducer captures cardiac anatomy in a sparse set of radially sampled, curved cross sections within a single cardiac
cycle. The method employs a 3D Active Shape Model of the Left Ventricle (LV) in combination with local appearance
models as prior knowledge to steer the segmentation. A set of local appearance patches generate the model update points
for fitting the model to the LV in the curved FRU cross-sections. Updates are then propagated over the dense 3D model
mesh to overcome correspondence problems due to the data sparsity, whereas the 3D Active Shape Model serves to
retain the plausibility of the generated shape.
Leave-one-out cross validation was carried out on single-beat FRU data from 28 patients suffering from various cardiac
pathologies. Error measurements against expert-annotated contours yielded an average point-to-point distance of around
3.8 ± 2.4 mm and point-to-surface distance of 2.0 ± 1.8 mm and average volume estimation error of around 9 ± 7%.
Robustness tests with respect to different model initializations showed acceptable performance for initial positions within
a range of 22 mm for displacement and 12° for orientation. This demonstrates that the method combines robustness with
respect to initialization with an acceptable accuracy, while using sparse single-beat FRU data.
Automatic image segmentation techniques are essential for medical image interpretation and analysis. Though
numerous methods on image segmentation have been reported, the quality of a segmentation often heavily relies on the
positioning of an accurate initial contour. In this paper, a novel solution is presented for the automated object detection
in medical image data. A shape- and intensity template is generated from a training set, and both the search image and
the template are mapped into a log-polar domain, where rotation and scale are represented by a translation. Orientation
and scale of the object are estimated by determining maximum normalized correlation using a Symmetric Phase Only
Matched Filter (SPOMF) with a peak enhancement filter. The detected orientation and scale are subsequently applied to
the template, and a second pass of the SPOMF using the transformed template yields the actual position of the object in
the search image. Performance tests were carried out on two imaging modalities: a set of cardiac MRI images from 34
patients and 2D echocardiograms from 100 patients.
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