Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation
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Fecha
2019
Autores
Huérfano, Y.
Vera, M.
Mar, A.
Bravo, A.
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Editor
IOP Publishing
Resumen
In this research a three step method for right heart segmentation based on a gradient–
based difference operator and machine learning techniques is reported. The proposed method is
applied to human heart multi–slice computerized tomography (MSCT) volumes. The first step
is the preprocessing, where a gradient–based difference operator is applied to exploit the
functional relationship between the original input image and its edge enhanced version. In the
second step, the least squares support vector machines (LSSVM) are used with a double purpose.
First, an appropriate volume-of-interest is automatically established in order to isolate the
structure to segment. Second, another LSSVM is trained for locating the voxels required for
initializing the seed based clustering procedure. In the third step (segmentation step), the
preprocessed volumes are subsequently processed with an unsupervised clustering technique
based on simple linkage region growing. Dice score is used as a metric function to compare the
segmentations obtained using the proposed method with respect to ground truth volumes traced
by a cardiologist. The right atrium, pulmonary valve, right ventricle and venae cavae are
segmented from 80 cardiac MSCT volumes. Reported metrics confirm that this method is a
promising technique for right heart segmentation.
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Palabras clave
Computed tomography, Segmentation of the heart, Heart - Diseases - Diagnosis