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Segmentation of the pulmonary valve from cardiac tomography images using a strategy based on local similarity enhancement

Abstract:

n the following article, the use of the local similarity strategy in the three-dimensional (3D) segmentation of the pulmonary valve in 20 cardiac multilayer computed tomography images corresponding to the complete cardiac cycle of a subject is reported. The strategy consists of the following stages: a) pre-processing, b) segmentation and c) intonation of parameters. Step a) is applied, preliminary to the final diastole instant and is divided into two phases called: Filtering and Definition of a region of interest (ROI) and using the so-called local similarity (LSE) technique. The application of these phases is intended to address the problems of noise, artifacts and low contrast that these images have. Stage b) allows segmentation of the pulmonary valve, using a clustering algorithm called region growth (RG) which is applied to the pre-processed images. The RG is initialized with a “seed” voxel which is detected by an artificial intelligence operator called least squares vector support machines (LSSVM). Finally, during step c), a metric called Dice coefficient (Dc) is used to compare the segmentations obtained by the proposed strategy and the segmentation generated manually by a cardiologist. The combination of filtering techniques that generates the highest Dc considering the instant of diastole is subsequently applied to the remaining 19 3D images, obtaining an average Dc comparable to that reported in the specialized literature.