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Realce Por Similaridad Local Para La SegmentacióN Computacional Del VentríCulo Derecho En ImáGenes De TomografíA Computarizada CardíAca

Abstract:

This work proposes a strategy to segment the right ventricle (RV) into three-dimensional (3-D) multi-layered computed tomography images. This strategy consists of the stages of pre-processing, segmentation and intonation of parameters. The preprocessing stage is divided into two phases. In the first one, called the filtering phase, a technique called Local Similarity Enhancement (LSE) is used in order to reduce the impact of artifacts and attenuate noise in the quality of the images. This technique combines an averaging filter, an edge detector filter (called black top hat) and a Gaussian Filter (GF). In the second, identified as a Region Of Interest definition phase (ROI), filtered images, least squares vector support machines and a priori information are considered to isolate the anatomical structures that surround the RV. On the other hand, a clustering algorithm, called Region Growth (RG), is implemented during the 3-D segmentation stage, which is applied to the preprocessed images. During the intonation of parameters of the proposed strategy, the Dice Coefficient (Dc) is used to compare the segmentations of the RV, obtained automatically, with the segmentation of the right ventricle generated manually by a cardiologist. The combination of parameters that generated the highest Dc considering the instant of diastole is then applied to the remaining 19 three-dimensional images, obtaining an average Dc higher than 0.85 which indicates a good correlation between the segmentations generated by the expert cardiologist and those produced by the strategy developed.