A greedy completion algorithm for retrieving fuzzy fine structures : Application to aortic lumina separation in CTA data

Abstract

By this contribution we tackle the challenge of extracting fuzzy, curvilinear fine structures from medical image data. The dissection membrane represents a highly tortuous fine structure within aortas of dissection patients. Due to its variability in topology and morphology, extraction by any assumed shape priors is deemed to fail. Based on the response of 3D/2D phase congruency filter, we select a segment of high significance in order to remove false positives within each CTA slice. Multicriterial, greedy tracking serves for membrane completion, while a inter-slice grouping algorithm performs detection of global outliers. Erroneous slice results are replaced using sampled membrane segments from adjacent slices. Our proposed algorithm not only improves the membrane segmentation by up to 32% when compared to stand-alone usage of local phase features, but also enables separation of the true and false lumen.