title: Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images.
abstract: Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p  <  0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands.
authors.forename: Ha Manh
authors.initials: HM
authors.lastname: Luu
authors.forename: Camiel
authors.initials: C
authors.lastname: Klink
authors.forename: Adriaan
authors.initials: A
authors.lastname: Moelker
authors.forename: Wiro
authors.initials: W
authors.lastname: Niessen
authors.forename: Theo
authors.initials: T
authors.lastname: van Walsum
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008102:Liver Circulation; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*