Tytuł artykułu

Kidney Segmentation in CT Data Using Hybrid Level-Set Method with Ellipsoidal Shape Constraints

Tytuł czasopisma

Metrology and Measurement Systems

Rocznik

2017

Wolumin

vol. 24

Numer

No 1

Autorzy

Skalski, Andrzej ; Heryan, Katarzyna ; Jakubowski, Jacek ; Drewniak, Tomasz

Słowa kluczowe

Level Set method ; kidney ; CT data ; image segmentation ; ellipsoid

Wydział PAN

Nauki Techniczne

Wydawca

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Data

2017.03.30

Typ

Artykuły / Articles

Identyfikator

DOI: 10.1515/mms-2017-0006 ; ISSN 2080-9050, e-ISSN 2300-1941

Źródło

Metrology and Measurement Systems; 2017; vol. 24; No 1

Strony

101-112

Referencje

Brigger (2000), - spline snakes : a flexible tool for parametric contour detection Transactions on Image Processing, IEEE, 9, 1484. ; Yang (2014), Automatic kidney segmentation in CT images based on multi - atlas image registration th Annual International Conference of the IEEE Engineering in Medicine and Biology, Society, 5538. ; Bugajska (2015), The renal vessel segmentation for facilitation of partial nephrectomy SPA Processing : Algorithms Architectures Arrangements and Applications, IEEE Signal, 50. ; Zollner (2007), Towards Automatically Assessment of Kidney Volume from DCE - MRI Time Courses using Active Contours Mag Reson, Proc Soc Med. ; Klatte (2015), A literature review of renal surgical anatomy and surgical strategies for partial nephrectomy, European urology, 68, 980, doi.org/10.1016/j.eururo.2015.04.010 ; Spiegel (2009), Segmentation of kidneys using a new active shape model generation technique based on non - rigid image registration Medical Imaging and Graphics, Computerized, 33, 29. ; Nedevschi (2008), Kidney CT image segmentation using multi - feature EM algorithm , based on Gabor filters th Conference on Intelligent Computer Communication and Processing, International, 4, 283. ; Tsagaan (2001), Segmentation of kidney by using a deformable model Processing Proceedings International Conference on, Image, 1059. ; Dice (1945), Measures of the amount of ecologic association between species, Ecology, 26, 297, doi.org/10.2307/1932409 ; Xu (2015), Efficient multi - atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning image analysis, Medical, 24, 18. ; Khalifa (2011), A new deformable model - based segmentation approach for accurate extraction of the kidney from abdominal CT images th Conference on Image Processing, IEEE International ICIP, 18, 3393. ; Shao (2013), Application of a vasculature model and standardization of the renal hilar approach in laparoscopic partial nephrectomy for precise segmental artery clamping, European Urology, 63, 1072, doi.org/10.1016/j.eururo.2012.10.017 ; Vese (2002), A multiphase level set framework for image segmentation using the Mumford and Shah model of computer vision, International journal, 50, 271. ; Osher (2006), Level set methods and dynamic implicit surfaces Business Media, Springer Science. ; Kass (1988), Snakes : Active contour models of computer vision, International journal, 1, 321. ; Pluempitiwiriyawej (2005), STACS : New active contour scheme for cardiac MR image segmentation Transactions on Medical Imaging, IEEE, 24, 593. ; Lin (2006), Computer - aided kidney segmentation on abdominal CT images Transactions on Information Technology in Biomedicine, IEEE, 10, 59. ; Yushkevich (2006), User - guided active contour segmentation of anatomical structures : Significantly improved efficiency and reliability, Neuroimage, 31, 1116, doi.org/10.1016/j.neuroimage.2006.01.015 ; Chan (2001), Active contours without edges transactions on Image processing, IEEE, 10, 266.