Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification

[1]  Paul Suetens,et al.  Fundamentals of Medical Imaging by Paul Suetens , 2009 .

[2]  Bart De Dobbelaer,et al.  Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification , 2008, The MIDAS Journal.

[3]  Jean Stawiaski,et al.  Interactive Liver Tumor Segmentation Using Graph-cuts and Watershed , 2008, The MIDAS Journal.

[4]  M. Schaap,et al.  3D Segmentation in the Clinic: A Grand Challenge II - Coronary Artery Tracking , 2008, The MIDAS Journal.

[5]  R. Engelmann,et al.  Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique. , 2007, Medical physics.

[6]  B. Ginneken,et al.  3D Segmentation in the Clinic: A Grand Challenge , 2007 .

[7]  R. Tuma Sometimes size does matter , 2006, The Journal of Cell Biology.

[8]  C. Thng,et al.  Liver Tumor Volume Estimation By Semi-Automatic Segmentation Method , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[9]  Luis Ibáñez,et al.  The ITK Software Guide , 2005 .

[10]  Richard C. Pais,et al.  Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography. , 2004, Academic radiology.

[11]  S. Osher,et al.  Geometric Level Set Methods in Imaging, Vision, and Graphics , 2011, Springer New York.

[12]  Peter J. Yim,et al.  Volumetry of hepatic metastases in computed tomography using the watershed and active contour algorithms , 2003, 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings..

[13]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[14]  E. Halpern,et al.  CT tumor measurement for therapeutic response assessment: comparison of unidimensional, bidimensional, and volumetric techniques initial observations. , 2002, Radiology.

[15]  Koenraad Van Leemput,et al.  Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.

[16]  M. Christian,et al.  [New guidelines to evaluate the response to treatment in solid tumors]. , 2000, Bulletin du cancer.

[17]  A. Reeves,et al.  Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images. , 1999, Medical physics.

[18]  G. Marchal,et al.  Evaluation of manual vs semi-automated delineation of liver lesions on CT images , 1997, European Radiology.

[19]  E. Van Cutsem,et al.  Size quantification of liver metastases in patients undergoing cancer treatment: reproducibility of one-, two-, and three-dimensional measurements determined with spiral CT. , 1997, Radiology.

[20]  R. Kimmel,et al.  Geodesic Active Contours , 1995, Proceedings of IEEE International Conference on Computer Vision.

[21]  Guy Marchal,et al.  Automatic image partitioning for generic object segmentation in medical images , 1995 .

[22]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..