Fully automatic and nonparametric quantification of adipose tissue in fat–water separation MR imaging

[1]  Alistair A. Young,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2017, MICCAI 2017.

[2]  Kostas Delibasis,et al.  A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces , 2013, Comput. Methods Programs Biomed..

[3]  Bernd Freisleben,et al.  Segmentation of pituitary adenoma: A graph-based method vs. a balloon inflation method , 2013, Comput. Methods Programs Biomed..

[4]  Cecilia Sjöberg,et al.  Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures , 2013, Comput. Methods Programs Biomed..

[5]  M. R. Cheung,et al.  Using manual prostate contours to enhance deformable registration of endorectal MRI , 2012, Comput. Methods Programs Biomed..

[6]  Meritxell Bach Cuadra,et al.  A review of atlas-based segmentation for magnetic resonance brain images , 2011, Comput. Methods Programs Biomed..

[7]  H. Eggers,et al.  Dual‐echo Dixon imaging with flexible choice of echo times , 2011, Magnetic resonance in medicine.

[8]  Rasmus Larsen,et al.  Unsupervised Assessment of Subcutaneous and Visceral Fat by MRI , 2009, SCIA.

[9]  Örjan Smedby,et al.  Quantification of abdominal fat accumulation during hyperalimentation using MRI , 2009 .

[10]  Örjan Smedby,et al.  Quantitative abdominal fat estimation using MRI , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  A. Horská,et al.  Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging , 2008, International Journal of Obesity.

[12]  C. McKenzie,et al.  Validation of Fat Volume Quantification with IDEAL MRI , 2008 .

[13]  Qi Peng,et al.  Automated method for accurate abdominal fat quantification on water‐saturated magnetic resonance images , 2007, Journal of magnetic resonance imaging : JMRI.

[14]  J. Kullberg,et al.  Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI , 2007, International Journal of Obesity.

[15]  R. Semelka,et al.  Accurate quantification of visceral adipose tissue (VAT) using water‐saturation MRI and computer segmentation: Preliminary results , 2006, Journal of magnetic resonance imaging : JMRI.

[16]  W. Chan,et al.  The preferred magnetic resonance imaging planes in quantifying visceral adipose tissue and evaluating cardiovascular risk , 2005, Diabetes, obesity & metabolism.

[17]  Luigi Landini,et al.  An accurate and robust method for unsupervised assessment of abdominal fat by MRI , 2004, Journal of magnetic resonance imaging : JMRI.

[18]  W. Chan,et al.  Sonographic measurement of mesenteric fat thickness is a good correlate with cardiovascular risk factors: comparison with subcutaneous and preperitoneal fat thickness, magnetic resonance imaging and anthropometric indexes , 2003, International Journal of Obesity.

[19]  Lutz Heinemann,et al.  A rapid and reliable semiautomated method for measurement of total abdominal fat volumes using magnetic resonance imaging. , 2003, Magnetic resonance imaging.

[20]  J. Després,et al.  Waist circumference, visceral obesity, and cardiovascular risk. , 2003, Journal of cardiopulmonary rehabilitation.

[21]  Masafumi Matsuda,et al.  Metabolic effects of visceral fat accumulation in type 2 diabetes. , 2002, The Journal of clinical endocrinology and metabolism.

[22]  M. Shinomiya,et al.  A novel method of measuring intra-abdominal fat volume using helical computed tomography , 2002, International Journal of Obesity.

[23]  B. Wajchenberg Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. , 2000, Endocrine reviews.

[24]  P. Kopelman Obesity as a medical problem , 2000, Nature.

[25]  P. Tothill,et al.  Measurement of abdominal fat by magnetic resonance imaging, dual-energy X-ray absorptiometry and anthropometry in non-obese men and women , 1999, International Journal of Obesity.

[26]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[27]  R M Peshock,et al.  Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. , 1994, Journal of lipid research.

[28]  J. Seidell,et al.  Techniques for the measurement of visceral fat: a practical guide. , 1993, International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity.

[29]  Enzo Bonora,et al.  Measurement of abdominal fat with T1‐weighted MR images , 1991, Journal of magnetic resonance imaging : JMRI.

[30]  J H Ruijs,et al.  Assessment of intra-abdominal and subcutaneous abdominal fat: relation between anthropometry and computed tomography. , 1987, The American journal of clinical nutrition.

[31]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[33]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[34]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[35]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .