Fuzzy algorithms: Application to adipose tissue quantification on MR images

Biomedical Signal Processing and Control

Volumn 2, Issue 3, 2007, Pages 239-247

  Roullier, Vincent ^a,b,c   Cavaro Ménard, Christine ^a   Calmon, Guillaume ^b   Aubé, Christophe ^c  

^a LISA UPRES EA 4014   (France)

^b GE HEALTHCARE   (France)

^c ANGERS UNIVERSITY HOSPITAL   (France)

Author keywords

FGcM; Fuzzy methods; Medical image analysis; MRI

Indexed keywords

CARDIOVASCULAR SYSTEM; CLUSTERING ALGORITHMS; DISEASES; FUZZY SYSTEMS; MAGNETIC RESONANCE IMAGING; METABOLISM; TISSUE;

ADIPOSE TISSUE; FUZZY ALGORITHMS; METABOLIC SYNDROME;

MEDICAL IMAGING;

ABDOMINAL OBESITY; ADIPOSE TISSUE; ADULT; AGED; ARTICLE; AUTOMATION; CARDIOVASCULAR DISEASE; CLINICAL ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; FEMALE; FUZZY SYSTEM; HIGH RISK PATIENT; HUMAN; INTRAABDOMINAL FAT; MALE; METABOLIC SYNDROME X; NUCLEAR MAGNETIC RESONANCE IMAGING; PATIENT IDENTIFICATION; PRIORITY JOURNAL; RELIABILITY; RISK ASSESSMENT; SUBCUTANEOUS FAT; VALIDATION PROCESS; WAIST CIRCUMFERENCE; WESTERN HEMISPHERE;

EID: 35348854463     PISSN: 17468094     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bspc.2007.07.005     Document Type: Article

References (12)

1. Ford E.S., Giles W.H., and Dietz W.H. Prevalence of the metabolic syndrome among US adults: findings from the third national health and nutrition examination survey. JAMA 287 3 (2002) 356-359
2. Ninomiya J.K., L'Italien G., Criqui M.H., Whyte J.L., Gamst A., and Chen R.S. Association of the metabolic syndrome with history of myocardial infarction and stroke in the third national health and nutrition examination survey. Circulation 109 1 (2004) 42-46
3. Alberti K.G., Zimmet P., Shaw J., and I.D.F. Epidemiology Task Force Consensus Group. The metabolic syndrome-a new worldwide definition. Lancet 366 9491 (2005) 1059-1062
4. Positano V., Gastaldelli A., Sironi A., Santarelli M., Lombardi M., and Landini L. An accurate and robust method for unsupervised assessment of abdominal fat by MRI. J. Magn. Reson. Imag. 20 (2004) 684-689
5. Poll L.W., Wittsack H.-J., Koch J.-A., Willers R., Cohnen M., Kapitza C., Heinemann L., and Mödder U. A rapid and reliable semiautomated method for measurment of total abdominal fat volumes using magnetic resonance imaging. Magn. Reson. Imag. 21 6 (2003) 631-636
6. Dunn J. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybernetics 3 (1973) 32-57
7. Bezdek J. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
8. Ménard M., and Eboueya M. Extreme physical information and objective functions in fuzzy clustering. Fuzzy Sets Syst. 128 3 (2002) 285-303
9. Krishnapuram R. A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1 (1993) 98-110
10. Urso P.D., and Giordani P. A weighted fuzzy c-means clustering model for fuzzy data. Comput. Stat. Data Anal. 50 6 (2006) 1496-1523
11. Tsallis C. Entropic nonextensivity: a possible measure of complexity. Fractal 13 2 (2000) 371-391
12. Udupa J.K., and Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph. Models Image Process. 58 3 (1996) 246-261