An accurate and robust method for unsupervised assessment of abdominal fat by MRI.
OBJECTIVE
To describe and evaluate an automatic and unsupervised method for assessing the quantity and distribution of abdominal adipose tissue by MRI.


METHODS
A total of 20 patients underwent whole-abdomen MRI. A total of 32 transverse T1-weighted images were acquired from each subject. The data collected were transferred to a dedicated workstation and analyzed by both our unsupervised method and a manual procedure. The proposed methodology allows the automatic processing of MRI axial images, segmenting the adipose tissue by fuzzy clustering approach. The use of an active contour algorithm on image masks provided by the fuzzy clustering algorithm allows the separation of subcutaneous fat from visceral fat. Finally, an automated procedure based on automatic image histogram analysis identifies the visceral fat.


RESULTS
The accuracy, reproducibility, and speed of our automatic method were compared with the state-of-the-art manual approach. The unsupervised analysis correlated well with the manual analysis, and was significantly faster than manual tracing. Moreover, the unsupervised method was not affected by intraobserver and interobserver variability.


CONCLUSIONS
The results obtained demonstrate that the proposed method can provide the volume of subcutaneous adipose tissue, visceral adipose tissue, global adipose tissue, and the ratio between subcutaneous and visceral fat in an unsupervised and effective manner.
Positano
Vincenzo
V
Institute of Clinical Physiology, CNR, Pisa, Italy. positano@ifc.cnr.it
Gastaldelli
Amalia
A
Sironi
Anna Maria
AM
Santarelli
Maria Filomena
MF
Lombardi
Massimo
M
Landini
Luigi
L
D034841:Abdominal Cavity / Q000473:pathology*; D000273:Adipose Tissue / Q000473:pathology*; D000886:Anthropometry; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009765:Obesity / Q000175:diagnosis; D015203:Reproducibility of Results