• Vincenzo Positano, Amalia Gastaldelli, Anna Maria Sironi, Maria Filomena Santarelli, Massimo Lombardi, and Luigi Landini.
• Institute of Clinical Physiology, CNR, Pisa, Italy. positano@ifc.cnr.it
• J Magn Reson Imaging. 2004 Oct 1; 20 (4): 684-9.

PurposeTo describe and evaluate an automatic and unsupervised method for assessing the quantity and distribution of abdominal adipose tissue by MRI.Material And MethodsA 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.ResultsThe 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.ConclusionThe 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.Copyright 2004 Wiley-Liss, Inc.

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