Advanced Image Acquisition, Processing Techniques, Applns, by D. Ventzas

By D. Ventzas

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2006). A review on mr image intensity inhomogeneity correction, Int. J. Biomed. 1-11. ; Tlibblin, G. (1984). Abdominal adipose tissue distribution, obesity and risk of cardiovascular disease and death: 13 y follow up of participants in the study of men born in 1913. 1401-1404. (2009). Automated separation of visceral and subcutaneous adiposity in in-vivo microcoputed tomographies of mice. 222-231. (2009). In vivo quantification of subcutaneous and visceral adiposity by micro-computed tomography in a small animal model.

1998). 839-846. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging. 405–21. Wang, D. ; Vagnucci, A. (1981). Gradient inverse weighted smoothing scheme and the evaluation of its performance. 167–181. A. (1965). Fuzzy sets. Inform Contr,1965,Vol. 338-353. (2006). Automated quantification of body fat distribution on volumetric computed tomography. 777-783. 4 Image Processing Quality Analysis for Particle Based Peptide Array Production on a Microchip 1Kirchhoff Jenny Wagner1,2,6 et al* Institute for Physics, Heidelberg University 2German Cancer Research Centre 6Frankfurt Institute for Advanced Studies Germany 1.

To deal with problem of the non fat tissue with high intensity in the T1 weighted image, the T2 parametric image based primarily on relaxation is explicitedly calculated from the multiple echo images. Because the T2 value reflects the transverse relaxation time of tissues, the T2 parametric image can help to separate the fat and non-fat tissues. An example is displayed in Figure 4, where the T2 parametric image exhibits a larger range of statitstially distinguishable values and thus aids us in seperating the fat from the non-fat tissues.

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