摘要：We propose a constrained version of Mumford and Shah's (1989) segmentation model with an information-theoretic
point of view in order to devise a systematic procedure to
segment brain magnetic resonance imaging (MRI) data for
parametric T1-Map and T1-weighted images, in both 2-D and
3D settings. Incorporation of a tuning weight in particular adds
a probabilistic flavor to our segmentation method, and makes
the 3-tissue segmentation possible. Moreover, we proposed a
novel method to jointly segment the T1-Map and calibrate RF Inhomogeneity
(JSRIC). This method assumes the average T1 value of white matter is the same across transverse slices in
the central brain region, and JSRIC is able to rectify the flip angles
to generate calibrated T1-Maps. In order to generate an
accurate T1-Map, the determination of optimal flip-angles and
the registration of flip-angle images are examined. Our JSRIC
method is validated on two human subjects in the 2D T1-Map
modality and our segmentation method is validated by two public
databases, BrainWeb and IBSR, of T1-weighted modality in
the 3D setting.
