[행사] [2021.4.7(Wed.)] 대학원 인공지능&AI융합네트워크학과 콜로키엄 개최 안내
- 정보통신대학교학팀
- 김성민
- Create Date 2021-04-02
- Views 2876
< 대학원 인공지능 & AI융합네트워크학과 콜로키엄 개최 안내 >
(Artificial Intelligence & AI Convergence Network Colloquium)
- When : 2021.4.7.(Wed.) am 10:30~
- Abstract :
In this talk, I will present 3 different roles of total variation (TV) regularization in variational methods in image
segmentation and MR image reconstruction. First, TV as a measure of the perimeter of a candidate partition
encoded by the indicator function of a set: In unsupervised image segmentation, the total length of region
boundaries is often minimized to obtain a compact partition that likely matches the way humans perceive.
A statistical distance between color distributions of distinctive regions in a candidate partition is maximized with
the minimization of TV for unsupervised image partitioning. Second, TV as a measure of streaking artifacts in
QSM deconvolution: QSM is a noninvasive MRI method for a quantitative study of the tissue magnetic
susceptibility distribution by solving the magnetic field to susceptibility source inversion problem. A major
challenge in the ill-posed inverse problem is streaking artifacts from noise in the field which propagates at the
complementary magic angle. These artifacts can be selectively reduced by weighted TV regularization that
makes use of anatomical information of the corresponding magnitude image. Lastly, TV as a measure of
undersampling artifacts in image reconstruction for multi-contrast MRI. In clinical MRI, multiple contrasts such
as T1w, T2w, and FLAIR are sequentially acquired, consequently taking a long scan time. To shorten the scan
time, structuralinformation shared between contrasts is extracted and can be incorporated into the TV term as
an orthogonalprojector in the model-based image reconstruction for the subsequent contrasts that are highly
undersampled.