Neda Changizi Successfully Defends Her MSc Thesis
April 29, 2010
Congratulations to Neda Changizi on successfully defending her MSc thesis, entitled:
PROBABILISTIC MULTI-LABEL REPRESENTATIONS FOR STATISTICAL ANATOMICAL SHAPE ANALYSIS
Several sources of uncertainties in shape boundaries in medical images have motivated the use of probabilistic labeling approaches. Being able to perform statistical analysis on these probabilistic multi-shape representations is important in understanding normal and pathological geometrical variability of anatomical structures. By making use of methods for dealing with what is known as compositional data, we propose a new framework intrinsic to the unit simplex for statistical analysis of probabilistic multi-shape anatomy. In this framework, the isometric logratio transformation is used to isometrically and bijectively map the simplex to the Euclidean real space.
As another contribution of this thesis, the label space multi-shape representation is extended to the barycentric label space, in which a proper invertible mapping between probability vectors and label space is proposed.
Favorable properties of the proposed methods are demonstrated quantitatively and qualitatively on artificial objects and brain image data.
M.Sc. Examining Committee:
Dr. Ghassan Hamarneh, Senior Supervisor
Dr. Richard Zhang, Supervisor
Dr. Torsten Möller, Examiner
Dr. Greg Mori, Chair
More info here.