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This book presents the development and application of
regularized statistical methods for the analysis of
anatomical structures. One of the comprehensive goals
of this type of research is to use non-invasive
medical imaging devices for the detection of diseases
which are otherwise difficult to diagnose at an early
stage. Statistics represents a quintessential part of
such investigations as they are preluded by a
clinical hypothesis that must be verified based on
observed data. The massive amounts of image data
produced in each examination pose an important and
interesting statistical challenge, in that there are
many more image features (variables) than subjects
(observations), making an infinite number of
solutions possible. To arrive at a unique and
interesting answer, the analysis must be regularized
in a sensible manner. This book describes such
regularization options, discusses efficient
algorithms which make the analysis of large data sets
feasible, and gives examples of applications.
regularized statistical methods for the analysis of
anatomical structures. One of the comprehensive goals
of this type of research is to use non-invasive
medical imaging devices for the detection of diseases
which are otherwise difficult to diagnose at an early
stage. Statistics represents a quintessential part of
such investigations as they are preluded by a
clinical hypothesis that must be verified based on
observed data. The massive amounts of image data
produced in each examination pose an important and
interesting statistical challenge, in that there are
many more image features (variables) than subjects
(observations), making an infinite number of
solutions possible. To arrive at a unique and
interesting answer, the analysis must be regularized
in a sensible manner. This book describes such
regularization options, discusses efficient
algorithms which make the analysis of large data sets
feasible, and gives examples of applications.