Regularized Statistical Analysis of Anatomy

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.

Autorentext
Karl Sjöstrand recieved his Ph.D. degree in Mathematical Modelling from the Technical University of Denmark in 2007 and his master of science degree in computer systems engineering from the Technical Faculty at the University of Lund, Sweden, in 2003.

Klappentext
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.