The purpose of this chapter is to present a survey of recent publications concerning medical
image registration techniques. These publications will be classified according to a model based
on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods
3D reconstruction, medical image processing from colons, using intel image processing for based class. This source code. Some code missing but I think you can understand it. Development version. This source code is very interesting for learning segmentation and registration from dataset. This code also has some technique about GPU image processing for ray tracing. Also learn many filter apply for transform from spatial domain to frequency domain.
醫(yī)學(xué)影像配準(zhǔn)（medical Image Registration）的入門資料。其中包括：
Fast Parametric Elastic Image Registration.pdf
Image Registration of Sectioned Brains.pdf
Mutual-information-based registration of medical images - a survey.pdf
Registration of histological serial sectionings.pdf
RegistrationMethodsOverview.pdf
From helping to assess the value of new medical treatments to evaluating the
factors that affect our opinions and behaviors, analysts today are finding
myriad uses for categorical data methods. In this book we introduce these
methods and the theory behind them.
Statistical methods for categorical responses were late in gaining the level
of sophistication achieved early in the twentieth century by methods for
continuous responses. Despite influential work around 1900 by the British
statistician Karl Pearson, relatively little development of models for categorical
responses occurred until the 1960s. In this book we describe the early
fundamental work that still has importance today but place primary emphasis
on more recent modeling approaches. Before outlining
