Use of active shape models for locating structures in medical images

http://dbkgroup.org/Papers/davey_kell_micrev96.pdf

Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses.

This paper describes a technique for building compact models of the shape and appearance of flexible objects (such as organs) seen in 2-D images The models are derived from the statistics of sets of labelled images of examples of the objects Each model consists of a flexible shape template, describing how important points of the object can vary, and a statistical model of the expected grey levels in regions around each model point The shape models are parameterised in such a way as to allow ‘legal’ configurations Such models have proved useful in a wide variety of applications We describe how the models can be used in local image search and give examples of their application to medical images We also describe how the method can be simply extended to segment 3-D objects in volume images and to track structures in image sequences

The Use of Active Shape Models for Locating Structures in Medical Images

An Introduction to R What Is R? Installing R Help and Documentation Data Objects in R Data Import and Export Basic Data Manipulation Computing with Data Organizing an Analysis Data Analysis Using Graphical Displays Introduction Initial Data Analysis Analysis Using R Simple Inference Introduction Statistical Tests Analysis Using R Conditional Inference Introduction Conditional Test Procedures Analysis Using R Analysis of Variance Introduction Analysis of Variance Analysis Using R Simple and Multiple Linear Regression Introduction Simple Linear Regression Multiple Linear Regression Analysis Using R Logistic Regression and Generalized Linear Models Introduction Logistic Regression and Generalized Linear Models Analysis Using R Density Estimation Introduction Density Estimation Analysis Using R Recursive Partitioning Introduction Recursive Partitioning Analysis Using R Scatterplot Smoothers and Generalized Additive Models Introduction Scatterplot Smoothers and Generalized Additive Models Analysis Using R Survival Analysis Introduction Survival Analysis Analysis Using R Analyzing Longitudinal Data I Introduction Analyzing Longitudinal Data Linear Mixed Effects Models Analysis Using R Prediction of Random Effects The Problem of Dropouts Analyzing Longitudinal Data II Introduction Methods for Nonnormal Distributions Analysis Using R: GEE Analysis Using R: Random Effects Simultaneous Inference and Multiple Comparisons Introduction Simultaneous Inference and Multiple Comparisons Analysis Using R Meta-Analysis Introduction Systematic Reviews and Meta-Analysis Statistics of Meta-Analysis Analysis Using R Meta-Regression Publication Bias Principal Component Analysis Introduction Principal Component Analysis Analysis Using R Multidimensional Scaling Introduction Multidimensional Scaling Analysis Using R Cluster Analysis Introduction Cluster Analysis Analysis Using R Bibliography Index A Summary appears at the end of each chapter.

/pdf/a-handbook-of-statistical-analyses-using-r-5agandye4o.pdf

A Handbook of Statistical Analyses Using R

It is essential to keep track of the movements we make, and one way to do that is to monitor correlates, or corollary discharges, of neuronal movement commands. We hypothesized that a previously identified pathway from brainstem to frontal cortex might carry corollary discharge signals. We found that neuronal activity in this pathway encodes upcoming eye movements and that inactivating the pathway impairs sequential eye movements consistent with loss of corollary discharge without affecting single eye movements. These results identify a pathway in the brain of the primate Macaca mulatta that conveys corollary discharge signals.

/pdf/a-pathway-in-primate-brain-for-internal-monitoring-of-24bbpntum8.pdf

WŁadysław Milo

Papers

Multivariate Statistics: A Practical Approach