Skip to main content

Model-Based Clustering Methods for Medical Images

PI: Adrian Elmes Raftery
Sponsor: Model-Based Clustering Methods for Medical Images
Project Period: -
Amount: $1,090,322.00

Abstract

Many problems in the health and medical sciences have at their core the task of finding cohesive groups of observations in data. Examples include a group of voxels in an MRI image that correspond to a tumor, genes whose mRNA expression levels track one another, and tissues whose gene expression patterns are similar. The statistical method for solving this problem is cluster analysis. Most cluster analysis methods used in practice have been ad hoc, but recently the development of more formal model-based clustering methods has provided a principled framework for answering central questions such as: How many clusters are there? Which clustering method should be used? How should one deal with outliers? Our main goal is to develop new methods for problems in model-based clustering that arise in medical image segementation and gene expression data. The three major thrusts will be the development of: (A) model-based clustering methods for large numbers of variables; (B) automated medical image segementation methods appropriate for dynamic MRI breast images; and (C) model-based clustering methods for microarray gene expression data aimed at finding groups of genes that function together, and groups of tissues or tissue types that have similar gene expression patterns.