IDEALS Home University of Illinois at Urbana-Champaign logo The Alma Mater The Main Quad

Image classification and feature selection

Show full item record

Bookmark or cite this item: http://hdl.handle.net/2142/31979

Files in this item

File Description Format
PDF Chen_Gang.pdf (2MB) Restricted to U of Illinois (no description provided) PDF
Title: Image classification and feature selection
Author(s): Chen, Gang
Director of Research: Simpson, Douglas G.
Doctoral Committee Chair(s): Simpson, Douglas G.
Doctoral Committee Member(s): Liang, Feng; Qu, Annie; Oelze, Michael L.
Department / Program: Statistics
Discipline: Statistics
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: Ph.D.
Genre: Dissertation
Subject(s): Ultrasound tissue classification feature selection, Level Set Segmentation B-mode image logistic regression Variational Bayesian Lasso Elastic Net Adaboost Genetic Algorithm map/reduce cloud computing
Abstract: Tissue classification and feature selection have been increasing studied during the last two decades, however the available methods are still limited and need improvement. In this manuscript, we develop tissue classification and feature selection methods based on Dynamic Adaboost with logistic regression as its weak learner and a new Variational Bayesian (VB) logistic regression with regularization. Furthermore we investigate the statistical properties of these methods and extend VB logistic regression to handle large scale data. In chapter 1, we will introduce some key concepts like Ultrasound Tissue Classification, Level Set Segmentation method, Bayesian version of Lasso and Elastic Net and Variational Bayesian approximation. In chapter 2, we will introduce a framework of tumor segmentation and feature extraction for ultrasound B-mode images, as well as a semi-parametric model for the texture features. In chapter 3, we apply the Adaboost method with logistic regression as weak learner for tumor classification. Genetic Algorithm (GA) is used for stochastic search based feature selection and the algorithm is parallelized to accelerate the computation. In chapter 4, we propose a new variational Bayesian logistic regression incorporating the Lasso and Elastic Net type regularization for feature selection. In chapter 5, we extend the above VB logistic regression to large scale data by map/reduce cloud computing. We will illustrate the experimental results in each chapter using simulation data and ultrasound image data from our research.
Issue Date: 2012-06-27
URI: http://hdl.handle.net/2142/31979
Rights Information: copyright2012 Gang Chen
Date Available in IDEALS: 2012-06-27
Date Deposited: 2012-05
 

This item appears in the following Collection(s)

Show full item record

Item Statistics

  • Total Downloads: 10
  • Downloads this Month: 0
  • Downloads Today: 0

Browse

My Account

Information

Access Key