Image classification and feature selection

Chen, Gang

Permalink

https://hdl.handle.net/2142/31979 Copy

Description

Title

Image classification and feature selection

Author(s)

Chen, Gang

Issue Date

2012-06-27T21:22:52Z

Director of Research (if dissertation) or Advisor (if thesis)

Simpson, Douglas G.

Doctoral Committee Chair(s)

Simpson, Douglas G.

Committee Member(s)

• Liang, Feng
• Qu, Annie
• Oelze, Michael L.

Department of Study

Statistics

Discipline

Statistics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(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.

Graduation Semester

2012-05

Permalink

http://hdl.handle.net/2142/31979

Copyright and License Information

copyright2012 Gang Chen

Owning Collections