2026-09-18T23:51:30Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/31979 2023-07-10 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

Made available in DSpace on 2012-06-27T21:22:52Z (GMT). No. of bitstreams: 9 Chen_Gang.pdf: 2139361 bytes, checksum: 3e14f03bb001785a473bc6193dc54f8b (MD5) license.txt: 4058 bytes, checksum: 50cddeb1b191bb1536258b22551f35da (MD5) ch4.tex: 87063 bytes, checksum: 8fac0f031e2c4b465952bb040f6131fa (MD5) ch3.tex: 19442 bytes, checksum: 7a6939c2ae1ede909f491c8db5e02504 (MD5) ch5.tex: 18729 bytes, checksum: 7e1effd302d7b4e2e98452f88ee1f0f1 (MD5) ch2.tex: 30525 bytes, checksum: 66e921da17e3fc8c0d8f0f31f4065f7a (MD5) ch6.tex: 10742 bytes, checksum: 5aa5aa67d07f3f6a49cefc3f6e815b0a (MD5) ch1.tex: 19067 bytes, checksum: ff6a32c9efe1a49e3b6afbce3ac31856 (MD5) Gang.tex: 4907 bytes, checksum: 20041ebf17561d70153ce0200581768e (MD5) Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by William Ingram (wingram2@illinois.edu) on 2012-06-27T21:24:44Z Item is restricted until 2014-06-27T21:24:27Z B-mode image logistic regression Variational Bayesian Lasso Elastic Net Adaboost Genetic Algorithm map/reduce cloud computing Image classification and feature selection Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2014-06-28T10:00:24Z Item was in collections: Graduate Theses and Dissertations at Illinois (ID: 204) Dissertations and Theses - Statistics (ID: 774) No. of bitstreams: 9 Chen_Gang.pdf: 2139361 bytes, checksum: 3e14f03bb001785a473bc6193dc54f8b (MD5) license.txt: 4058 bytes, checksum: 50cddeb1b191bb1536258b22551f35da (MD5) ch4.tex: 87063 bytes, checksum: 8fac0f031e2c4b465952bb040f6131fa (MD5) ch3.tex: 19442 bytes, checksum: 7a6939c2ae1ede909f491c8db5e02504 (MD5) ch5.tex: 18729 bytes, checksum: 7e1effd302d7b4e2e98452f88ee1f0f1 (MD5) ch2.tex: 30525 bytes, checksum: 66e921da17e3fc8c0d8f0f31f4065f7a (MD5) ch6.tex: 10742 bytes, checksum: 5aa5aa67d07f3f6a49cefc3f6e815b0a (MD5) ch1.tex: 19067 bytes, checksum: ff6a32c9efe1a49e3b6afbce3ac31856 (MD5) Gang.tex: 4907 bytes, checksum: 20041ebf17561d70153ce0200581768e (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2014-06-28T10:00:24Z http://hdl.handle.net/2142/31979 en 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. Simpson, Douglas G. Simpson, Douglas G. Liang, Feng Qu, Annie Oelze, Michael L. Chen, Gang 2012-06-27T21:22:52Z 2012-06-27T21:22:52Z 2014-06-28T10:00:24Z 2012-05 2012-06-27T21:22:52Z 2012-05 Item withdrawn by Rebecca Bryant (rabryant@illinois.edu) on 2012-04-17T14:19:38Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 8 Gang.tex: 4907 bytes, checksum: 20041ebf17561d70153ce0200581768e (MD5) ch1.tex: 19067 bytes, checksum: ff6a32c9efe1a49e3b6afbce3ac31856 (MD5) ch6.tex: 10742 bytes, checksum: 5aa5aa67d07f3f6a49cefc3f6e815b0a (MD5) ch2.tex: 30525 bytes, checksum: 66e921da17e3fc8c0d8f0f31f4065f7a (MD5) ch5.tex: 18729 bytes, checksum: 7e1effd302d7b4e2e98452f88ee1f0f1 (MD5) ch3.tex: 19442 bytes, checksum: 7a6939c2ae1ede909f491c8db5e02504 (MD5) ch4.tex: 87063 bytes, checksum: 8fac0f031e2c4b465952bb040f6131fa (MD5) Chen_Gang.pdf: 2139425 bytes, checksum: dbb3f23998682d81f6256eb14079175c (MD5) copyright2012 Gang Chen Ultrasound tissue classification feature selection, Level Set Segmentation text Statistics 1583 Statistics 0329 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Statistics -UIUC 10KS0329PHD