Author : Thanh Bui Minh
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (963 download)
Book Synopsis Statistical Modeling, Level-set and Ensemble Learning for Automatic Segmentation of 3D High-frequency Ultrasound Data by : Thanh Bui Minh
Download or read book Statistical Modeling, Level-set and Ensemble Learning for Automatic Segmentation of 3D High-frequency Ultrasound Data written by Thanh Bui Minh and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work investigates approaches to obtain automatic segmentation of three media (i.e., lymph node parenchyma, perinodal fat and normal saline) in lymph node (LN) envelope data to expedite quantitative ultrasound (QUS) in dissected LNs from cancer patients. A statistical modeling study identified a two-parameter gamma distribution as the best model for data from the three media based on its high fitting accuracy, its analytically less-complex probability density function (PDF), and closed-form expressions for its parameter estimation. Two novel level-set segmentation methods that made use of localized statistics of envelope data to handle data inhomogeneities caused by attenuation and focusing effects were developed. The first, local region-based gamma distribution fitting (LRGDF), employed the gamma PDFs to model speckle statistics of envelope data in local regions at a controllable scale using a smooth function with a compact support. The second, statistical transverse-slice-based level-set (STS-LS), used gamma PDFs to locally model speckle statistics in consecutive transverse slices. A novel method was then designed and evaluated to automatically initialize the LRGDF and STS-LS methods using random forest classification with new proposed features. Methods developed in this research provided accurate, automatic and efficient segmentation results on simulated envelope data and data acquired for LNs from colorectal- and breast-cancer patients as compared with manual expert segmentation. Results also demonstrated that accurate QUS estimates are maintained when automatic segmentation is applied to evaluate excised LN data.