High Dimensional Clustering and Applications of Learning Methods

High Dimensional Clustering and Applications of Learning Methods

Non-Redundant Clustering, Principal Feature Selection and Learning Methods Applied to Image- Guided Radiotherapy

LAP Lambert Academic Publishing ( 2009-04-23 )

€ 59,00

Buy at the MoreBooks! Shop

This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple- template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle.

Book Details:

ISBN-13:

978-3-8383-0080-1

ISBN-10:

3838300807

EAN:

9783838300801

Book language:

English

By (author) :

Ying Cui

Number of pages:

160

Published on:

2009-04-23

Category:

Informatics, IT