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.
| Autorius: | Ying Cui |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2009 |
| Knygos puslapių skaičius: | 160 |
| ISBN-10: | 3838300807 |
| ISBN-13: | 9783838300801 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „High Dimensional Clustering and Applications of Learning Methods: Non-Redundant Clustering, Principal Feature Selection and Learning Methods Applied to Image- Guided Radiotherapy“
