Similarity Search in Medical Data | Automatic differentiation between low- and high-grade brain tumors

Katrin Haegler

ISBN 10: 3838131711 / ISBN 13: 9783838131719

Published by Südwestdeutscher Verlag für Hochschulschriften, 2015

About this Item

Similarity Search in Medical Data | Automatic differentiation between low- and high-grade brain tumors | Katrin Haegler | Taschenbuch | 164 S. | Englisch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838131719 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106598453

Bibliographic Details

• Title: Similarity Search in Medical Data | Automatic differentiation between low- and high-grade brain tumors
• Publisher: Südwestdeutscher Verlag für Hochschulschriften
• Publication Date: 2015
• Language: English
• ISBN 10: 3838131711
• ISBN 13: 9783838131719
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 11D millimeters 220 millimeters width by 150 millimeters height by 11 millimeters depth
• Weight: 262 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

At present, an adequate therapy planning of newly detected brain tumors needs invasive biopsy due to the fact that prognosis and treatment, both vary strongly for different tumor grades. To assist neuroradiologist experts during the differentiation between tumors of different malignancy a novel, efficient similarity search technique for uncertain data in combination with a technique for parameter-free outlier detection is proposed. Previous work is limited to axis-parallel Gaussian distributions, hence, correlations between different features are not considered in these similarity searches. In this work a similarity search technique for general Gaussian Mixture Models (GMMs) without independence assumption is presented. The actual components of a GMM are approximated in a conservative but tight way. The conservativity of the approach leads to a filter-refinement architecture, which guarantees no false dismissals and the tightness of the approximations causes good filter selectivity. Promising results for advancing the differentiation between brain tumors of different grades could be obtained by applying the approach to four-dimensional Magnetic Resonance Images of glioma patients.

About the Author

Katrin Haegler, Dr.: Studies of Bioinformatics at Ludwig-Maximilians (LMU) University and Technical University Munich. PhD studentship in Computer Sience at the LMU Munich. Core software engineer at SEP AG, Weyarn, Germany.