Haegler Katrin (15 results)

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  • Language: English

    Published by S?dwestdeutscher Verlag f?r Hochschulschriften, 2012

    3838131711 / 9783838131719

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    PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

  • Language: English

    Published by S?dwestdeutscher Verlag f?r Hochschulschriften, 2012

    3838131711 / 9783838131719

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  • Language: English

    Published by Südwestdeutscher Verlag für Hochschulschriften, 2012

    3838131711 / 9783838131719

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  • Language: English

    Published by Südwestdeutscher Verlag für Hochschulschriften, 2012

    3838131711 / 9783838131719

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  • Language: English

    Published by Südwestdeutscher Verlag für Hochschulschriften, 2012

    3838131711 / 9783838131719

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  • Language: English

    Published by Südwestdeutscher Verlag für Hochschulschriften, 2012

    3838131711 / 9783838131719

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  • Language: English

    Published by Südwestdeutscher Verlag Für Hochschulschriften, 2012

    3838131711 / 9783838131719

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    Seller: Revaluation Books, Exeter, United KingdomRevaluation Books

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    Paperback. Condition: Brand New. 164 pages. 8.66x5.91x0.37 inches. In Stock.

  • Language: English

    Published by Südwestdeutscher Verlag Für Hochschulschriften Mär 2012, 2012

    3838131711 / 9783838131719

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    Taschenbuch. Condition: Neu. Neuware -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.

  • Language: English

    Published by Südwestdeutscher Verlag für Hochschulschriften, 2015

    3838131711 / 9783838131719

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    Taschenbuch. Condition: Neu. 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.

  • Language: English

    Published by Südwestdeutscher Verlag Für Hochschulschriften Mär 2012, 2012

    3838131711 / 9783838131719

    • Softcover

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Taschenbuch. Condition: Neu. Neuware - 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.

  • Language: English

    Published by Südwestdeutscher Verlag Für Hochschulschriften Mär 2012, 2012

    3838131711 / 9783838131719

    • Softcover

    Seller: Books-by-Floh, Paderborn, GermanyBooks-by-Floh

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    Taschenbuch. Condition: Neu. Neuware -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. 164 pp. Englisch.

  • Language: English

    Published by Südwestdeutscher Verlag Für Hochschulschriften Mär 2012, 2012

    3838131711 / 9783838131719

    • Softcover
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    Seller: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermanyRheinberg-Buch Andreas Meier eK

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    Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 164 pp. Englisch.

  • Language: English

    Published by Südwestdeutscher Verlag Für Hochschulschriften Mär 2012, 2012

    3838131711 / 9783838131719

    • Softcover
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    Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 164 pp. Englisch.

  • Language: English

    Published by Südwestdeutscher Verlag für Hochschulschriften, 2012

    3838131711 / 9783838131719

    • Softcover
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    Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Haegler KatrinKatrin 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, Germ.

  • Language: English

    Published by Südwestdeutscher Verlag Für Hochschulschriften Mär 2012, 2012

    3838131711 / 9783838131719

    • Softcover
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    Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000

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    Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Englisch.