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Erschienen in:
Neural Networks – State of Art, Brief History, Basic Models and Architecture Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

MRI Texture-Based Classification of Dystrophic Muscles. A Search for the Most Discriminative Tissue Descriptors

verfasst von : Dorota Duda, Marek Kretowski, Noura Azzabou, Jacques D. de Certaines

Erschienen in: Computer Information Systems and Industrial Management

Verlag: Springer International Publishing

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Abstract

The study assesses the usefulness of various texture-based tissue descriptors in the classification of canine hindlimb muscles. Experiments are performed on T2-weighted Magnetic Resonance Images (MRI) acquired from healthy and Golden Retriever Muscular Dystrophy (GRMD) dogs over a period of 14 months. Three phases of canine growth and/or dystrophy progression are considered. In total, 39 features provided by 8 texture analysis methods are tested. Features are ranked according to their frequency of selection in a modified Monte Carlo procedure. The top-ranked features are used in differentiation (i) between GRMD and healthy dogs at each phase of canine growth, and (ii) between three phases of dystrophy progression in GRMD dogs. Three classifiers are applied: Adaptive Boosting, Neural Networks, and Support Vector Machines. Small sets of selected features (up to 10) are found to ensure highly satisfactory classification accuracies.

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Vorheriges Kapitel Information Density Based Image Binarization for Text Document Containing Graphics
Nächstes Kapitel Detection of Orbital Floor Fractures by Principal Component Analysis
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Metadaten
Titel
MRI Texture-Based Classification of Dystrophic Muscles. A Search for the Most Discriminative Tissue Descriptors
verfasst von
Dorota Duda
Marek Kretowski
Noura Azzabou
Jacques D. de Certaines
Copyright-Jahr
2016
Buch
Computer Information Systems and Industrial Management

Print ISBN: 978-3-319-45377-4

Electronic ISBN: 978-3-319-45378-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-45378-1_11

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