%0 Conference Proceedings
%T MRI Texture-Based Classification of Dystrophic Muscles. A Search for the Most Discriminative Tissue Descriptors
%+ Białystok University of Technology
%+ Institute of Myology, Nuclear Magnetic Resonance Laboratory
%+ Institut d'Imagerie BioMédicale (I2BM)
%+ Molecular Imaging Research Center [Fontenay-aux-Roses] (MIRCEN)
%A Duda, Dorota
%A Kretowski, Marek
%A Azzabou, Noura
%A Certaines, Jacques
%Z Part 3: Images, Visualization, Classification
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Vilnius, Lithuania
%Y Khalid Saeed
%Y Władysław Homenda
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-9842
%P 116-128
%8 2016-09-14
%D 2016
%R 10.1007/978-3-319-45378-1_11
%K Golden Retriever Muscular Dystrophy (GRMD)
%K Duchenne Muscular Dystrophy (DMD)
%K Texture analysis
%K Feature selection
%K Classification
%K MRI T2
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01637453/document
%2 https://inria.hal.science/hal-01637453/file/419526_1_En_11_Chapter.pdf
%L hal-01637453
%U https://inria.hal.science/hal-01637453
%~ SHS
%~ CEA
%~ CNRS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ CEA-UPSAY
%~ IFIP-TC8
%~ IFIP-CISIM
%~ INSERM-SACLAY
%~ UNIV-PARIS-SACLAY
%~ CEA-UPSAY-SACLAY
%~ IFIP-LNCS-9842
%~ JACOB
%~ CEA-DRF
%~ MIRCEN