%0 Conference Proceedings
%T MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution
%+ Białystok University of Technology
%+ Institute of Myology, Nuclear Magnetic Resonance Laboratory
%+ Molecular Imaging Research Center [Fontenay-aux-Roses] (MIRCEN)
%A Duda, Dorota
%A Kretowski, Marek
%A Azzabou, Noura
%A Certaines, Jacques, De
%Z Part 4: Data Analysis and Information Retrieval
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 14th Computer Information Systems and Industrial Management (CISIM)
%C Warsaw, Poland
%Y Khalid Saeed
%Y Władysław Homenda
%I Springer
%3 Computer Information Systems and Industrial Management
%V LNCS-9339
%P 255-266
%8 2015-09-24
%D 2015
%R 10.1007/978-3-319-24369-6_21
%K Golden Retriever Muscular Dystrophy (GRMD)
%K Duchenne Muscular Dystrophy (DMD)
%K Texture analysis
%K Tissue characterization
%K Muscles
%K Classification
%K Dog
%K MRI T2
%K Computer-Aided Diagnosis (CAD)
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X In this study, a texture analysis is applied to T2-weighted Magnetic Resonance Images (MRI) of canine pelvic limbs in order to differentiate between Golden Retriever Muscular Dystrophy (GRMD) dogs and healthy ones. The differentiation is performed at three phases of canine growth and/or disease development: 2-4 months (the first phase), 5-6 months (the second phase), and 7 months and more (the third phase). Eight feature extraction methods (statistical, model-based, and filter-based) and five classifiers are tested. Four types of muscles are analyzed: the Extensor Digitorum Longus (EDL), the Gastrocnemius Lateralis (GasLat), the Gastrocnemius Medialis (GasMed) and the Tibial Cranialis (TC). The experiments were performed on five healthy and five GRMDdogs. Each of themuscles was considered separately. The best classification results were 95.81% (the EDL muscle), 97.19% (GasLat), and 91.37% (EDL) correctly recognized cases, for the first, second and third phase, respectively. These results were obtained with an SVM classifier.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01444470/document
%2 https://inria.hal.science/hal-01444470/file/978-3-319-24369-6_21_Chapter.pdf
%L hal-01444470
%U https://inria.hal.science/hal-01444470
%~ SHS
%~ CEA
%~ CNRS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ CEA-UPSAY
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-9339
%~ INSERM-SACLAY
%~ UNIV-PARIS-SACLAY
%~ CEA-UPSAY-SACLAY
%~ JACOB
%~ CEA-DRF
%~ MIRCEN