MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution
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24 septembre 2015
- handle: 10670/1.avvi0t
- hal: hal-01444470
- doi: 10.1007/978-3-319-24369-6_21
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-24369-6_21
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Golden Retriever Muscular Dystrophy (GRMD) Duchenne Muscular Dystrophy (DMD) Texture analysis Tissue characterization Muscles Classification Dog MRI T2 Computer-Aided Diagnosis (CAD)Sujets proches
Muscular dystrophies Muscle Musculature Myodynamics Myology Golden retrievers Domestic dog Canis canis Canis domesticus Canis familiarus domesticus Canis lupus familiaris Dog Canis familiarus Methods of analysis Analysis and chemistry Analytical methods Chemical analysis Analysis methods Analysis and examinationCiter ce document
Dorota Duda et al., « MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-319-24369-6_21
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Résumé
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.