MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution - Computer Information Systems and Industrial Management
Conference Papers Year : 2015

MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution

Abstract

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.
Fichier principal
Vignette du fichier
978-3-319-24369-6_21_Chapter.pdf (294.4 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01444470 , version 1 (24-01-2017)

Licence

Identifiers

Cite

Dorota Duda, Marek Kretowski, Noura Azzabou, Jacques De Certaines. MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution. 14th Computer Information Systems and Industrial Management (CISIM), Sep 2015, Warsaw, Poland. pp.255-266, ⟨10.1007/978-3-319-24369-6_21⟩. ⟨hal-01444470⟩
327 View
264 Download

Altmetric

Share

More