Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy

Aydin Eresen, Lejla Alic, Sharla M. Birch, Wade Friedeck, John F. Griffin, Joe N. Kornegay, J. I. Jim X.

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)


Introduction: Golden retriever muscular dystrophy (GRMD), an X-linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD. Methods: To assess severity in the GRMD, we analyzed texture features extracted from multi-parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines. Results: A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan-time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort. Conclusions: The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes. Muscle Nerve 59:380–386, 2019.

Original languageEnglish
Pages (from-to)380-386
Number of pages7
JournalMuscle and Nerve
Issue number3
Early online date21 Nov 2018
Publication statusPublished - Mar 2019
Externally publishedYes


  • DMD
  • GRMD
  • imaging biomarkers
  • machine learning
  • texture analysis


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