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首页> 外文会议>International Conference on Computer Information Systems and Industrial Management >Multi-muscle Texture Analysis for Dystrophy Development Identification in Golden Retriever Muscular Dystrophy Dogs
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Multi-muscle Texture Analysis for Dystrophy Development Identification in Golden Retriever Muscular Dystrophy Dogs

机译：营养不良肌营养不良犬营养不良发展鉴定的多肌纹理分析

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摘要

The study assesses the suitability of multi-muscle texture analysis (TA) for the dystrophy development characterization in Golden Retriever Muscular Dystrophy (GRMD) dogs. Textural features, statistical and model-based, are derived from T2-weighted Magnetic Resonance Images (MRI) of canine hindlimb muscles. Features obtained from different types of muscles (EDL, GasLat, GasMed, and TC) are analyzed simultaneously. Four phases of dystrophy progression, including the "zero phase" - the absence of the disease, are differentiated. Two classifiers are applied: Support Vector Machines (SVM) and Adaptive Boosting (AdaBoost). A Monte Carlo-based feature selection enables to find features (and the corresponding muscle types) that are the most useful in identifying the phase of dystrophy. The simultaneous consideration of several muscles improves the classification accuracy by maximum 12.5% in comparison to the best corresponding result achieved with single-muscle TA. A combination of 17 textural features derived from different types of muscles provides a classification accuracy of approximately 82%.
机译：该研究评估了多肌纹理分析（TA）在金毛肌营养不良（GRMD）犬的营养不良发展表征中的适用性。基于T2加权磁共振图像（MRI）的纹理特征，统计和模型基于罐头后肢肌肉。同时分析从不同类型的肌肉（EDL，GaslAT，Gassmed和Tc）获得的特征。营养不良进展的四个阶段，包括“零期” - 缺乏疾病，是不同的。应用两个分类器：支持向量机（SVM）和自适应升压（Adaboost）。基于蒙特卡罗的特征选择使得能够找到最有用的特征（和相应的肌肉类型）在识别营养不良阶段的阶段。与单肌TA实现的最佳相应结果相比，对几个肌肉的同时考虑最高12.5％。来自不同类型肌肉的17个纹理特征的组合提供了大约82％的分类精度。

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