Healthcare Informatics Research · 2016 · 38 citations · 6 references
ComputingOrthopaedic SurgeryOrthopedic SurgeryDiagnostic ImagingShoulder SurgeryUltrasonographyComputer-aided DiagnosisImage ClassificationMedical Image AnalysisPattern RecognitionSupport Vector MachinesTexture AnalysisAccuracyGray-level Co-occurrence MatricesRotator Cuff UltrasonographyShoulder UltrasoundStatistical Texture AnalysisSupport Vector ClassificationSupport Vector Machine ClassificationTexture Classification
OBJECTIVES: In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM). METHODS: -test. The support vector machine classification showed accuracy using feature combinations. Support Vector Machine offers good performance with a small amount of training data. Sensitivity, specificity, and accuracy are used to evaluate performance of a classification test. RESULTS: From the results, first order statics features and GLCM and GLRLM features afford 95%, 85%, and 100% accuracy, respectively. First order statistics and GLCM and GLRLM features in combination provided 100% accuracy. Combinations that include GLRLM features had high accuracy. GLRLM features were confirmed as highly accurate features for classified normal and abnormal. CONCLUSIONS: This algorithm will be helpful to diagnose supraspinatus tendon tear on ultrasound images.
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Michael Mavroforakis, Harris Georgiou, Nikos Dimitropoulos et al. · Artificial Intelligence in Medicine · 2006 · 118 citations