Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System
| Autor: | Sun Kook Yoo, Won Seuk Jang, Byung Eun Park |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Computer applications to medicine. Medical informatics Biomedical Engineering R858-859.7 Health Informatics 030218 nuclear medicine & medical imaging Correlation 03 medical and health sciences 0302 clinical medicine Health Information Management Region of interest Histogram medicine Entropy (information theory) computer-assisted image analysis Rotator cuff Computer vision support vector machine business.industry ultrasonography rotator cuff Support vector machine statistical data analyses medicine.anatomical_structure Skewness 030220 oncology & carcinogenesis Computer-aided Original Article Artificial intelligence business |
| Zdroj: | Healthcare Informatics Research, Vol 22, Iss 4, Pp 299-304 (2016) Healthcare Informatics Research |
| ISSN: | 2093-3681 |
| Popis: | 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 First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest. Our results show that first order statistics (mean, skewness, entropy, energy, smoothness), GLCM (correlation, contrast, energy, entropy, difference entropy, homogeneity, maximum probability, sum average, sum entropy), and GLRLM features are helpful to distinguish a normal supraspinatus tendon and an abnormal supraspinatus tendon. The statistical significance of these features is verified using a t-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. |
| Databáze: | OpenAIRE |
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