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Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System

Authors
 Byung Eun Park  ;  Won Seuk Jang  ;  Sun Kook Yoo 
Citation
 HEALTHCARE INFORMATICS RESEARCH, Vol.22(4) : 299-304, 2016 
Journal Title
 HEALTHCARE INFORMATICS RESEARCH 
ISSN
 2093-3681 
Issue Date
2016
Keywords
Computer-Assisted Image Analysis ; Rotator Cuff ; Statistical Data Analyses ; Support Vector Machine ; Ultrasonography
Abstract
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.
Files in This Item:
T201605351.pdf Download
DOI
10.4258/hir.2016.22.4.299
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
Chang, Won Seok(장원석) ORCID logo https://orcid.org/0000-0002-4925-3549
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/152784
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