Fig 1.
Supraspinatus tendon shown in ultrasound images.
(a) A case of tendon tendinopathy. (b) A case of supraspinatus tear. (c) and (d): The lesion contours of (a) and (b), respectively, which were delineated by a shoulder orthopedic surgeon using ImageJ.
Fig 2.
The illustration of texture analysis considering neighboring pixel pairs of four directions: 0°, 45°, 90°, and 135° and distance = 1.
Table 1.
The test results of intensity features using the Mann-Whitney U-test.
Table 2.
The test results of texture features using student’s t-test (mean) or the Mann-Whitney U-test (median).
Table 3.
The performance comparisons of intensity features, texture features, and the combination of both feature sets.
Fig 3.
Tear classification results with probabilities higher than 50% were classified to be tear.
(a) a moderate supraspinatus near full thickness tear with unobvious characteristics in the ultrasound image (hypoechoic area near the tendon insertion indicated by a white arrow) was misclassified by the texture feature set (42%) but correctly classified by the combination of texture and intensity feature sets (100%). (b) a small supraspinatus partial thickness tear at bursal surface was misclassified by the intensity feature set (9%) but correctly classified by the combination of texture and intensity feature sets (68%).
Fig 4.
The trade-offs between the sensitivity and specificity of the computer-aided tear classification system using different feature sets are illustrated by receiver operating characteristic curves.
(“Moment” is referred to intensity model; “GLCM” is referred to texture model; and “Moment + GLCM” is referred to a combined model.)