Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: A focused assistive diagnostic method

Gupta, R. and Elamvazuthi, I. and Dass, S.C. and Faye, I. and Vasant, P. and George, J. and Izza, F. (2014) Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: A focused assistive diagnostic method. BioMedical Engineering Online, 13 (1).

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Background: Disorders of rotator cuff tendons results in acute pain limiting the normal range of motion for shoulder. Of all the tendons in rotator cuff, supraspinatus (SSP) tendon is affected first of any pathological changes. Diagnosis of SSP tendon using ultrasound is considered to be operator dependent with its accuracy being related to operator's level of experience. Methods: The automatic segmentation of SSP tendon ultrasound image was performed to provide focused and more accurate diagnosis. The image processing techniques were employed for automatic segmentation of SSP tendon. The image processing techniques combines curvelet transform and mathematical concepts of logical and morphological operators along with area filtering. The segmentation assessment was performed using true positives rate, false positives rate and also accuracy of segmentation. The specificity and sensitivity of the algorithm was tested for diagnosis of partial thickness tears (PTTs) and full thickness tears (FTTs). The ultrasound images of SSP tendon were taken from medical center with the help of experienced radiologists. The algorithm was tested on 116 images taken from 51 different patients. Results: The accuracy of segmentation of SSP tendon was calculated to be 95.61 in accordance with the segmentation performed by radiologists, with true positives rate of 91.37 and false positives rate of 8.62. The specificity and sensitivity was found to be 93.6, 94 and 95, 95.6 for partial thickness tears and full thickness tears respectively. The proposed methodology was successfully tested over a database of more than 116 US images, for which radiologist assessment and validation was performed. Conclusions: The segmentation of SSP tendon from ultrasound images helps in focused, accurate and more reliable diagnosis which has been verified with the help of two experienced radiologists. The specificity and sensitivity for accurate detection of partial and full thickness tears has been considerably increased after segmentation when compared with existing literature. © 2014 Gupta et al.

Item Type: Article
Impact Factor: cited By 23
Uncontrolled Keywords: Diagnosis; Image processing; Image segmentation; Mathematical morphology; Mathematical operators; Mathematical transformations; Medical imaging; Ultrasonic applications, Automatic segmentations; Curvelet transforms; Image processing technique; Mathematical concepts; Morphological operations; Morphological operator; Supraspinatus tendons; Ultrasound image segmentation, Tendons, adult; Article; automation; controlled study; curve fitting; curvelet transform; diagnostic accuracy; diagnostic imaging; diagnostic test accuracy study; echography; false positive result; human; image analysis; image enhancement; image processing; image segmentation; imaging and display; intermethod comparison; major clinical study; priority journal; rotator cuff rupture; sensitivity and specificity; supraspinatus muscle; supraspinatus tendon tear; algorithm; biomechanics; calcinosis; computer assisted diagnosis; echography; laboratory diagnosis; middle aged; pathology; procedures; radiology; reproducibility; rotator cuff; skeletal muscle; tendon; tendon injury; young adult, Adult; Algorithms; Automation; Biomechanical Phenomena; Calcinosis; Diagnosis, Computer-Assisted; False Positive Reactions; Humans; Image Processing, Computer-Assisted; Middle Aged; Muscle, Skeletal; Radiology; Reproducibility of Results; Rotator Cuff; Tendon Injuries; Tendons; Ultrasonography; Young Adult
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 08:53
Last Modified: 25 Mar 2022 08:53

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