A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images.

Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.

Tendon-motion tracking in an ultrasound image sequence using optical-flow-based block matching.

BACKGROUND: Tendon motion, which is commonly observed using ultrasound imaging, is one of the most important features used in tendinopathy diagnosis. However, speckle noise and out-of-plane issues make the tracking process difficult. Manual tracking is usually time consuming and often yields inconsistent results between users. METHODS: To automatically track tendon motion in ultrasound images, we developed a new method that combines the advantages of optical flow and multi-kernel block matching. For every pair of adjacent image frames, the optical flow is computed and used to estimate the accumulated displacement. The proposed method selects the frame interval adaptively based on this displacement. Multi-kernel block matching is then computed on the two selected frames, and, to reduce tracking errors, the detailed displacements of the frames in between are interpolated based on the optical flow results. RESULTS: In the experiments, cadaver data were used to evaluate the tracking results. The mean absolute error was less than 0.05 mm. The proposed method also tracked the motion of tendons in vivo, which provides useful information for clinical diagnosis. CONCLUSION: The proposed method provides a new index for adaptively determining the frame interval. Compared with other methods, the proposed method yields tracking results that are significantly more accurate.

Identification of the Position and Thickness of the First Annular Pulley in Sonographic Images.

The purpose was to identify the A1 pulley's exact location and thickness by comparing measurements from a clinical high-frequency ultrasound scanner system (CHUS), a customized high-frequency ultrasound imaging research system (HURS) and a digital caliper. Ten cadaveric hands were used. We explored the pulley by layers, inserted guide pins and scanned it with the CHUS. After identifying the pulley, we measured each long finger's thickness using the CHUS and excised the pulley to measure its thickness with a digital caliper and the HURS. The thin hypo-echoic layer was revealed to be the synovial fluid space, and the pulley appears hyper-echoic regardless of scan direction. We also defined the pulley's boundaries. Moreover, the CHUS provided a significantly lower measurement of the pulley's thickness than the digital caliper and HURS. Likewise, based on the digital caliper's measurement, the HURS had significantly lower mean absolute and relative errors than the CHUS.