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.
BackgroundTendon 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.MethodsTo 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.ResultsIn 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0335-x) contains supplementary material, which is available to authorized users.
In billet production, the quality of billet is an important issue to assure. In this study, we proposed an automatic inspection system to locate and classify defects for billet. The proposed system is consisted of three modules: (1) image processing and defect location, (2) feature extraction and selection, (3) incremental learning classifier. In the first module, the region of interest is extracted and normalized to reduce the effects of uneven illumination. We then develop two methods to detect different types of defects based on their characteristics. In the second module, k-nearest neighbor classifier and tabu search are employed to select the best set of features for classification. In the last module, a classifier with incremental learning capability called Learn++ is used to classify the detected defects. Experiments show that the proposed system provides defect detection with good accuracy and speed. Comparing with the conventional BPN, the Learn++ classifier is much more efficient in training and obtains better classification rates.
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