Application of a novel Kalman filter based block matching method to ultrasound images for hand tendon displacement estimation. Issue 1 (22nd December 2015)

• Record Type: Journal Article
• Authors: Lai, Ting‐Yu; Chen, Hsiao‐I; Shih, Cho‐Chiang; Kuo, Li‐Chieh; Hsu, Hsiu‐Yun; Huang, Chih‐Chung
• Is Part Of: Medical physics. Volume 43:Issue 1(2016)
• Page Start: 148
• Page End: 158
• Publication Date: 2015-12-22
• Subjects: biological tissues -- biomedical ultrasonics -- image matching -- Kalman filters -- medical image processing -- patient rehabilitation -- phantoms; Ultrasonographic imaging -- Noise; Diagnosis using ultrasonic, sonic or infrasonic waves -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; ultrasound image -- tendon tracking -- Kalman‐filter -- Fisher‐Tippett similarity measure -- rehabilitation; Ultrasonography -- Medical image noise -- Selected area electron diffraction -- Ultrasonic transducers -- Ultrasonic velocity -- Tissues -- Experiment design -- Adhesion; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4937932
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Information about tendon displacement is important for allowing clinicians to not only quantify preoperative tendon injuries but also to identify any adhesive scaring between tendon and adjacent tissue. The Fisher‐Tippett (FT) similarity measure has recently been shown to be more accurate than the Laplacian sum of absolute differences (SAD) and Gaussian sum of squared differences (SSD) similarity measures for tracking tendon displacement in ultrasound B‐mode images. However, all of these similarity measures can easily be influenced by the quality of the ultrasound image, particularly its signal‐to‐noise ratio. Ultrasound images of injured hands are unfortunately often of poor quality due to the presence of adhesive scars. The present study investigated a novel Kalman‐filter scheme for overcoming this problem. Methods: Three state‐of‐the‐art tracking methods (FT, SAD, and SSD) were used to track the displacements of phantom and cadaver tendons, while FT was used to track human tendons. These three tracking methods were combined individually with the proposed Kalman‐filter (K1) scheme and another Kalman‐filter scheme used in a previous study to optimize the displacement trajectories of the phantom and cadaver tendons. The motion of the human extensor digitorum communis tendon was measured in the present study using the FT‐K1 scheme. Results: The experimental results indicated that SSD exhibited better accuracy in the phantom experiments, whereas FT exhibited better performance for tracking real tendon motion in the cadaver experiments. All three tracking methods were influenced by the signal‐to‐noise ratio of the images. On the other hand, the K1 scheme was able to optimize the tracking trajectory of displacement in all experiments, even from a location with a poor image quality. The human experimental data indicated that the normal tendons were displaced more than the injured tendons, and that the motion ability of the injured tendon was restored after appropriate rehabilitation sessions. Conclusions: The obtained results show the potential for applying the proposed FT‐K1 method in clinical applications for evaluating the tendon injury level after metacarpal fractures and assessing the recovery of an injured tendon during rehabilitation.