3D ultrasound reconstruction of sonographic callus

Aims The aim of this study was to establish a reliable method for producing 3D reconstruction of sonographic callus. Methods A cohort of ten closed tibial shaft fractures managed with intramedullary nailing underwent ultrasound scanning at two, six, and 12 weeks post-surgery. Ultrasound capture was performed using infrared tracking technology to map each image to a 3D lattice. Using echo intensity, semi-automated mapping was performed to produce an anatomical 3D representation of the fracture site. Two reviewers independently performed 3D reconstructions and kappa coefficient was used to determine agreement. A further validation study was undertaken with ten reviewers to estimate the clinical application of this imaging technique using the intraclass correlation coefficient (ICC). Results Nine of the ten patients achieved union at six months. At six weeks, seven patients had bridging callus of ≥ one cortex on the 3D reconstruction and when present all achieved union. Compared to six-week radiographs, no bridging callus was present in any patient. Of the three patients lacking sonographic bridging callus, one went onto a nonunion (77.8% sensitive and 100% specific to predict union). At 12 weeks, nine patients had bridging callus at ≥ one cortex on 3D reconstruction (100%-sensitive and 100%-specific to predict union). Presence of sonographic bridging callus on 3D reconstruction demonstrated excellent reviewer agreement on ICC at 0.87 (95% confidence interval 0.74 to 0.96). Conclusion 3D fracture reconstruction can be created using multiple ultrasound images in order to evaluate the presence of bridging callus. This imaging modality has the potential to enhance the usability and accuracy of identification of early fracture healing. Cite this article: Bone Joint Res 2021;10(12):759–766.

DESIGN AND IMPLEMENTATION OF ANTI-TANK GUIDED-MISSILE (ATGM) CONTROL SYSTEM USING SEMI-AUTOMATIC COMMAND LINE OF SIGHT (SACLOS) METHOD BASED ON DIGITAL IMAGE PROCESSING

<div><p class="Els-history-head">The Anti-Tank Guided-Missile (ATGM) system has a very important role in the modern battlefield. This system proved its effectiveness in many modern conflicts such as the Syrian Civil War and Nagorno-Karabakh War. The ATGM system has a very simple electronic and mechanism but it has a very high level of accuracy and precision. One of the control methods used in ATGM is SACLOS method. This method tracks missile position by detecting an infrared lamp that is placed on the missile tail. The tracking system sends control signals to the missile as a result of the correction of the missile position when flying. The infrared tracking system in this research was made using a modified OV5647 camera with the addition of a 940 nm narrow bandpass filter. There are 3 cameras with 1x, 8x, and 16x magnifications which are accessed using 3 Raspberry Pi boards. X and y coordinate data of the infrared lamp is sent to the airframe using wireless telemetry. Atmega328 microcontroller process x and y coordinate data into input proportional control. The result of this research is the prototype of an anti-tank missile control system with an infrared tracking instrument capable track a series of 88 infrared LEDs as far as 997.16 meters with a tracking speed of 90.11 FPS. The threshold parameters of image processing using luminance of YUV color space has a range of 240-255. The control parameter Kp=7 is used in wind tunnel testing with airspeed 20 m/s capable of directing airframe motion to the telescope's crosshairs.</p></div>

Multi-Task Driven Feature Models for Thermal Infrared Tracking

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR tracking. Specifically, we first use an auxiliary classification network to guide the generation of TIR-specific discriminative features for distinguishing the TIR objects belonging to different classes. Second, we design a fine-grained aware module to capture more subtle information for distinguishing the TIR objects belonging to the same class. These two kinds of features complement each other and recognize TIR objects in the levels of inter-class and intra-class respectively. These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task. In addition, we develop a large-scale TIR training dataset to train the network for adapting the model to the TIR domain. Extensive experimental results on three benchmarks show that the proposed algorithm achieves a relative gain of 10% over the baseline and performs favorably against the state-of-the-art methods. Codes and the proposed TIR dataset are available at https://github.com/QiaoLiuHit/MMNet.