A case of a mobile intrathoracic foreign object

Penetrating chest trauma to children is rare in the UK, making up 0.8% of wounds to children. When it does occur, it often results in damage to mediastinal structures including but not limited to the heart, lungs and great vessels. Rarely foreign objects can be intrapericardial. We present the case of a 14-year-old boy who presented haemodynamically stable following pellet gun wound to the chest. Multi-modality imaging revealed the bullet to be in the pericardium without associated cardiothoracic injuries, confirmed following surgery. Although a multi-modality imaging approach was used in diagnosing the precise location of the gun pellet, including imaging involving ionising radiation, we argue that early localisation can potentially be achieved with initial imaging and basic anatomical correlation, reducing the time to diagnosis. Using all the images available, including CT scout images, can assist in localisation and identifying important negatives.

Download Full-text

Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽

10.3390/s21165279 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5279

Author(s):

Dong-Hoon Kwak ◽

Guk-Jin Son ◽

Mi-Kyung Park ◽

Young-Duk Kim

Keyword(s):

Object Detection ◽

Real Time ◽

Hyperspectral Imaging ◽

High Speed ◽

Detection System ◽

Imaging Techniques ◽

Foreign Object ◽

Subtraction Method ◽

Foreign Objects ◽

Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

Download Full-text

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

Journal of Imaging ◽

10.3390/jimaging7070104 ◽

2021 ◽

Vol 7 (7) ◽

pp. 104

Author(s):

Vladyslav Andriiashen ◽

Robert van Liere ◽

Tristan van Leeuwen ◽

Kees Joost Batenburg

Keyword(s):

Object Detection ◽

Meat Products ◽

Food Product ◽

Processing Technique ◽

Dual Energy ◽

Foreign Object ◽

Metal Debris ◽

X Ray ◽

Foreign Objects ◽

X Ray Imaging

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

Download Full-text