Computed Tomography (CT) is a high-precision medical imaging technique that utilizes X-rays and computer reconstruction to provide detailed three-dimensional images of human anatomy. It is used for clinical diagnosis and treatment. Non-ideal scanning conditions often occur, including the presence of metal implants in the human body and limited-angle scanning. These non-ideal conditions result in serious metal artifacts and limited-angle artifacts. To address the above challenge, in this paper, we propose a novel deep dual-domain progressive diffusion network, namely DPD-Net, to jointly suppress metal artifact and limited-angle artifact for the first time. DPD-Net leverages the advantage of dual-domain strategy for limited-angle artifact suppression in image-domain and metal trace inpainting in sinogram-domain simultaneously. To sufficiently solve dual-artifact problem, the dual-domain generative diffusion models are designed for data distribution learning. The proposed DPD-Net is trained and evaluated on a publicly available dataset. Extensive experimental results validate that the proposed method outperforms the state-of-the-art competing methods.
Nowadays vehicle license plate recognition system (VLPRS) has turned out to be a key technique to many automated
transport systems such as road traffic monitoring, automatic highway payment system, automatic traffic video index and
searching. It includes license plate location, license plate characters segmentation and license plate characters
recognition. Fast and successful license plate region extraction is the first and essential step for VLPRS. In real
application environment, many difficulties must be settled, such as the poor image quality caused by uneven lighting
condition and shadow, dust and fading affection and various camera observation angles. In this paper, a vehicle license
plate location method which combines Gentle Adaboost and Harris corner validation is proposed. It is robustness to the
environment illumination variety and suitable to real working environment.
Liver segmentation is critical in designing and developing computer-assisted systems that have been used for liver
disease diagnosis before surgery or transplantation. The purpose of this study is to develop a computerized system for
extracting liver contours and reconstructing liver volume using contrast-enhanced hepatic CT images. The automatic
liver segmentation method adopted the graph optimal algorithm with ratio contour as its salient measure. This new cost
function encoded the Gestalt laws and synthesized the gap length, the liver region area, the length of the closed contour
and the average curvature of the closed boundary. With the extracted liver contours, a promising system to exclude
tissues outside the liver was developed. It promised to save time and simplify liver volume reconstruction by minimizing
intervention operations. Some 3D-rendered reconstruction results were also created to demonstrate the final results of our
system.
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