X-ray microtomography from cold-sprayed coatings brings a new insight on this deposition process. A noise-tolerant segmentation algorithm is introduced, based on the combination of two segmentations: a deterministic multiscale segmentation and a stochastic segmentation. The stochastic approach uses random Poisson lines as markers. Results on a X-ray microtomographic image of aluminium particles are presented and validated.
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http://dx.doi.org/10.1111/j.1365-2818.2012.03655.x | DOI Listing |
J Clin Med
January 2025
Centre of Excellence for Sustainable Living and Working (SustAInLivWork), 51423 Kaunas, Lithuania.
: This study focuses on the critical task of blood vessel segmentation in medical image analysis, essential for diagnosing cardiovascular diseases and enabling effective treatment planning. Although deep learning architectures often produce very high segmentation results in medical images, coronary computed tomography angiography (CTA) images are more challenging than invasive coronary angiography (ICA) images due to noise and the complexity of vessel structures. : Classical architectures for medical images, such as U-Net, achieve only moderate accuracy, with an average Dice score of 0.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Medical Device and Healthcare, Dongguk University, Seoul 04620, Republic of Korea.
Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method.
View Article and Find Full Text PDFJ Imaging
January 2025
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213000, China.
Manual labeling of lesions in medical image analysis presents a significant challenge due to its labor-intensive and inefficient nature, which ultimately strains essential medical resources and impedes the advancement of computer-aided diagnosis. This paper introduces a novel medical image-segmentation framework named Efficient Generative-Adversarial U-Net (EGAUNet), designed to facilitate rapid and accurate multi-organ labeling. To enhance the model's capability to comprehend spatial information, we propose the Global Spatial-Channel Attention Mechanism (GSCA).
View Article and Find Full Text PDFEntropy (Basel)
January 2025
College of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China.
During the rice harvesting process, severe occlusion and adhesion exist among multiple targets, such as rice, straw, and leaves, making it difficult to accurately distinguish between rice grains and impurities. To address the current challenges, a lightweight semantic segmentation algorithm for impurities based on an improved SegFormer network is proposed. To make full use of the extracted features, the decoder was redesigned.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Optical Coherence Tomography (OCT) is a crucial imaging modality for diagnosing and monitoring retinal diseases. However, the accurate segmentation of fluid regions and lesions remains challenging due to noise, low contrast, and blurred edges in OCT images. Although feature modeling with wide or global receptive fields offers a feasible solution, it typically leads to significant computational overhead.
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