Results 21 to 30 of about 151,110 (308)
Neutrosophic DICOM Image Processing and its applications [PDF]
Neutrosophic Sets and Systems, 2023 Medical images are essential in contemporary medicine because they provide practicable entropy, which is used to diagnose medical conditions. It is useful to visualize abnormality in several parts of the body.
D. Nagarajan, S. Broumi
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Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review [PDF]
Advanced Ultrasound in Diagnosis and Therapy, 2023 Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures.
Jin Guo, MD, Zhaojun Li, PhD, Yanping Lin, PhD
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Medical Images Segmentation Operations [PDF]
Proceedings of the Institute for System Programming of the RAS, 2018 Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts.
S. A. Musatian +5 more
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Brain Image Segmentation Based on Fuzzy Clustering
Al-Mustansiriyah Journal of Science, 2018 The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not ...
Mohammed Y. Kamil
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Digital Medical Image Segmentation Using Fuzzy C-Means Clustering
UHD Journal of Science and Technology, 2020 In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases.
Bakhtyar Ahmed Mohammed +1 more
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BMC Medical Imaging, 2021 Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical ...
Dominik Müller, Frank Kramer
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Frontiers in Public Health, 2022 To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using ...
Weiwei Gao +3 more
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DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling
NeuroImageThis study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational ...
Zongren Li +3 more
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U-Net-Based Medical Image Segmentation
Journal of Healthcare Engineering, 2022 Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the performance of segmentation in medical imaging in recent years, U-Net has been cited academically more than ...
Xiao-Xia Yin +4 more
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