Results 271 to 280 of about 169,019 (308)
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Novel segmentation algorithm in segmenting medical images
Journal of Systems and Software, 2010The aim of this paper is to develop an effective fuzzy c-means (FCM) technique for segmentation of Magnetic Resonance Images (MRI) which is seriously affected by intensity inhomogeneities that are created by radio-frequency coils. The weighted bias field information is employed in this work to deal the intensity inhomogeneities during the segmentation ...
S. R. Kannan +3 more
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Segmentation of medical images using LEGION
IEEE Transactions on Medical Imaging, 1999Advances in visualization technology and specialized graphic workstations allow clinicians to virtually interact with anatomical structures contained within sampled medical-image datasets. A hindrance to the effective use of this technology is the difficult problem of image segmentation.
Naeem Shareef +2 more
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Pairwise learning for medical image segmentation
Medical Image Analysis, 2021Fully convolutional networks (FCNs) trained with abundant labeled data have been proven to be a powerful and efficient solution for medical image segmentation. However, FCNs often fail to achieve satisfactory results due to the lack of labelled data and significant variability of appearance in medical imaging.
Renzhen Wang +4 more
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Transformers in medical image segmentation: A review
Biomedical Signal Processing and Control, 2023Hanguang Xiao
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SEGMENTATION OF MEDICAL IMAGES
Herald of Khmelnytskyi National University. Technical sciences, 2020Segmentation is an integral part of the digital image processing process. It is the division or division of the image into some parts that meet the specified characteristics and characterize these areas and the image as a whole. At the segmentation stage, issues are solved that complement the standard tasks of image processing, namely coding ...
V. MOSTOVYI, S. HORIASHCHENKO
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Intuitionistic Fuzzy Segmentation of Medical Images
IEEE Transactions on Biomedical Engineering, 2010This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates ...
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Advanced Materials Research, 2013
Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms.
Bing Song He, Feng Zhu, Yong Gang Shi
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Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms.
Bing Song He, Feng Zhu, Yong Gang Shi
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2019
Today, IoT in therapeutic administrations has ended up being more productive in light of the fact that the correspondence among authorities and patients has been improved with versatile applications. These applications are made by the associations with the objective that the pros can screen the patient's prosperity.
Ramgopal Kashyap, Surendra Rahamatkar
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Today, IoT in therapeutic administrations has ended up being more productive in light of the fact that the correspondence among authorities and patients has been improved with versatile applications. These applications are made by the associations with the objective that the pros can screen the patient's prosperity.
Ramgopal Kashyap, Surendra Rahamatkar
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Evaluation of Segmentation algorithms for Medical Imaging
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005This paper describes an approach to be used for medical image segmentation evaluation. The process for segmenting organs and structures from medical images is gaining increased importance in the diagnosis of diseases and in guiding minimally invasive surgical and therapeutic procedures.
Aaron, Fenster, Bernard, Chiu
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2011
This chapter presents a new and efficient unsupervised color segmentation scheme named GBOD to detect visual objects from medical color images and to extract their color and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis.
Liana Stanescu +3 more
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This chapter presents a new and efficient unsupervised color segmentation scheme named GBOD to detect visual objects from medical color images and to extract their color and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis.
Liana Stanescu +3 more
openaire +1 more source