Results 31 to 40 of about 18,646,034 (305)
A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential
IEEE Access, 2019 Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium.
Frank Kulwa +7 more
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European Radiology Experimental, 2020 Background Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets ...
Johannes Hofmanninger +5 more
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MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation [PDF]
, 2007 This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image analysis, thus we approach this task as a machine-learning problem ...
Julio Villena-Román +7 more
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Automated Disease Detection in Gastroscopy Videos Using Convolutional Neural Networks
Frontiers in Medicine, 2022 A large percentage of the world's population is affected by gastric diseases ranging from erosion and ulcer to serious ailments such as gastric cancer, which is mainly caused by Helicobacter pylori(H.pylori) infection.
Chenxi Zhang +6 more
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Distance Regularized Level Set Evolution for Medical Image Segmentation [PDF]
, 2013 Medical image is an important tool because it can be used for surgical planning and simulation, radiotherapy planning, and tracking the progress of disease. To analyze the medical image, it must partitioned into different segment using image segmentation
Pranowo, Pranowo, Rianto , Indra
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Medical Image Processing on the GPU : Past, Present and Future [PDF]
, 2013 Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient.
Eklund, Anders, +7 more
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Evaluating Knowledge Transfer in the Neural Network for Medical Images
IEEE Access, 2023 The performance of deep learning models, such as convolutional neural networks (CNN)s, is highly dependent on the size of the training dataset. Consequently, it can be challenging to achieve satisfactory performance when training models from scratch in ...
S. Akbarian +3 more
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Efficient reconfigurable architectures for 3D medical image compression [PDF]
, 2010 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT ...
Afandi, Ahmad
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MEDICAL IMAGE ENCRYPTION USING DNA ENCODING AND MODIFIED CIRCULAR SHIFT
Barekeng, 2022 This paper proposes a new encryption method for the encryption of medical images. The method is used to divide the image into several blocks and then scramble the image blocks using DNA chains and then shift the pixels in a circle with certain rules.
Kiswara Agung Santoso +2 more
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Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification
Applied Sciences, 2022 The rising global incidence of chronic kidney disease necessitates the development of image categorization of renal glomeruli. COVID-19 has been shown to enter the glomerulus, a tissue structure in the kidney.
Hsi-Chieh Lee, Ahmad Fauzan Aqil
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