Hybrid imaging modalities combine two or more medical imaging techniques offering exciting new possibilities to image the structure, function and biochemistry of the human body in far greater detail than has previousl...
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Hybrid imaging modalities combine two or more medical imaging techniques offering exciting new possibilities to image the structure, function and biochemistry of the human body in far greater detail than has previously been possible to improve patient diagnosis. In this context, simultaneous Positron Emission Tomography and Magnetic Resonance (PET/MR) imaging offers great complementary information, but it also poses challenges from the point of view of hardware and software compatibility. The PET signal may interfere with the MR magnetic field and vice -versa, posing several challenges and constrains in the PET instrumentation for PET/MR systems. Additionally, anatomical maps are needed to properly apply attenuation and scatter corrections to the resulting reconstructed PET images, as well motion estimates to minimize the effects of movement throughout the acquisition. In this review, we summarize the instrumentation implemented in modern PET scanners to overcome these limitations, describing the historical development of hybrid PET/MR scanners. We pay special attention to the methods used in PET to achieve attenuation, scatter and motion correction when it is combined with MR, and how both imaging modalities may be combined in PET image reconstruction algorithms.
While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training ...
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ISBN:
(数字)9798350376876
ISBN:
(纸本)9798350376883
While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations.
We show an experimental method of quantifying the effect of light scattering by liquid crystals (LCs) and then apply rather simple imageprocessingalgorithms (Wiener deconvolution and contrast-limited adaptive histog...
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We show an experimental method of quantifying the effect of light scattering by liquid crystals (LCs) and then apply rather simple imageprocessingalgorithms (Wiener deconvolution and contrast-limited adaptive histogram equalization) to improve the quality of obtained images when using electrically tunable LC lenses (TLCLs). Better contrast and color reproduction have been achieved. We think that this approach will allow the use of thicker LC cells and thus increase the maximum achievable optical power of the TLCL without a noticeable reduction of image quality. This eliminates one of the key limitations for their use in various adaptive imaging applications requiring larger apertures. (C) 2020 Optical Society of America
A brain tumor is an abnormal growth or mass of cells in or around the brain. Early detection of brain tumors is imperative, impacting the quality of life and potential fatality. Prolonged undetected brain tumors can c...
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A brain tumor is an abnormal growth or mass of cells in or around the brain. Early detection of brain tumors is imperative, impacting the quality of life and potential fatality. Prolonged undetected brain tumors can cause irreversible brain damage. Early detection enables medical intervention to prevent severe harm, preserving cognitive function and reducing permanent damage risk. These tumors come in a wide variety of sizes, locations, and other characteristics. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming task that might lead to inaccuracies. Many researchers investigated a variety of algorithms for detecting and classifying brain tumors that were both accurate and fast. Deep learning (DL) approaches have recently been popular in developing automated systems capable of accurately diagnosing or segmenting various tumors in less time. In this research, we propose several ways to detect brain cancer and tumors using computational intelligence and statistical imageprocessing techniques. We use three different deep learning architecture models along with data augmentation and imageprocessing to categorize brain MRI scan images into cancerous and non-cancerous types. We later conducted a comparative analysis of our models: EfficientNetB4, vision Transformer (viT) combined with EfficientNetB4 (a novel hybrid model), and a custom CNN model built from scratch. The experiment results demonstrate that all models achieved high accuracy and very low complexity rate. Specifically, EfficientNetB4 achieving 99.76%, 99.6% in vision Transformer + EfficientNetB4, and scratch CNN achieved 97.25% accuracy. Our models require very less computational power and have much better accuracy results as compared to other pretrained models.
The application of machine learning models is increasing rapidly, particularly solving challenges within underwater environments. This research paper introduces a machine learning model designed for the detection of u...
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ISBN:
(数字)9798331537579
ISBN:
(纸本)9798331537586
The application of machine learning models is increasing rapidly, particularly solving challenges within underwater environments. This research paper introduces a machine learning model designed for the detection of underwater species and garbage waste in underwater *** systems are lacking due to the inadequate dataset real time classification and misclassifications of images. These systems typically identify only a small number of species because of the difficulties associated with varying underwater light conditions. This research proposes a new approach by utilizing the YOLO algorithm for the detection and classification of underwater images. The system functions by capturing underwater images, which are then preprocessed to improve the features. The dataset consists of 600 images, with 7 different classes and splitted into training and testing. The proposed method detects and classifies underwater objects with an overall precision of 95% to 97%. This research helps marine researchers and students in identifying underwater species and helps to improve underwater environments.
In India’s rural regions, tomato are the primary extensively raised economic crop. varying surroundings and various other elements affect the standard progress of tomato plants. Plant disease is a major contributor t...
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ISBN:
(数字)9798331537012
ISBN:
(纸本)9798331537029
In India’s rural regions, tomato are the primary extensively raised economic crop. varying surroundings and various other elements affect the standard progress of tomato plants. Plant disease is a major contributor to financial loss and a big problem in agricultural output, in addition to severe weather and natural disasters. The traditional methods for detecting diseases in tomato crops proved ineffective, and the duration required for infection detection was prolonged. Early identification of diseases can yield better outcomes than existing detection algorithms. Consequently, Machine Learning methodologies leveraging imageprocessing technology may be employed to detect tomato plant leaf diseases at an early stage. This research delivers an in-depth examination of the division and detection approaches provided for confirming illnesses in tomato leaves. The current research assesses the merits and drawbacks of the suggested methodologies. This study inevitably calls for the early identification of tomato leaf disease using Convolutional Neural Networks.
Deep Neural Network (DNN) belongs to an important class of machine learning algorithms generally used to classify digital data in the form of image and speech recognition. The computational complexity of a DNN-based i...
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Purpose: Digital Breast Tomosynthesis (DBT) is an advanced mammography technique for which there are currently no internationally agreed methods and reference values for image quality assessment. The aim of this multi...
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Purpose: Digital Breast Tomosynthesis (DBT) is an advanced mammography technique for which there are currently no internationally agreed methods and reference values for image quality assessment. The aim of this multicentre study was to evaluate a simple method to assess the technical image quality of reconstructed and synthetic 2D (SM) images of different models of DBT systems using commercially available phantoms. Methods: The signal difference to noise ratio (SDNR) was chosen as an index of technical image quality and was evaluated for three commercial phantoms, Tomophan, Tormam and CIRS model 015, on 55 DBT systems (six vendors, nine models). Results: SDNR was found to depend on several factors: detail size, average glandular dose (AGD), reconstruction algorithm, software version and applied post-processing. In particular, an increase in SDNR was observed with increasing detail size, AGD, as well as with the use of contrast-enhanced post-processing and iterative reconstruction algorithms. Most systems showed higher SDNR values in SM images respect to DBT for the largest details and a decrease for smaller details. Conclusions: This study proposes a straightforward method to assess the technical image quality of reconstructed clinical breast structures. However this method could be used to establish reference values for technical image over time.
The Weed Plant Detection in Agricultural Field images explores the application of Deep Learning (DL) methods in the field of agriculture, specifically focusing on weed detection and classification. These tasks are cru...
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ISBN:
(数字)9798350361155
ISBN:
(纸本)9798350361162
The Weed Plant Detection in Agricultural Field images explores the application of Deep Learning (DL) methods in the field of agriculture, specifically focusing on weed detection and classification. These tasks are crucial for weed management. Achieving higher crop yields. Identifying weeds within crops presents challenges due to their appearance. The paper examines data collection, dataset preparation, DL algorithms for weed identification, and evaluation metrics. It highlights the use of learning techniques that involve pre-trained models. The introduction of an approach utilizing the vGG16 Algorithm is discussed, which combines learning with advanced imageprocessing to enable real-time weed identification. This approach aims to minimize yield loss and reduce herbicide usage. It also emphasizes that this technology is scalable and adaptable to crops while integrating into autonomous farming equipment, promoting sustainable agriculture.
Spectral video has emerged as a non-invasive scientific tool to analyze the behavior of dynamic scenes in high spectral resolution. In view of its importance, several compressive spectral imaging (CSI) systems have be...
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ISBN:
(纸本)9781665416696
Spectral video has emerged as a non-invasive scientific tool to analyze the behavior of dynamic scenes in high spectral resolution. In view of its importance, several compressive spectral imaging (CSI) systems have been adapted to acquire compressed projections of dynamic scenes at high frame rates. These acquisition techniques capture and encode three-dimensional (3D) spatio-spectral information of the scene into a set of two-dimensional (2D) projections at different time frames. various computational reconstruction algorithms can be used to recover the underlying spectral video from the compressed measurements and then, processing tasks such as classification, detection, among others, can be performed. However, the main challenge of reconstruction-based approaches is the high computational cost, which increases with the number of frames. This paper presents a CNN-based method to segment moving objects on the compressive domain. This method can exploit spatial-temporal correlations and detect the prominent motion regions without recovering the spectral-temporal data set. Simulation results show that the proposed method outperforms a state-of-the-art approach that also detects motion on the compressive domain, and obtains comparable segmentation performance with respect to a method that works on the reconstructed data.
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