image Quality Assessment (IQA) is a critical task in imageprocessing, computervision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of cons...
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ISBN:
(纸本)9798350302196
image Quality Assessment (IQA) is a critical task in imageprocessing, computervision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of consumers. However, current IQA metrics may have limitations in evaluating image quality accurately for different types of images or under different conditions. To address this, a proposed approach involves calculating an overall image quality score based on linear combination of individual image quality metrics. This method considers multiple image features such as sharpness, contrast, color, and noise, and assigns weights to each feature based on its relative importance in determining overall image quality. By combining and weighting multiple image features, the proposed approach aims to provide a more comprehensive and accurate evaluation of image quality compared to using individual metrics alone. In this study, we focus on the importance of edge features and structure-based metrics in object detection and analyze existing No-Reference (NR) IQA metrics, including Just Noticeable Blur (JNB), Cumulative Probability Blur Detection (CPBD), Visual Quality Assessment (VQA), Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) and No-Reference Low- Light image Enhancement Evaluation (NLIEE), and propose a linear combination formula that combines these metrics to evaluate image. We evaluate the performance of the proposed metric on several datasets by calculating the linear combination of two, three, and all the five metrics. The weights assigned to each metric in the linear combination formula are determined through experimental analysis. The combined metric scores and MOS scores are then used to compute the Spearman's rank order correlation coefficient, which measures the monotonic relationship between two variables. A higher SROCC value indicates a stronger correlation between the combined metric and MOS scores and is used to evaluate the performanc
The microchannel plates are electron multiplier which mainly used in image intensifier tubes for imaging and intensification of the photoelectron image. In this paper, the research models of the funnel microchannel pl...
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Spinal ligaments are crucial elements in the complex biomechanical simulation models as they transfer forces on the bony structure, guide and limit movements and stabilize the spine. The spinal ligaments encompass sev...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
Spinal ligaments are crucial elements in the complex biomechanical simulation models as they transfer forces on the bony structure, guide and limit movements and stabilize the spine. The spinal ligaments encompass seven major groups being responsible for maintaining functional interrelationships among the other spinal components. Determination of the ligament origin and insertion points on the 3D vertebrae models is an essential step in building accurate and complex spine biomechanical models. In our paper, we propose a pipeline that is able to detect 66 spinal ligament attachment points by using a step-wise approach. Our method incorporates a fast vertebra registration that strategically extracts only 15 3D points to compute the transformation, and edge detection for a precise projection of the registered ligaments onto any given patient-specific vertebra model. Our method shows high accuracy, particularly in identifying landmarks on the anterior part of the vertebra with an average distance of 2.24 mm for anterior longitudinal ligament and 1.26 mm for posterior longitudinal ligament landmarks. The landmark detection requires approximately 3.0 seconds per vertebra, providing a substantial improvement over existing methods. Clinical relevance: using the proposed method, the required landmarks that represent origin and insertion points for forces in the biomechanical spine models can be localized automatically in an accurate and time-efficient manner.
Patient-specific 3D spine models serve as a foundation for spinal treatment and surgery planning as well as analysis of loading conditions in biomechanical and biomedical research. Despite advancements in imaging tech...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
Patient-specific 3D spine models serve as a foundation for spinal treatment and surgery planning as well as analysis of loading conditions in biomechanical and biomedical research. Despite advancements in imaging technologies, the reconstruction of complete 3D spine models often faces challenges due to limitations in imaging modalities such as planar X-Ray and missing certain spinal structures, such as the spinal or transverse processes, in volumetric medical images and resulting segmentations. In this study, we present a novel accurate and time-efficient method to reconstruct complete 3D lumbar spine models from incomplete 3D vertebral bodies obtained from segmented magnetic resonance images (MRI). In our method, we use an affine transformation to align artificial vertebra models with patient-specific incomplete vertebrae. The transformation matrix is derived from vertebra landmarks, which are automatically detected on the vertebra endplates. The results of our evaluation demonstrate the high accuracy of the performed registration, achieving an average point-to-model distance of 1.95 mm. Additionally, in assessing the morphological properties of the vertebrae and intervertebral characteristics, our method demonstrated a mean absolute error (MAE) of 3.4° in the angles of functional spine units (FSUs), emphasizing its effectiveness in maintaining important spinal features throughout the transformation process of individual vertebrae. Our method achieves the registration of the entire lumbar spine, spanning segments L1 to L5, in just 0.14 seconds, showcasing its time-efficiency. Clinical relevance: the fast and accurate reconstruction of spinal models from incomplete input data such as segmentations provides a foundation for many applications in spine diagnostics, treatment planning, and the development of spinal healthcare solutions.
As the "Mobile AI" revolution continues to grow, so does the need to understand the behaviour of edge-deployed deep neural networks. In particular, MobileNets [9, 22] are the go-to family of deep convolution...
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Neural radiance field (NeRF), in particular, its extension by instant neural graphics primitives is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual sc...
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Neural radiance field (NeRF), in particular, its extension by instant neural graphics primitives is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its enormous potential for virtual reality (VR) applications, there is currently little robust integration of NeRF into typical VR systems available for research and benchmarking in the VR community. In this poster paper, we present an extension to instant neural graphics primitives and bring stereoscopic, high-resolution, low-latency, 6-DoF NeRF rendering to the Unity game engine for immersive VR applications. 1 1 Link to the repository: https://***/uhhhci/immersive-ngp
Sum-of-Squares polynomial normalizing flows have been proposed recently, without taking into account the convexity property and the geometry of the corresponding parameter space. We develop two gradient flows based on...
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Generating anatomically plausible spinal 3D models is significant for biomedical research, allowing the study of the mechanical behavior in the spinal structures under various conditions for different populations. In ...
Generating anatomically plausible spinal 3D models is significant for biomedical research, allowing the study of the mechanical behavior in the spinal structures under various conditions for different populations. In this paper, we present a novel method that can automatically build 3D lumbar models from initial vertebra meshes based on a small list of configuration parameters including a maximum and minimum height of the intervertebral discs and a lumbar lordosis angle. The conducted experiments show that our method accurately captures the user-input characteristics in the generated spine models, as evidenced by an RMSE value of 0.0. Furthermore, we performed validation by comparing the intervertebral disc measurements of our created models with anatomical reference values obtained from published studies. The results of these experiments demonstrate the robustness and accuracy of our method with an RSSR value of 0.1mm for standing spine models and an RSSR value of 0.55mm for supine spine models.
Deep Affine Normalizing Flows are efficient and powerful models for high-dimensional density estimation and sample generation. Yet little is known about how they succeed in approximating complex distributions, given t...
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Non-rigid registration of cell nuclei in time-lapse microscopy images needs accurate estimation of the deformation fields between the reference and all other images. To address the issue of accumulated errors in class...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Non-rigid registration of cell nuclei in time-lapse microscopy images needs accurate estimation of the deformation fields between the reference and all other images. To address the issue of accumulated errors in classical temporal incremental registration approaches, we introduce a new semi-incremental optimization method. Based on a proper initialization constructed through motion concatenation, which exploits temporal coherence within the image sequence, the deformation field between each image and the reference is computed, reducing accumulated errors and yielding more reliable results. Experiments on real time-lapse cell images demonstrate that our method outperforms previous approaches, including deep learning models, in terms of registration accuracy. Additionally, computation on a GPU significantly enhances efficiency.
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