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检索条件"任意字段=22nd Brazilian Symposium on Computer Graphics and Image Processing"

共 268 条记录，以下是1-10 订阅

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Tutorials of SIBGRAPI 2009 - 22nd brazilian symposium on computer graphics and image processing

Tutorials of SIBGRAPI 2009 - 22nd Brazilian Symposium on Com...

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22nd brazilian symposium on computer graphics and image processing, SIBGRAPI-TUTORIALS 2009

ISBN: (纸本)9780769538150

The proceedings contain 6 papers. The topics discussed include: introduction to GPU programming with GLSL;geometric algebra: a powerful tool for solving geometric problems in visual computing;multiscale methods for image processing: the wavelet and the scale-space approaches;unleashing the power of the Playstation 3 to boost graphics programming;digital image processing in remote sensing;and advanced techniques in CBIR: local descriptors, visual dictionaries and bags of features.

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Proceedings of SIBGRAPI 2009 - 22nd brazilian symposium on computer graphics and image processing

Proceedings of SIBGRAPI 2009 - 22nd Brazilian Symposium on C...

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22nd brazilian symposium on computer graphics and image processing, SIBGRAPI 2009

ISBN: (纸本)9780769538136

The proceedings contain 44 papers. The topics discussed include: hermite interpolation of implicit surfaces with radial basis functions;Legolizer: a real-time system for modeling and rendering LEGO representations of boundary models;synthesis and transfer of time-variant material appearance on images;perspective contouring in illustrative visualization;salient clustering for view-dependent multiresolution rendering;fast medial axis transform for planar domains with general boundaries;rectangular hexagonal mesh generation for parametric modeling;segmentation of brain structures by watershed transform on tensorial morphological gradient of diffusion tensor imaging;the use of high resolution images in morphological operator learning;GSAShrink: a novel iterative approach for wavelet-based image denoising;a regularized nonlinear diffusion approach for texture image denoising;and mammography images restoration by quantum noise reduction and inverse MTF filtering.

关键词： image segmentation

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Proceedings of SIBGRAPI 2009 - 22nd brazilian symposium on computer graphics and image processing: Preface

Proceedings of SIBGRAPI 2009 - 22nd Brazilian Symposium on C...

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Proceedings of SIBGRAPI 2009 - 22nd brazilian symposium on computer graphics and image processing 2009年 ix页

作者： Nonato, Luis Gustavo Scharcanski, Jacob

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Tutorials of SIBGRAPI 2009 - 22nd brazilian symposium on computer graphics and image processing 2009: Preface

Tutorials of SIBGRAPI 2009 - 22nd Brazilian Symposium on Com...

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Tutorials of SIBGRAPI 2009 - 22nd brazilian symposium on computer graphics and image processing 2009年 vi页

作者： Falcão, Alexandre Pesco, Sinésio

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Non-Rigid Registration of Time-Lapse Cell Microscopy images Improved by a Semi-Incremental Optimization Method

Non-Rigid Registration of Time-Lapse Cell Microscopy Images ...

引用

IEEE International symposium on Biomedical Imaging

作者： Qi Gao Karl Rohr Core Facility Platform Mannheim Medical Faculty Mannheim Heidelberg University Germany Biomedical Computer Vision Group BioQuant IPMB Heidelberg University Germany

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.

关键词： image registration Accuracy Deformation Microscopy Optimization methods graphics processing units Estimation Fluorescence image sequences Reliability

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BoxPolypSAM: Leveraging SAM in Box-Supervised Polyp Segmentation

BoxPolypSAM: Leveraging SAM in Box-Supervised Polyp Segmenta...

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IEEE International symposium on Biomedical Imaging

作者： Yinqi Cai De-jun Fan Feng Gao Mingzhi Mao Weijun Sun Guanbin Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Automatic polyp segmentation from colonoscopy images is crucial for colon cancer diagnosis. Nonetheless, training fully supervised polyp segmentation models requires timeconsuming pixel-level annotations. Instead, box annotations of polyps are more attainable. To reduce the burden of annotation, we propose BoxPolypSAM, a box-supervised polyp segmentation model based on the Segment Anything Model (SAM). We leverage a box-supervised projection loss to confine predicted masks within ground-truth boxes and an augmentation consistency loss to reduce the variability of predicted masks. For adapting SAM, we propose a model architecture AutoPromptSAM, which creates a dense prompt from the image encoder's features for automatic segmentation and employs efficient fine-tuning techniques to transfer the capabilities of the SAM model. Moreover, we distill the model to a lightweight model for quicker inference and less GPU memory cost, which also improves performance. Experimental results on six datasets demonstrate our proposed model and the distilled model show excellent performance in polyp segmentation without relying on any pixel-level labels.

关键词： Training image segmentation Adaptation models Costs Annotations Biological system modeling Memory management graphics processing units Colonoscopy Colorectal cancer

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Skull Fracture Visualization Using Disk Harmonic Maps

Skull Fracture Visualization Using Disk Harmonic Maps

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IEEE International symposium on Biomedical Imaging

作者： Nicolas Hadjittoouli Costas Pitris Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Skull fractures, especially those of the base and facial regions, are challenging to identify due to the intricate anatomy and their subtle morphological characteristics. They necessitate meticulous attention and allocation of sufficient time to ensure accurate diagnosis and optimal patient care. This paper introduces a novel approach that converts a head CT to a mesh representation, in order to utilize disk harmonic mapping and eventually create a flatten map of the skull for improved visualization. In such a view, fractures appear continuous with enhanced contrast. The effectiveness of this technique was evaluated using the CQ500 dataset. This alternative visualization of a flattened skull can provide radiologists and emergency department personnel a valuable addition to conventional radiological views, increasing the effectiveness and speed of skull fracture detection.

关键词： Visualization Accuracy Head Computed tomography image processing Harmonic analysis computer aided diagnosis Resource management Personnel Biomedical imaging

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UNETVL: Enhancing 3D Medical image Segmentation with Chebyshev KAN Powered Vision-LSTM

UNETVL: Enhancing 3D Medical Image Segmentation with Chebysh...

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IEEE International symposium on Biomedical Imaging

作者： Xuhui Guo Tanmoy Dam Rohan Dhamdhere Gourav Modanwal Anant Madabhushi Electrical Engineering and Computer Science Department University of Michigan Ann Arbor USA Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

3D medical image segmentation has progressed considerably due to Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), yet these methods struggle to balance long-range dependency acquisition with computational efficiency. To address this challenge, we propose UNETVL (U-Net Vision-LSTM), a novel architecture that leverages recent advancements in temporal information processing. UNETVL incorporates Vision-LSTM (ViL) for improved scalability and memory functions, alongside an efficient Chebyshev Kolmogorov-Arnold Networks (KAN) to handle complex and long-range dependency patterns more effectively. We validated our method on the ACDC and AMOS2022 (post challenge Task 2) benchmark datasets, showing a significant improvement in mean Dice score compared to recent state-of-the-art approaches, especially over its predecessor, UNETR, with increases of 7.3% on ACDC and 15.6% on AMOS, respectively. Extensive ablation studies were conducted to demonstrate the impact of each component in UNETVL, providing a comprehensive understanding of its architecture. Our code is available at https://***/tgrex6/UNETVL, facilitating further research and applications in this domain.

关键词： computer vision Three-dimensional displays Semantic segmentation Scalability Memory management Information processing Chebyshev approximation Transformers Convolutional neural networks Biomedical imaging

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State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters

State-of-the-Art Stroke Lesion Segmentation at 1/1000th of P...

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IEEE International symposium on Biomedical Imaging

作者： Alex Fedorov Yutong Bu Xiao Hu Chris Rorden Sergey Plis Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University Atlanta GA USA Department of Mathematics Emory University Atlanta GA USA Department of Psychology University of South Carolina Columbia SC USA Department of Computer Science Georgia State University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Efficient and accurate whole-brain lesion segmentation re-mains a challenge in medical image analysis. In this work, we revisit MeshNet, a parameter-efficient segmentation model, and introduce a novel multi-scale dilation pattern with an encoder-decoder structure. This innovation enables capturing broad contextual information and fine-grained details without traditional downsampling, upsampling, or skip-connections. Unlike previous approaches processing subvolumes or slices, we operate directly on whole-brain 256 3 MRI volumes. Evaluations on the Aphasia Recovery Cohort (ARC) dataset demonstrate that MeshNet achieves superior or comparable DICE scores to state-of-the-art architectures such as Med-NeXt and U-MAMBA at 1/1000th of parameters. Our results validate MeshNet's strong balance of efficiency and performance, making it particularly suitable for resource-limited environments such as web-based applications and opening new possibilities for the widespread deployment of advanced medical image analysis tools.

关键词： image segmentation Technological innovation image analysis Accuracy Magnetic resonance imaging MIMICs Aphasia Lesions Biomedical imaging Testing

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Nnmamba: 3D Biomedical image Segmentation, Classification and Landmark Detection with State Space Model

Nnmamba: 3D Biomedical Image Segmentation, Classification an...

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IEEE International symposium on Biomedical Imaging

作者： Haifan Gong Luoyao Kang Yitao Wang Yihan Wang Xiang Wan Xusheng Wu Haofeng Li Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Health Development Research and Data Management Center Shenzhen China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In biomedical image analysis, developing architectures that effectively capture long-range dependencies is crucial. Traditional Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers, though proficient in global context integration, are computationally demanding for high-dimensional medical images. Here, we present nnMamba, a novel architecture that combines the strengths of CNNs with the long-range modeling capabilities of State Space Models (SSMs). We introduce the Mamba-In-Convolution with Channel-Spatial Siamese learning (MICCSS) block to model long-range voxel relationships. Additionally, we implement channel scaling and channel-sequential learning methods to enhance performance in dense prediction and classification tasks. Extensive experiments on seven datasets demonstrate that nnMamba outperforms current state-of-the-art methods in 3D image segmentation, classification, and landmark detection. nnMamba effectively integrates CNNs' local representation with SSMs' global con-text processing, establishing a new benchmark for long-range dependency modeling in medical image analysis. Code is available at https://***/lhaof/nnMamba.

关键词： Learning systems Solid modeling image segmentation Three-dimensional displays image analysis Computational modeling Biological system modeling computer architecture Transformers Biomedical imaging

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