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检索条件"机构=Computer Vision and Image Processing Laboratory CVIP Lab"

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Colorectal Polyps Detection in Virtual Colonoscopy Using 3D Geometric Features and Deep Learning

Colorectal Polyps Detection in Virtual Colonoscopy Using 3D ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Mohamed Yousuf Samir Harb Islam Alkabbany Asem Ali Salwa Elshazley Aly Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Faculty of Engineering Ain Shams University Cairo Egypt Higher Technological Institute 10th of Ramadan City Egypt Kentucky Imaging Technologies Louisville KY

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys for treatment. In this work, we propose a framework to help radiologists in identifying polyp candidates using virtual colonoscopy. In the proposed approach, first a colon is segmented from a CT scan, then 3D reconstruction, to generate a surface representation of the colon, is performed. From the reconstructed 3D colon, 2D images are generated using the virtual colonoscopy, Fly-In approach. To enhance polyp detection, we fuse these 2D images and the 3D colon representation by generating 3D geometric feature maps, e.g. depth and curvature maps. CNN-based models are trained and validated to detect polyps using the generated feature maps, which are combined in multi-channel images. These images are successfully used to train a CNN-based model that detects polyps with mAP ∼ 97.1%.Colorectal cancer, colon polyp, computerized tomography (CT), Detection, CNN, segmentation.

关键词： image segmentation Surface reconstruction Three-dimensional displays Virtual colonoscopy Computed tomography Computational modeling Feature extraction

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An Automatic Colorectal Polyps Detection Approach for Ct Colonography

An Automatic Colorectal Polyps Detection Approach for Ct Col...

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IEEE International Conference on image processing

作者： Mohamed Yousuf Islam Alkabbany Asem Ali Salwa Elshazley Albert Seow Gerald Dryden Aly Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Faculty of Engineering Ain Shams University Cairo Egypt Kentucky Imaging Technologies Louisville KY School of Medicine University of Louisville Louisville KY

In this work, we propose an automatic colorectal polyps detection approach that consists of two cascade stages. In the first stage, a CNN model is trained to detect polyps in axial CT slices, The CNN model has been fed by the segmented colon wall CT slices instead of the original CT slices. Using the segmented images as an input to the CNN model has drastically improved the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, the second stage classifier is deployed to exploit the different views of the CT scans instead of the axial view only. So, the classifier is trained using the 2D images of axial views, i.e., the candidate polyps generated by the detector, as well as their corresponding 2D images of sagittal and coronal views. The experimental results of this approach were validated by 3 radiologists and the approach successfully identified polyps after the classification stage with an AUC ∼ 98.6%.

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image saliency detection method based on multi-feature maps fusion 4

Image saliency detection method based on multi-feature maps ...

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4th International Conference on computer Graphics, image, and Virtualization, ICCGIV 2024

作者： Li, Xiaoli Liu, Yunpeng Zhao, Huaici Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510683242

In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics." © 2024 SPIE.

关键词： Feature extraction

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Multi-View Network for Colorectal Polyps Detection in CT Colonography

Multi-View Network for Colorectal Polyps Detection in CT Col...

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IEEE International Conference on image processing

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys to treatment. In this work, we propose a framework to help radiologists in reading CT scans and identifying candidate CT slices that have polyps. We propose a colorectal polyps detection approach which consists of two cascaded stages. In the first stage, a CNN-based model is trained and validated to detect polyps in axial CT slices. To narrow down the effective receptive field of the detector neurons, the colon regions are segmented and then fed into the network instead of the original CT slice. This drastically improves the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, in the second stage, we propose a multi-view network (MVN) that classifies polyp candidates. The proposed MVN classifier is trained using sagittal and coronal views corresponding to the detected axial views. The approach is tested in 50 CTC-annotated cases, and the experimental results confirm that after the classification stage, polyps can be detected with an AUC $\sim 95.27 \%$.

关键词： Location awareness Sensitivity Computed tomography Neurons Detectors Colonography Feature extraction

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Accurate Colon Segmentation Using 2D Convolutional Neural Networks With 3D Contextual Information

Accurate Colon Segmentation Using 2D Convolutional Neural Ne...

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IEEE International Conference on image processing

作者： Samir Harb A. Elsayed M. Yousuf I. Alkabbany A. Ali S. Elshazley A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Higher Technological Institute 10th of Ramadan City Egypt Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Engineering Assiut University Assiut Egypt Kentucky Imaging Technologies Louisville KY

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This study introduces an innovative framework designed specifically for accurate colon segmentation in abdomen CT scans, tackling the distinct challenges inherent to this task. Building upon well-established 2D segmentation models, our architecture adeptly incorporates 3D contextual information via a novel method that generates an attention map for a given slice by considering its neighboring slices in a sequence. Our approach accomplishes effective colon segmentation without requiring complex 3D convolutional neural networks (CNNs) or Long Short-Term Memory (LSTM) networks by combining 2D CNNs. Validated on a dataset of 98 CT scans from 49 patients, the architecture exhibits notable performance, successfully capturing nuanced details crucial for precise colon segmentation. The experiments encompass a thorough examination of model selection and cross-validation, providing valuable insights into the efficacy of our proposed approach. The outcomes underscore the potential for streamlined colon segmentation in medical imaging by judiciously integrating 2D and 3D information, employing solely 2D networks, and mitigating challenges associated with 3D networks. The code for model architecture is available at: https://***/Samir-Farag/***

关键词： image segmentation Solid modeling Three-dimensional displays Computed tomography Predictive models Colon Convolutional neural networks

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Fast Candidate Region Extraction for SAR Ship Target 37

Fast Candidate Region Extraction for SAR Ship Target

引用

37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Zhang, Panpan Luo, Haibo Xu, Zheng He, Miao Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China The Key Lab of Image Understanding and Computer Vision Shenyang110016 China

ISBN: (纸本)9781665465366

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset. © 2022 IEEE.

关键词： Synthetic aperture radar

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Terahertz compressive imaging: understanding and improvement by a better strategy for data selection

Terahertz compressive imaging: understanding and improvement...

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作者： Xing, Chungui Qi, Feng Liu, Zhaoyang Wang, Yelong Guo, Shuxu State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision Shenyang China

Compressive sensing (CS) is a novel sampling modality, which indicates the signals can be sampled at a rate much below the Nyquist sampling rate. CS has increasing interest recently due to high demand of rapid, efficient, and in-expensive signal processing applications in the μmWave and mmWave frequencies, such as communication and imaging. There have been a lot of theoretical studies on this topic, but there is a lack of systematic experimental analysis of the implementation method itself. In this paper, we have investigated the influencing factors of terahertz compressive sensing based on experimental results, including illumination and the size of the pixel. Besides, to differentiate from current approaches, which generally make full use of the data, we propose to sort the data first and select a part of them based on amplitude, which might deliver a better image by prompting the mathematical calculations compulsively. We believe that such considerations given above would help to make a better system design and improve the performance of compressive imaging, and these results will also be helpful in the application of terahertz communication. © 2021 John Wiley & Sons, Ltd

关键词： Compressed sensing

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Fast Candidate Region Extraction for SAR Ship Target

Fast Candidate Region Extraction for SAR Ship Target

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Panpan Zhang Haibo Luo Zheng Xu Miao He Shenyang Institute of Automation Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China

ISBN: (纸本)9781665465373

关键词： Deep learning Training Redundancy Radar polarimetry Inference algorithms Indexes Marine vehicles

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image saliency detection via multi-feature and manifold-space ranking 2021

Image saliency detection via multi-feature and manifold-spac...

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3rd Asia Pacific Information Technology Conference, APIT 2021

作者： Li, Xiaoli Zhao, Huaici Liu, Yunpeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China The Key Lab of Image Understanding and Computer Vision Shenyang110016 China Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9781450388108

In this paper, we propose an image saliency detection method by using multi-feature and manifold-space ranking. Basically, the proposed method extracts the color-histogram feature to obtain the fine information of the image, and the color-mean feature to obtain the coarse information respectively. To further improve the detection accuracy of the feature correlation between different image units, a manifold-space ranking method is used to calculate saliency values of image units to construct a saliency map on each feature-space. Specifically, we fuse the two saliency maps to obtain the final saliency map. Extensive experiments demonstrate that the proposed method not only outperforms the other methods, but also improves the accuracy and robustness of the saliency detection. © 2021 ACM.

关键词： Color

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RTM3D: Real-Time Monocular 3D Detection from Object Keypoints for Autonomous Driving 16th

RTM3D: Real-Time Monocular 3D Detection from Object Keypoin...

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16th European Conference on computer vision, ECCV 2020

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision ShenyangLiaoning China

ISBN: (纸本)9783030585792

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS > 24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark. © 2020, Springer Nature Switzerland AG.

关键词： Real time systems

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