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deep learning for anterior segment OCT angiography automated denoising and vascular quantitative measurement

BIOMEDICAL SIGNAL processing AND CONTROL 2023年第1期83卷

作者： Luo, Man Xu, Zhiling Ye, Zehua Liang, Zhendong Xiao, Hui Li, Yiqing Li, Zhidong Zhu, Yingting He, Yonghong Zhuo, Yehong Sun Yat sen Univ Zhongshan Ophthalm Ctr Guangdong Prov Clin Res Ctr Ocular Dis State Key Lab OphthalmolGuangdong Prov Key Lab Op Guangzhou 510060 Peoples R China Tsinghua Shenzhen Int Grad Sch Dept Life & Hlth Shenzhen 518055 Peoples R China

Anterior segment optical coherence tomography angiography (AS-OCTA) has superior advantages in objective assessment of anterior segment (AS) vessels. Resolving noise interference in image is necessary to optimize the application of AS-OCTA. The study aimed to explore an automated denoising algorithm based on deep learning (DL). The algorithm was built through 21,000 pairs of images, and tested with 30 healthy eyes, 47 preoperative eyes with glaucoma, and 30 eyes undergone trabeculectomy (Trab). The real pure noise images were acquired by artificial simulation of eye movements through AS-OCTA. The algorithm included deep convolutional generative adversarial network (DCGAN), Res-Unet and Otsu. imageJ software quantified vessel density (VD) and vessel diameter index (VDI). images after noise reduction had relatively satisfactory peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Indiana bleb appearance grading scale (IBAGS), Kronfeld grading system (KGS) and intraocular pressure (IOP) used for Trab analysis. The DL method was superior to the conventional methods (PSNR = 16.45, SSIM = 0.52, both P < 0.001), and the denoising reduced measurement error of VD and VDI (P < 0.001). The denoising methods enabled the differentiation of V2 in IBAGS from V0 and V1 (P < 0.001) or that of II in KGS from I (P = 0.020). VD and VDI could better reflect IOP after noise reduction (R2 increased from 0.25 to 0.63, 0.14 to 0.41, both P < 0.001). Our research offered a DL denoising algorithm which improved the quality of AS-OCTA and the accuracy of AS vessel analysis.

关键词： Anterior segment optical coherence tomography angiography image denoising Vascular biomarkers deep learning Res-Unet deep convolution generation countermeasure network

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Retinares: An Integrated deep learning Framework for Diabetic Retinopathy Classification and real-time Severity Prediction

Retinares: An Integrated Deep Learning Framework for Diabeti...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： R. Praveen Kumar A. Vivek Vardhan G. Sai Sanjay P. Susmitha Ramana S. Sai Pavan Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Diabetic Retinopathy (DR) is a leading cause of blindness among diabetics, necessitating accurate and timely diagnosis. Current object detection-based methods often misclassify healthy retinal images, hindering effective patient care. This research proposes a ResNet-based classification framework to address this challenge. By focusing on precise classification rather than object detection, our approach significantly reduces false positives. Leveraging a Kaggle dataset, we demonstrate the model's superior performance in assessing DR severity compared to existing methods. A user-friendly Flask-based interface enables easy image upload and classification results. Our findings, evaluated using accuracy, precision, recall, and Fl-score, highlight the potential of our model to improve DR diagnosis and patient outcomes.

关键词： deep learning Diabetic retinopathy Electric potential Accuracy Communication systems Focusing Object detection Blindness Retina real-time systems

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Optical Preprocessing for Low-Latency Machine Vision

Optical Preprocessing for Low-Latency Machine Vision

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作者： Muminov, Baurzhan University of California Riverside

学位级别：Ph.D., Doctor of Philosophy

In recent years there has been an increased interest towards edge computing, i.e., computing performed on distributed devices as opposed to centralized high-power hubs. Examples of edge computing would be the local image processing performed on Unmanned Autonomous Vehicles (UAV's) or the specialized machine vision systems on drones. These edge computing applications require schemes that are efficient with power and memory and typically must operate real-time. Many state-of-the-art image processing solutions that employ advanced optimization and deep neural networks (NNs) achieve impressive benchmark results, but are computationally demanding and thus on many occasions, impractical. The additional requirement for a range of applications is noise robustness or the ability to work in (extreme) low-light conditions; reasonable quality image or accurate object classification may be critical when there is low light flux or when the environment is over-saturated with other signals. Here, we approach edge computing with a combination of optical preprocessing and shallow NN and we show that this hybrid approach greatly reduces the computational requirements. For low-SNR imaging, we develop a technique that reconstructs objects and scenes from their Fourier-plane images. The optical preprocessing is performed via encoded diffraction with optical vortex singularities. The optical vortex encoder achieves differentiation of the already-compressed Fourier-plane patterns and enables facile inverse inference of the original object scene. We demonstrate that our method is robust to noise. And for a simple NN architecture (one or two layers), leads to generalization, i.e., reconstruction of objects from classes that are greatly different from the ones the NN was trained on. Our research identifies strong potential for swift hybrid imaging systems with edge computing applications and highlights the valuable function of the vortex encoder for spectral differentiation.

关键词： Low-latency Machine learning Machine vision Noise robustness Topological optics Vortices

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Indirect cutting tool wear classification using deep learning and chip colour analysis

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第3-4期111卷 1099-1114页

作者： Pagani, Luca Parenti, Paolo Cataldo, Salvatore Scott, Paul J. Annoni, Massimiliano Univ Huddersfield EPSRC Future Metrol Hub Huddersfield HD1 3DH Queensgate England Politecn Milan Dept Mech Engn Via La Masa 1 I-20156 Milan Italy

In the growing Industry 4.0 market, there is strong need to implement automatic inspection methods to support manufacturing processes. Tool wear in turning is one of the biggest concerns that most expert operators are able to indirectly infer through the analysis of the removed chips. Automatising this operation would enable developing more efficient cutting processes that turns in easier process planning management toward the Zero Defect Manufacturing paradigm. This paper presents a deep learning approach, based on image processing applied to turning chips for indirectly identifying tool wear levels. The procedure extracts different indicators from the RGB and HSV image channels and instructs a neural network for classifying the chips, based on tool state conditions. images were collected with a high-resolution digital camera during an experimental cutting campaign involving tool wear analysis with direct microscope imaging. The sensitivity analysis confirmed that the most sensible image channels are the hue valueHthat were used to teach the network, leading to performances in the range of 95 of proper classification. The feasibility of the deep learning approach for indirectly understanding the tool wear from the chip colour characterisation is confirmed. However, due to the big effects on chip colours of variables as the workpiece material and cutting process parameters, the applicability is limited to stable production flows. An industrial implementation can be foreseen by populating proper large databases and by implementing real-time chip segmentation analysis.

关键词： image processing Vision inspection deep learning Tool wear Chip analysis Monitoring

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deep learning for Sparse and Limited-View Photoacoustic Tomography image Reconstruction

Deep Learning for Sparse and Limited-View Photoacoustic Tomo...

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作者： Guan, Steven George Mason University

学位级别：Ph.D.

Photoacoustic tomography (PAT) is a non-ionizing imaging modality capable of acquiring high contrast and resolution images based on optical absorption at depths greater than traditional optical imaging techniques. PAT has matured as a technology to the stage of transitioning from a laboratory to a clinical setting is possible. This presents a wide variety of practical considerations and limitations with instrumentation and data acquisition. Common challenges include having a limited number of available acoustic detectors and a reduced “view” of the imaging target. Forming an image with classical reconstruction methods from insufficient data often result in images with artifacts that degrade image quality. Advanced methods such as iterative reconstruction can be effective in reducing or removing the artifacts. But these methods are also computationally expensive and might not be appropriate in settings requiring near real-time imaging. In this work, we summarize our efforts in utilizing deep learning to address the deficiencies of sparse spatial sampling and limited-view detection in PAT image reconstruction. We begin with an introduction to fundamental principles of photoacoustic imaging (Chapter 1). This is followed by a brief introduction to deep learning and summarize commonly used deep learning frameworks for PAT image reconstruction(Chapter 2). Next, we describe a novel convolutional neural network architecture termed Fully Dense UNet for sparse PAT image reconstruction (Chapter 3). We then describe pixel-wise deep learning, a data pre-processing step that seeks to provide a more informative input to the neural network (Chapter 4). Next, we describe a modified network architecture termed Dense Dilated UNet that leverages the benefits of dense connectivity and dilated convolutions for 3D PAT image reconstruction (Chapter 5). We then describe Fourier Neural Networks as a fast and general solver for the photoacoustic wave equation (Chapter 6). Finally, we conclude

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Online learning of image Recognition Task Offloading Policies Over Wireless Links

Online Learning of Image Recognition Task Offloading Policie...

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IEEE International Conference on Communications (ICC)

作者： Iordanis Koutsopoulos Department of Informatics Athens University of Economics and Business Athens Greece

We study the problem of online learning of optimal offloading policies for image processing tasks, for minimizing a cost that is weighted sum of transmit energy and object recognition error rate. A mobile node generates image processing tasks that involve object recognition. There exist three options: (i) transmit the image to a remote server for processing with a deep-learning (DL) model, (ii) process locally with a simpler model, (iii) apply a lightweight, error-prone technique for object detection, and if objects are detected, then send image to the server. The proper offloading decision requires knowledge of the transmit energy cost and object recognition error rate for each option. However, these processes are non-stationary due to unpredictable object occurrence, mobility and propagation dynamics, and they depend on the object inference result which is unknown at decision time. We cast the problem as an adversarial multi-armed bandit, in which the EXP3 algorithm achieves sublinear regret. For the constrained problem, we propose an algorithm that extends EXP3 and achieves good regret in the objective and constraint, thus asymptotically learning the optimal static ran-domized offloading policy, while satisfying the error constraint. Performance is validated via numerical experiments informed by real-life object recognition measurements and models.

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Coverless real-time image information hiding based on image block matching and dense convolutional network

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JOURNAL OF real-time image processing 2020年第1期17卷 125-135页

作者： Luo, Yuanjing Qin, Jiaohua Xiang, Xuyu Tan, Yun Liu, Qiang Xiang, Lingyun Cent South Univ Forestry & Technol Coll Comp Sci & Informat Technol Changsha Peoples R China Univ Alabama Coll Commun & Informat Sci Tuscaloosa AL 35487 USA Changsha Univ Sci & Technol Sch Comp & Sci Engn Changsha Peoples R China

Information security has become a key issue of public concern recently. In order to radically resist the decryption and analysis in the field of image information hiding and significantly improve the security of the secret information, a novel coverless information hiding approach based on deep learning is proposed in this paper. deep learning can select the appropriate carrier according to requirements to achieve real-time image data hiding and the high-level semantic features extracted by CNN are more accurate than the low-level features. This method does not need to employ the designated image for embedding the secret data but transfer a set of real-time stego-images which share one or several visually similar blocks with the given secret image. In this approach, a group of real-time images searched online are segmented according to specific requirements. Then, the DenseNet is used to extract the high-level semantic features of each similar block. At the same time, a robust hash sequence with feature sequence, DC and location is generated by DCT. The inverted index structure based on the hash sequence is constructed to attain real-time image matching efficiently. At the sending end, the stego-images are matched and sent through feature retrieval. At the receiving end, the secret image can be recovered by extracting similar blocks through the received stego-images and stitching the image blocks according to the location information. Experimental results demonstrate that the proposed method without any modification traces provides better robustness and has higher retrieval accuracy and capacity when compared with some existing coverless image information hiding.

关键词： Coverless information hiding Data hiding deep learning DCT DenseNet real-time image processing

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Improved Cerebral images Semantic Segmentation Using Advanced Approaches of deep learning 2nd

Improved Cerebral Images Semantic Segmentation Using Advance...

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2nd International Conference on Intelligent Systems and Pattern Recognition, ISPR 2022

作者： Zoghbi, Abderraouf Benleulmi, Maroua Cheriguene, Soraya Azizi, Nabiha Lagrini, Samira Layeb, S. Nadine Labged Laboratory Badji Mokhtar University Annaba Algeria Department of Computer Science Badji Mokhtar University Annaba Algeria Saad Dahlab University Blida Algeria University of Paris Paris France

ISBN: (纸本)9783031082764

Reliable detection of brain tumors is a challenging task, even with the proper acquisition of Magnetic resonance imaging (MRI) images. Computer-aided detection (CADe) systems can reduce the workload of physicians and minimize the time required for accurate segmentation of illnesses. CADe systems for brain tumors comprises two principles stages: pre-processing of MRI images, and segmentation to define the region of interest (ROI). This paper describes the application of deep learning to detect tumors on brain MRI images where the number of defective samples available is small, which is fairly common in many real practical applications and can negatively affect the model performance. In the presented study, the Generative Adversarial Networks (GANs) learned how to synthesize realistic images to improve the training of DL models. We also explored CyclaGAN architecture via hyperparameter tuning and performed image segmentation for brain tumor. The proposed CADe system was validated on Figshare dataset of brain MRI images. Experimental results show that, the data augmentation method can increase the segmentation performance efficiently and effectively. © 2022, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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real-time Detection of Concealed Threats with Passive Millimeter Wave and Visible images via deep Neural Networks

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SENSORS 2021年第24期21卷 8456页

作者： Yang, Hao Zhang, Dinghao Qin, Shiyin Cui, Tie Jun Miao, Jungang Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

Passive millimeter wave has been employed in security inspection owing to a good penetrability to clothing and harmlessness. However, the passive millimeter wave images (PMMWIs) suffer from low resolution and inherent noise. The published methods have rarely improved the quality of images for PMMWI and performed the detection only based on PMMWI with bounding box, which cause a high rate of false alarm. Moreover, it is difficult to identify the low-reflective non-metallic threats by the differences in grayscale. In this paper, a method of detecting concealed threats in human body is proposed. We introduce the GAN architecture to reconstruct high-quality images from multi-source PMMWIs. Meanwhile, we develop a novel detection pipeline involving semantic segmentation, image registration, and comprehensive analyzer. The segmentation network exploits multi-scale features to merge local and global information together in both PMMWIs and visible images to obtain precise shape and location information in the images, and the registration network is proposed for privacy concerns and the elimination of false alarms. With the grayscale and contour features, the detection for metallic and non-metallic threats can be conducted, respectively. After that, a synthetic strategy is applied to integrate the detection results of each single frame. In the numerical experiments, we evaluate the effectiveness of each module and the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms the existing methods with 92.35% precision and 90.3% recall in our dataset, and also has a fast detection rate.

关键词： deep learning millimeter wave imaging image fusion object detection

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IMPROVING NEURAL BLIND DECONVOLUTION

IMPROVING NEURAL BLIND DECONVOLUTION

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IEEE International Conference on image processing (ICIP)

作者： Kotera, Jan Sroubek, Filip Smidl, Vaclav Czech Acad Sci Inst Informat Theory & Automat Prague Czech Republic

ISBN: (纸本)9781665441155

The field of blind image deblurring was for a long time dominated by Maximum-A-Posteriori methods seeking the optimal pair of sharp image-blur of a suitable functional. Recently, learning-based methods, especially those based on deep convolutional neural networks, are proving effective and are receiving increasing attention by the research community. In 2020, Ren et al. proposed a deblurring method called SelfDeblur which combines the model-driven approach of traditional MAP methods and the generative power of neural nets. The method is capable of producing very high-quality results, yet it inherits some problems of MAP methods, especially possible convergence to a wrong local optimum. In this paper we propose several easy-to-implement modifications of SelfDeblur, namely suitable initialization, multiscale processing, and regularization, that improve the average performance of the original method and decrease the probability of failure.

关键词： blind deblurring SelfDeblur deep image prior multiscale

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