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Automated Bone Segmentation Using a Hybrid Model of DRLSE and Faster R-CNN 25

Automated Bone Segmentation Using a Hybrid Model of DRLSE an...

引用

25th IEEE conference on signal processing - Algorithms, Architectures, Arrangements, and applications (IEEE SPA)

作者： Naderian, Amirkhashayar Sanei, Saeid Nottingham Trent Univ Dept Comp Sci Nottingham England

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

The computer aided diagnosis systems from radiological images has been of interest to researchers mostly for detection of bone fracture or dislocation. The accuracy highly depends on bone segmentation. Any improvement of such systems, particularly for noisy x-ray images, is very valuable. Classical image segmentation depending on image homogeneity are time consuming and require pixel-wise labelling. On the other hand, saliency map based approaches fail to detect the region around the fracture or segment the dislocated bones. In our research we have used transfer learning to train the faster regional convolutional neural network (FCNN) alongside distance regularized level set evolution (DRLSE) to have accurate bone segmentation without any pixel-wise labelling enabling segmentation of the region around the fracture and dislocated bones. We applied the proposed method to a number of hand x-ray images and achieved accuracy values of 95% and average precision-recall of 0.96.

关键词： Bone segmentation distance regularized level set evolution Faster R-CNN image processing medical imaging transfer learning x-ray

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Design of image fusion system based on FPGA

Design of image fusion system based on FPGA

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2022 International conference on image, signal processing, and Pattern Recognition, ISPP 2022

作者： Chen, Chen School of Information Engineering Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510654846

With the continuous development of digital information technology and the improvement of chip processing capacity, image processing applications are becoming more and more popular[1]. image processing has become an important modern technology, the current image acquisition equipment has its own limitations, but if the image information collected by two sensors to do fusion processing, can get better results of the image. The proposed image fusion system, the use of even have a camera to collect images, by using Laplacian pyramid fusion method, two image fusion can be testified by upper high resolution and low resolution RGB greyscale figure fusion, get the image with better effect, make the information of the fused images more rich, overcome the deficiency of the single sensor, It has certain application value. © 2022 SPIE.

关键词： image fusion

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image Watermarking based on Saliency Detection and Multiple Transformations

Image Watermarking based on Saliency Detection and Multiple ...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Khan, Ahmed Wong, KokSheik Baskaran, Vishnu Monn Monash Univ Malaysia Sch Informat Technol Subang Jaya Malaysia

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

An ideal image watermarking (IW) scheme aims to manage the trade-off among quality, capacity, and robustness. However, our literature survey reveals some flaws in the form of poor robustness and quality or low embedding capability. In this paper, multiple frequency domain based image watermarking scheme using salient (eye-catching) object detection is applied. Specifically, the host and the watermark images are partitioned into background and foreground regions by the proposed multi-dimension decomposition, which accumulates image patches and combining them to form the salient map. Next, the watermark image is encrypted by multiple applications of the 3D Arnold and logistic maps, then embedded into both the identified foreground and background regions of the host image by using different embedding strengths. The proposed method can embed 1 color pixel of the watermark image into 1 color pixel in the host image while maintaining high image quality. In the best case scenario, we could embed a 24-bit image as the watermark into a 24-bit image of the same dimension while maintaining an average RGB-SSIM of 0.9999. Experiments are carried out (with 10K MSRA dataset images) to verify the performance of the proposed method and to compare our proposed method against the state-of-the-art (SOTA) watermarking methods.

关键词： image quality Three-dimensional displays image color analysis Frequency-domain analysis Watermarking Object detection Information processing

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Distributed long-wave infrared array camera based on fiber image bundle

Distributed long-wave infrared array camera based on fiber i...

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2022 conference on Infrared, Millimeter, Terahertz Waves and applications, IMT 2022

作者： Li, Junwei Cui, Hao Liu, Shengjie Wang, Shengqian Wei, Kai Key Laboratory of Adaptive Optics Chinese Academy of Sciences Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Sciences Chengdu610209 China School of Optoelectronics University of Chinese Academy of Sciences Beijing100049 China Zhejiang University Hangzhou310058 China

ISBN: (纸本)9781510662476

To address the complex structure and bulky volume problem of long-wave infrared detection system, we design a front-and-back-splitting distributed long-wave infrared array camera based on fiber image bundle with a simple and high-flexibility structure. The proposed system achieves higher signal-to-noise ratio and improved resolution using image algorithm processing. Benefited by the fiber bundle, the front and back lens groups are separated, which is conductive to scanning imaging and conformal design. A principal prototype of 2-way synthesis is built to verify the imaging performance of the array camera. The prototype is used to obtain two-aperture time series images of small target. One is used as a benchmark and the other is registered and synthesized. The signal-to-noise ratio of composite image increased by 25.15% by eliminating time noise. Besides, this paper uses the redundant information among multiple micro-displacement images obtained by pixel-level displacement of fiber bundle to achieve super-resolution image reconstruction. The image resolution at least doubles in one-dimensional direction and the reconstruction algorithm can be extended to two dimensions. Experimental results of the prototype show that the distributed array camera we designed can improve the signal-to-noise ratio and realize image super-resolution reconstruction with structure simplified and volume reduced. © 2023 SPIE.

关键词： Cameras

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processing of the signal obtained from x-ray fluorescence emissions to estimate localization in biomedical applications 6

Processing of the signal obtained from X-ray fluorescence em...

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6th Argentine conference on Electronics (CAE)

作者： Martin, Nicolas E. Sofo Haro, Miguel Valente, Mauro Consejo Nacl Invest Cient & Tecn IFEG LIIFAMIR Cordoba Argentina UNC FAMAF Cordoba Argentina UNC Reactor Nucl RA0 CNEA IFEGCONICET Cordoba Argentina

ISBN: (纸本)9798350305081

x-ray fluorescence detection has been widely used in recent years in computed tomography due to its ability to non-destructively acquire structural and elemental information from samples. In this paper, we present a complete irradiation and image reconstruction system based on a linear spatial configuration of cadmium telluride detectors. This configuration is used to analyze the energy spectra detected in a phantom model, which was scanned by Monte Carlo simulation to examine the K series of the emission lines of Au nanoparticles (GNPs). The phantom used in our study has a cylindrical shape with a diameter of 60 mm and a height of 100 mm. It is filled with a material equivalent to water and tissue and contains five embedded cylinders, with concentrations of 1% down to 0.1% GNP. To simulate the irradiation process, we use the PENELOPE Monte Carlo technique tool in conjunction with a 150 kV monoenergetic source. A previously characterized cadmium telluride detector model was used and reconstructed with two widely used techniques in the environment to evaluate the complete system. The results obtained from our study demonstrate that the proposed detector configuration together with the proposed irradiation simulation system produces measurable and viable signals, comparable with other studies carried out, which would allow the effective application of various complex image reconstruction techniques.

关键词： x-ray flourescence Monte Carlo GNP Phantom

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Region of Interest Scalable image Compression Using Semantic Communications

Region of Interest Scalable Image Compression Using Semantic...

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2024 IEEE International conference on Consumer Electronics, ICCE 2024

作者： Samarathunga, Prabhath Gowrisetty, Vishnu Fernando, Thanuj Ganearachchi, Yasith Fernando, Anil University of Strathclyde Department of Computer and Information Sciences Glasgow United Kingdom

ISBN: (纸本)9798350324136

Growing consumer demand for media content across a wide range of devices has made scalable image compression vital in the current media landscape. image compression is conventionally achieved by means of statistical signal processing, but since recently deep learning techniques have been widely adopted as well. Capabilities of such systems also enable accurate identification of regions of interest in images, leading to optimized performance in most applications. This paper proposes a region-of-interest scalable image compression system using semantic communications, where an autoencoder-based semantic encoder performs the base-level compression, while a Semantic Mask Extracting Transformer (SeMExT) enables identification of regions of interest to create enhancement layers with different quality levels using a scalable JPEG encoder. When benchmarked against scalable JPEG across a variety of images, the proposed system demonstrates significantly improved compressive performance. The base layer achieved 61.4 times more compression on average, along with better rate-distortion performance at any given quality level. © 2024 IEEE.

关键词： Deep Neural Networks image Compression Region of Interest Scalable image Compression Semantic Communications

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Digital image Semantic Communications with Joint Source-Channel Coding and Constellation Optimization 16

Digital Image Semantic Communications with Joint Source-Chan...

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16th International conference on Wireless Communications and signal processing, WCSP 2024

作者： Shen, Linzi Ma, Nan Chen, Jianqiao Song, Mengshu Dong, Chen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen518066 China Zgc Institute of Ubiquitous-X Innovation and Applications The 6G R&d Department Beijing100876 China

ISBN: (纸本)9798350390643

Recently developed semantic communication facilitates an intelligent and minimalist approach to meet the growing demands for future sixth-generation (6G) communication sys-tems. However, since the constraints of backpropagation during neural network training, the predominant approach of most extant studies are based on differentiable channel inputs, which is difficult to match with existing digital communication systems effectively. Meanwhile, the research on modulation techniques considering constellation optimization for digital semantic communication is still in the early stage. To deal with these issues, we propose a novel deep joint source-channel coding (DJSCC)-based digital semantic communication with considering constellation optimization, which is referred to as DJSCC-C. Firstly, the digital semantic communication system is introduced. Secondly, the DJSCC-C system is proposed for image transmission, which is trained with a newly-constructed loss function, aiming to improve the transmission rate in the digital semantic communication system as well as optimize the joint source-channel coding capability. Finally, simulation results are provided to verify the superiority of the proposed scheme for future design of digital semantic communication systems. © 2024 IEEE.

关键词： Channel coding

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conference Program

Conference Program

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IEEE International conference on signal and image processing applications (ICSIPA)

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WiSPNET 2023 - International conference on Wireless Communications, signal processing and Networking

WiSPNET 2023 - International Conference on Wireless Communic...

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2023 International conference on Wireless Communications, signal processing and Networking, WiSPNET 2023

ISBN: (纸本)9798350300451

The proceedings contain 34 papers. The topics discussed include: realization of electronically controllable, wide bandwidth instrumentation amplifier using single DVCCTA;study of characteristics and applications of microwave photonic radar;disaster-news datasets for multi-label document classification, sentence classification, and abstractive document summarization tasks;design and simulation of a novel cell interaction based square calculator in quantum-dot cellular automata;design of noise transfer function for delta sigma modulator based on modified Jacobi polynomial approximations;customized CNN with Adam and Nadam optimizers for emotion recognition using facial expressions;maximizing highway safety through ai-enabled detection of pedestrians and animals in V2x environments;texture image classification with dilated convolution layers;quad port pattern reconfigurable antenna for indoor applications;a multi-objective weight based optimal cluster head selection for lifetime augmentation in IoT based heterogeneous wireless sensor networks;and enhanced deep convolutional neural network for identifying and classification of silicon wafer faults in IC fabrication industries.

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image Transmission and Denoising under non-Kolmogorov Turbulence 15

Image Transmission and Denoising under non-Kolmogorov Turbul...

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15th International conference on Information Optics and Photonics, CIOP 2024

作者： xu, Meiyong Wu, Dongfang Wang, Yutong Lu, xinning Wan, Jian Wang, Kaimin Zhang, Dawei Beijing University of Posts and Telecommunications Beijing100876 China Jinhua Research Institute of Zhejiang University Jinhua321017 China Ministry of Education Shanghai Key Lab of Modern Optical System Shanghai Environmental Biosafety Instruments and Equipment Engineering Technology Research Center University of Shanghai for Science and Technology Shanghai200093 China Shanghai Pudong New Area People's Hospital Shanghai201399 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

image signal degradation of computational ghost imaging is an important issue in atmospheric turbulence, especially for the images in laboratory safety monitoring. However, the relevant research under non-Kolmogorov turbulence model has been rarely reported. To address this issue, we proposed an efficient image signal processing and transmission scheme tailored for low-sampling conditions to enhance image signal denoising for applications like educational laboratory safety monitoring. Our approach employed computational ghost imaging enhanced by a modified sequence on a Hadamard orthogonal basis. Then we simulated turbulence using a non-Kolmogorov spectrum mode, which a phase screen approach realized. We applied a bucket detector to capture light intensities reflected or scattered from the target, and reconstructed the target image from captured signals with the computational ghost imaging algorithms. The orthogonally modified Hadamard matrix patterns with a compress ratio were applied to reduce noise and enhance image quality. Simulations show significant image quality improvements over traditional schemes. © 2024 SPIE.

关键词： image quality

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