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A Statistical Approach for Improving image Quality and Cell Quantification from Imaging Mass Cytometry 9

A Statistical Approach for Improving Image Quality and Cell ...

引用

9th international conference on Communication, image and signal Processing, CCISP 2024

作者： Xiao, Xu Lin, Yating Zhang, Lei Yang, Wenxian Yu, Rongshan School of Informatics Xiamen University Xiamen China Aginome Scientific Xiamen China

ISBN: (纸本)9798350356656

Imaging Mass Cytometry (IMC), a multiplexed imaging technology, has become a valuable tool in biomedical research due to its capability to measure over 100 markers theoretically. However, the presence of noise in IMC images can affect cell phenotyping and the precision of clinical analysis. To address this challenge, we propose IMCell, a highly effective and generalizable method that enhances image quality and cell quantification by leveraging biological priors. IMCell begins by generating decoy cells using Monte Carlo sampling and incorporates cell shape information for each protein channel. Subsequently, it estimates background noise from the expression of decoy cells by fitting Gaussian mixture models with the EM algorithm. Additionally, it identifies positive cell expressions by comparing distribution patterns between real cells and decoy cells. based on the analysis results from IMCell, we further introduce an image quality index for comprehensive image quality assessment by combining noise and positive cell expressions. Experiment results demonstrate the efficacy of our proposed approach in enhancing image quality and facilitating downstream analysis. By improving the quality of IMC images, IMCell holds the potential to enhance our understanding of complex biological systems and disease mechanisms. © 2024 ieee.

关键词： Biomedical imaging EM algorithm Gaussian mixture model image quality enhancement IMC

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Comparative Analysis of Local Adaptive LMS image Filtration 58

Comparative Analysis of Local Adaptive LMS Image Filtration

引用

58th international Scientific conference on Information, Communication and Energy systems and Technologies (ICEST)

作者： Mironov, Rumen P. Draganov, Ivo R. Tech Univ Sofia Fac Telecommun Bulv Kl Ohridsky 8 Sofia 1000 Bulgaria

ISBN: (纸本)9798350310733

In this paper the 2D part of the developed method for local adaptive 3D filtration of multidimensional grayscale images is compared with some advanced methods for image filtrations - BM3D, Average, Gaussian and etc. The adaptation of the presented algorithm is based on the information from a small local area of the neighbouring pixels of the processed images using the developed 2D Adaptive LMS filter. A comparative analysis of the quality of the processed images was made in terms of both peak signal-to-noise ratio calculations and subjective visual quality. The experimental results were carried out with a set of test halftone images that were noised with Additive White Gaussian Noise (AWGN) with different variance levels.

关键词： Digital signal processing Local adaptive image filtration 2D LMS adaptation image denoising

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Cross-Modality Guided Multi-Scale Feature Representation based image Compression for Industrial Scene Understanding 22

Cross-Modality Guided Multi-Scale Feature Representation Bas...

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22nd ieee international conference on Industrial Informatics, INDIN 2024

作者： Tu, Jingzheng Yan, Yan Lu, Ying Chen, Cailian Zhou, Shiru China Academy of Aerospace Science and Innovation Beijing100088 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9798331527471

Industrial surveillance videos capture subsequent video frames and deliver them to processing servers for video analytics. In such a case, image compression is a promising technology in industrial scene understanding due to its reduced transmission burden. Existing works focus on establishing macroscopic visual feature maps that contain compact image information. In this article, a cross-modality guided multi-scale feature representation based image compression method is proposed for understanding industrial scenes. Firstly, an image compressing framework with multi-grained representation (MGRIC) is proposed for industrial scene understanding, which builds image patch, structure, semantic, and signal levels feature maps. Secondly, an encoder-decoder architecture is designed for image compression with diverse granularities. Thirdly, an interaction policy between different levels is developed to ensure the subsequent level’s decoding. Experiments on the CUB-200-2011 dataset demonstrate that the proposed MGRIC method achieves competitive performance, which verifies its effectiveness. © 2024 ieee.

关键词： Decoding

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Deformation Field Component Attention Fusion for Coarse-to-Fine Medical image Registration Network 9

Deformation Field Component Attention Fusion for Coarse-to-F...

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9th international conference on Intelligent Computing and signal Processing, ICSP 2024

作者： Zhang, Chi Zhao, Haifeng Liu, Deyin School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China School of Artificial Intelligence Anhui University Hefei China

ISBN: (纸本)9798350376548

In recent years, deep learning-based medical image registration algorithms have demonstrated notable achievements in improving registration accuracy and speed. However, these algorithms have not explored the potential benefits of utilizing different networks to generate distinct deformation fields, which can then be integrated to produce a final deformation field. In this paper, we propose deformation field component attention fusion for coarse-to-fine medical image registration. Specifically, the registration process is divided into multiple stages, which generate different deformation fields that focus on capturing deformations of varying degrees. To ensure the effective integration of multiple deformation fields, we propose the Deformation Field Component Attention Fusion Module. This module separates the deformation field into three axial displacement components and performs independent attention fusion for the different axial displacement components. Through comparative experiments and ablation studies, we demonstrate the effectiveness of the proposed method. © 2024 ieee.

关键词： image registration

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MS-RainMamba: Learning Multi-Scale State Space Models for Single image Deraining

MS-RainMamba: Learning Multi-Scale State Space Models for Si...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Li, Haibo Liu, Zhanshuo Zhao, Tuo Zhao, Tingting Chen, Yarui Xie, Ning College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China Tokyo Japan Center for Future Media University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Despite the significant advances of Convolutional neural networks (CNNs) and Transformers in image deraining, they either suffer from limited receptive fields or incur quadratic complexity, leading to an imbalance between performance and efficiency. Recently, state space models (SSMs) have demonstrated significant potential in modeling long-range dependencies while maintaining linear complexity. However, existing Mamba-based approaches lack the exploration of useful complementary information from multiple image scales, which could be beneficial for facilitating rain removal. In this paper, we propose an effective multi-scale state-space model-based framework (MS-RainMamba) to explore richer scale-space information for better image deraining. Specifically, we design a local-enhanced state space module to better aggregate rich local and global information. In contrast to existing methods that adopt fixed-scale scanning for feature extraction, we develop a multi-scale hierarchical 2D scanning technique to better help image restoration. Experimental results on six benchmarks show that the proposed method performs favorably against state-of-the-art models. © 2025 ieee.

关键词： image deraining Mamba Multi-scale learning Rain removal State space model

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Segment Anything Model (SAM) Meets Object Detected Box Prompts 25

Segment Anything Model (SAM) Meets Object Detected Box Promp...

引用

25th ieee international conference on Industrial technology, ICIT 2024

作者： Akin, Erdal Caltenco, Héctor Adewole, Kayode S. Malekian, Reza Persson, Jan A. Malmö University Department Of Computer Science And Media Technology Malmö205 06 Sweden Malmö University Internet Of Things And People Centre Malmö205 06 Sweden Bitlis Eren University Computer Engineering Department Bitlis13100 Turkey Ericsson Ab Ericsson Research Lund Sweden

ISBN: (纸本)9798350340266

Segmenting images is an intricate and exceptionally demanding field within computer vision. Instance Segmentation is one of the subfields of image segmentation that segments objects on a given image or video. It categorizes the class labels according to individual instances, ensuring that distinct instance markers are assigned to each occurrence of the same object class, even if multiple instances exist. With the development of computer systems, segmentation studies have increased very rapidly. One of the state-of-the-art algorithms recently published by Meta AI, which segments everything on a given image, is called the Segment Anything Model (SAM). Its impressive zero-shot performance encourages us to use it for diverse tasks. Therefore, we would like to leverage the SAM for an effective instance segmentation model. Accordingly, in this paper, we propose a hybrid instance segmentation method in which Object Detection algorithms extract bounding boxes of detected objects and load SAM to produce segmentation, called Box Prompted SAM (BP-SAM). Experimental evaluation of the COCO2017 Validation dataset provided us with promising performance. © 2024 ieee.

关键词： Object detection

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Smart Lock for Enhancing Luggage Security Using internet of Things

Smart Lock for Enhancing Luggage Security Using Internet of ...

引用

2024 ieee international conference on Communication, Computing and signal Processing, IICCCS 2024

作者： Roy, Nirban Chakraborty, Shuvam Jana, Susovan Kolkata India Institute of Engineering & Management Department of Information Technology Kolkata India Kolkata India

ISBN: (纸本)9798350390759

The escalating incidents of luggage tampering and theft underscore the necessity for robust security measures tailored to modern travelers' needs. This paper presents a pioneering approach to bolstering luggage security for contemporary travelers through an IoT-based Smart Lock system. With a surge in luggage tampering and theft incidents, conventional security measures have proven inadequate, necessitating innovative solutions. Our Smart Lock system leverages IoT technology to seamlessly integrate with mobile applications, enabling remote locking/unlocking and advanced security functionalities. Key components including NodeMCU, ESP32cam, and vibration sensors work in tandem to ensure robust security. Integration with the Blynk IoT App facilitates intuitive control and alarm setting. Notably, the system incorporates face recognition for intruder detection, further enhancing security measures. Rigorous testing validates the system's efficacy in detecting intruders and ensuring reliable locking operations. © 2024 ieee.

关键词： Face recognition

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Analysis and Research of Spinal CT image Segmentation based on Improved Watershed Algorithm 3

Analysis and Research of Spinal CT Image Segmentation Based ...

引用

3rd ieee international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2023

作者： He, Yuting Wang, Shigang Gao, Xueshan School of Automation Guangxi University of Science and Technology Liuzhou China

ISBN: (纸本)9798350333862

The traditional watershed is easy to be affected and then produces over-segmentation. In order to reduce the image over-segmentation, it is necessary to reduce the irrelevant information and enhance the foreground and background contrast of the spine CT images by improving the threshold segmentation. After marking the foreground and background, the watershed algorithm works in its restricted area by modifying the original gradient function. By experimental comparative analysis, the proposed method effectively restricts the image over-segmentation and also removes the holes made by the influence of noise. At the same time, the improved algorithm has universal applicability to the spine images to process pictures for saving time and laying a good foundation for the subsequent reconstruction. © 2023 ieee.

关键词： image segmentation

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An Improved image Defogging Algorithm based on Dehazenet Network 2

An Improved Image Defogging Algorithm Based on Dehazenet Net...

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2nd international conference on signal Processing and Intelligent Computing, SPIC 2024

作者： Bai, Jingping Ren, Shiqing Fang, Zhao Shenyang Ligong University School of Information Science and Engineering Shenyang China Guangzhou College of Technology and Business Guangzhou China

ISBN: (纸本)9798350368888

The image dehaze technology is particularly important. This paper adopts the traditional physical model and deep learning network model approach, using the improved dehazenet network, using three different scales of convolution kernel for feature extraction of the original foggy image, and in the feature learning process using 3×3 tiny convolutional kernel to increase the model's depth;in order to avoid the network overfitting as well as to enhance the convergence speed of the network, the batch after convolution was added Batch Normalization processing is added after the convolution to estimate the transmittance t , and the original bootstrap filter is improved to a weighted bootstrap filter so as to better strengthen the edge information, and the quadtree method is used to estimate the atmospheric light value A instead of the original method to ensure a more accurate estimation of the atmospheric light. Using RESIDE dataset, the outdoor image with sky, the building image and the indoor restaurant image are used for the defogging experiments and the three values of PSRN, MSE and SSIM are evaluated. © 2024 ieee.

关键词： Deep learning

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An internet of Things Assisted Autism Spectrum Disorder Prediction Using Deep Learning Principles with Medical image Processing Assistance

An Internet of Things Assisted Autism Spectrum Disorder Pred...

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2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Bindhu, A. Mol, A. Sruthi Ravi, T. Bodkhe, R.G. Safori, Amjad Omar Vijayalakshmi, S. Infant Jesus College of Arts and Science for Women Department of Computer Science Mulagumoodu India Sathyabama Institute of Science and Technology Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Zaraa University Faculty of Media Department of Journalism and Digital Media Zaraa 13110 Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Characteristics of the complex neuro-developmental condition Autism Spectrum condition (ASD) include impaired social communication and restricted, repetitive patterns of behavior. This study presents a novel approach for identifying ASD in young infants by integrating deep learning, internet of Things (IoT) and medical image processing. The goal of this research is to create comprehensive and discriminative features by combining wearable sensor data with brain imaging and smart home environments. For the greatest results, early diagnosis is essential. The proposed method called IoT based Autism Predictive Framework (IoTAPF) has a high detection rate of 97.8% and is cross-validated against the current Learning based Autism Detection Strategy (LADS). The proposed approach aims at improving diagnostic specificity which helps in forecasting behaviors and thus pushing earlier interventions, providing better support for ASD individuals and their families. The paper discusses the design and implementation of the framework, including sensor integration, data management, and predictive modeling. Preliminary results from pilot studies are presented, demonstrating the framework's potential to improve diagnostic accuracy and support individualized care plans. The proposed solution represents a significant advancement in the application of IoT technology for early autism detection and management, paving the way for more proactive and data-driven approaches in the field of neuro-developmental disorders. © 2024 ieee.

关键词： Diseases

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