Multimedia Tools and Applications最新文献_第7页

Image decomposition based segmentation of retinal vessels 基于图像分解的视网膜血管分割

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-09 DOI: 10.1007/s11042-024-20171-5

Anumeha Varma, Monika Agrawal

{"title":"Image decomposition based segmentation of retinal vessels","authors":"Anumeha Varma, Monika Agrawal","doi":"10.1007/s11042-024-20171-5","DOIUrl":"https://doi.org/10.1007/s11042-024-20171-5","url":null,"abstract":"Retinal vessel segmentation has various applications in the biomedical field. This includes early disease detection, biometric authentication using retinal scans, classification and others. Many of these applications rely critically on an accurate and efficient segmentation technique. In the existing literature, a lot of work has been done to improve the accuracy of the segmentation task, but it relies heavily on the amount of data available for training as well as the quality of the images captured. Another gap is observed in terms of the resources used in these heavily trained algorithms. This paper aims to address these gaps by using a resource-efficient unsupervised technique and also increasing the accuracy of retinal vessel segmentation using the Fourier decomposition method (FDM) along with the Gabor transform for image signals. The proposed method has an accuracy of 97.39%, 97.62%, 95.34%, and 96.57% on DRIVE, STARE, CHASE_DB1, and HRF datasets, respectively. The sensitivities were found to be 88.36%, 88.51%, 90.37%, and 79.07%, respectively. A separate section makes a detailed comparison of the proposed method with several well-known methods and an analysis of the efficiency of the proposed method. The proposed method proves to be efficient in terms of time and resource requirements.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cubixel: a novel paradigm in image processing using three-dimensional pixel representation 立方像素：使用三维像素表示法的新型图像处理范例

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-09 DOI: 10.1007/s11042-024-20081-6

Sanad Aburass

{"title":"Cubixel: a novel paradigm in image processing using three-dimensional pixel representation","authors":"Sanad Aburass","doi":"10.1007/s11042-024-20081-6","DOIUrl":"https://doi.org/10.1007/s11042-024-20081-6","url":null,"abstract":"This paper introduces the innovative concept of the Cubixel—a three-dimensional representation of the traditional pixel—alongside the derived metric, Volume of the Void (VoV), which measures spatial disparities within images. By converting pixels into Cubixels, we can analyze the image’s 3D properties, thereby enriching image processing and computer vision tasks. Utilizing Cubixels, we’ve developed algorithms for advanced image segmentation, edge detection, texture analysis, and feature extraction, yielding a deeper comprehension of image content. Our empirical experimental results on benchmark images and datasets showcase the applicability of these concepts. Further, we discuss future applications of Cubixels and VoV in various domains, particularly in medical imaging, where they have the potential to significantly enhance diagnostic processes. By interpreting images as complex ‘urban landscapes’, we envision a new frontier for deep learning models that simulate and learn from diverse environmental conditions. The integration of Cubixels into deep learning architectures promises to revolutionize the field, providing a pathway towards more intelligent, context-aware artificial intelligence systems. With this groundbreaking work, we aim to inspire future research that will unlock the full potential of image data, transforming both theoretical understanding and practical applications. Our code is available at https://github.com/sanadv/Cubixel.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improvement of Blowfish encryption algorithm based on Galois ring for electro-optical satellite images 基于伽罗瓦环的 Blowfish 加密算法在光电卫星图像上的改进

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-09 DOI: 10.1007/s11042-024-20169-z

Muhammad Javid, Majid Khan, Muhammad Amin

{"title":"Improvement of Blowfish encryption algorithm based on Galois ring for electro-optical satellite images","authors":"Muhammad Javid, Majid Khan, Muhammad Amin","doi":"10.1007/s11042-024-20169-z","DOIUrl":"https://doi.org/10.1007/s11042-024-20169-z","url":null,"abstract":"The desire for new algebraic structures is always an interesting area of research for the design and development of new information confidentiality mechanisms. Confidentiality mechanism keeps information secure from unauthentic users and transforms information into a ciphered format that cannot be easily deciphered. The key components of data security are encryption/decryption algorithms. Encryption/Decryption Algorithms depend on mathematical structures used for substitution box (S-box) and key scheduling. In the present research, the Galois ring-based S-box is used in the Blowfish confidentiality technique and practically implement for electro-optical satellite image ciphering. The results of data ciphering are compared with Blowfish cipher with the standard S-box. Ciphered images have been evaluated by standard tests such as histogram equalization, randomness, correlation, and differential assaults analysis. These tests depict that the ciphered data by Blowfish based on Galois ring-based S-box is more secure and the security is achieved in the fourth round for electro-optical satellite images. Test results for satellite images illustrate that by less execution time found better satellite image ciphering in the fourth ciphering round by using the proposed scheme. With this addition, subsequent steps of the existing algorithm were modified which add more robustness to our digital information. Security analysis of this tested mechanism added more robustness to the confidentiality of digital images. The results of the anticipated ciphering scheme clarify the better performance as compared to the standard Blowfish algorithm for satellite images.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing classification of lung diseases by optimizing training hyperparameters of the deep learning network 通过优化深度学习网络的训练超参数来增强肺部疾病的分类能力

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-09 DOI: 10.1007/s11042-024-20085-2

Hardeep Saini, Davinder Singh Saini

{"title":"Enhancing classification of lung diseases by optimizing training hyperparameters of the deep learning network","authors":"Hardeep Saini, Davinder Singh Saini","doi":"10.1007/s11042-024-20085-2","DOIUrl":"https://doi.org/10.1007/s11042-024-20085-2","url":null,"abstract":"The COVID-19 pandemic was triggered by the SARS-CoV-2 virus which caused multiple ill-health conditions in infected individuals. There were many cases that culminated in death. Chest X-ray images became a proven method for spotting thoracic ailments. The resultant availability of huge public datasets of chest X-ray images has great potential in deep learning for lung ailment detection. This paper presents a classification that aims at acquiring the optimal hyperparameters using the metaheuristic algorithm for various pre-trained CNN training processes. The experimental results show that HSAGWO (Hybrid Simulated Annealing Grey Wolf Optimization) outperforms the other contemporary models for optimizing training hyperparameters in the ResNet50 network. The accuracy, precision, sensitivity (recall), specificity, and F1-score values obtained are 98.78%, 98.10%, 99.31%, and 98.64%, respectively, which are significantly better than the values obtained for the existing methods. The objective of this work is to improve classification accuracy and reduce false negatives while keeping computational time to a minimum.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized congestion control protocol in cellular network for improving quality of service 优化蜂窝网络拥塞控制协议，提高服务质量

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-09 DOI: 10.1007/s11042-024-20126-w

Sandhya S. V, S. M. Joshi

{"title":"An optimized congestion control protocol in cellular network for improving quality of service","authors":"Sandhya S. V, S. M. Joshi","doi":"10.1007/s11042-024-20126-w","DOIUrl":"https://doi.org/10.1007/s11042-024-20126-w","url":null,"abstract":"In recent decades, Cellular Networks (CN) have been used broadly in communication technologies. The most critical challenge in the CN was congestion control due to the distributed mobile environment. Some approaches, like mobile edge computing, congesting controlling systems, machine learning, and heuristic models, have failed to prevent congestion in CN. The reason for this problem is the lack of continuous monitoring function at every time interval. So, in this present study, a novel Golden Eagle-based Primal–dual Congestion Management (GEbPDCM) has been developed for the Long-Term Evolution (LTE) Ad hoc On-demand Vector (AODV) network. Here, the Golden Eagle function features will afford the continuous monitoring function to monitor data congestion. Hence, the main objective of this research is to improve the Quality of service (QoS) by optimizing congestion controls. Here, the QoS is measured by different metrics, such as delay, packet delivery ratio (PDR), throughput, packet loss, and energy consumption. Initially, the nodes were created in the MATLAB environment, and the GEbPDCM was activated to predict the data load and estimate the node density to measure the node status. Then, the high data overload was migrated to another free status node to control congestion. Finally, the proposed model efficiency was measured regarding delay, packet delivery ratio (PDR), throughput, packet loss, and energy consumption. The proposed model has scored high throughput at 97.1 Mbps and 97.1 PDR, reducing delay to 67.4 ms and 50.6 mJ energy consumption. Hence, the present model is suitable for the LTE network.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmented reality without SLAM 无 SLAM 的增强现实

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-06 DOI: 10.1007/s11042-024-20154-6

Aminreza Gholami, Behrooz Nasihatkon, Mohsen Soryani

{"title":"Augmented reality without SLAM","authors":"Aminreza Gholami, Behrooz Nasihatkon, Mohsen Soryani","doi":"10.1007/s11042-024-20154-6","DOIUrl":"https://doi.org/10.1007/s11042-024-20154-6","url":null,"abstract":"Most augmented reality (AR) pipelines typically involve the computation of the camera’s pose in each frame, followed by the 2D projection of virtual objects. The camera pose estimation is commonly implemented as SLAM (Simultaneous Localisation and Mapping) algorithm. However, SLAM systems are often limited to scenarios where the camera intrinsics remain fixed or are known in all frames. This paper presents an initial effort to circumvent the pose estimation stage altogether and directly computes 2D projections using epipolar constraints. To achieve this, we initially calculate the fundamental matrices between the keyframes and each new frame. The 2D locations of objects can then be triangulated by finding the intersection of epipolar lines in the new frame. We propose a robust algorithm that can handle situations where some of the fundamental matrices are entirely erroneous. Most notably, we introduce a depth-buffering algorithm that relies solely on the fundamental matrices, eliminating the need to compute 3D point locations in the target view. By utilizing fundamental matrices, our method remains effective even when all intrinsic camera parameters vary over time. Notably, our proposed approach achieved sufficient accuracy, even with more degrees of freedom in the solution space.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting breast cancer molecular subtypes from H &E-stained histopathological images using a spatial-transcriptomics-based patch filter 利用基于空间转录组学的补丁过滤器从 H & E 染色组织病理学图像中预测乳腺癌分子亚型

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-06 DOI: 10.1007/s11042-024-20160-8

Yuqi Chen, Juan Liu, Lang Wang, Peng Jiang, Baochuan Pang, Dehua Cao

{"title":"Predicting breast cancer molecular subtypes from H &E-stained histopathological images using a spatial-transcriptomics-based patch filter","authors":"Yuqi Chen, Juan Liu, Lang Wang, Peng Jiang, Baochuan Pang, Dehua Cao","doi":"10.1007/s11042-024-20160-8","DOIUrl":"https://doi.org/10.1007/s11042-024-20160-8","url":null,"abstract":"The molecular subtype of breast cancer plays an important role in the prognosis of patients and guides physicians to develop scientific therapeutic regimes. In clinical practice, physicians classify molecular subtypes of breast cancer with immunohistochemistry(IHC) technology, which requires a long cycle for diagnosis, resulting in a delay in effective treatment of patients with breast cancer. To improve the diagnostic rate, we proposed a machine learning method that predicted molecular subtypes of breast cancer from H&E-stained histopathological images. Although some molecular subtype prediction methods have been suggested, they are noisy and lack clinical evidence. To address these issues, we introduced a patch filter-based molecular subtype prediction (PFMSP) method using spatial transcriptomics data, training a patch filter with spatial transcriptomics data first, and then the trained filter was used to select valuable patches for molecular subtype prediction in other H&E-stained histopathological images. These valuable patches contained one or more genes expressed of ESR1, ESR2, PGR, and ERBB2. We evaluated the performance of our method on the spatial transcriptomics(ST) dataset and the TCGA-BRCA dataset, and the patches filtered by the patch filter achieved accuracies of 80% and 73.91% in predicting molecular subtypes on the ST and TCGA-BRCA datasets, respectively. Experimental results showed that the use of the trained patch filter to filter patches was beneficial to improving precision in predicting molecular subtypes of breast cancer.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach 利用 mRMR + SS0 + WSVM 改进乳腺癌分类：一种混合方法

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-06 DOI: 10.1007/s11042-024-20146-6

Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz

{"title":"Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach","authors":"Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz","doi":"10.1007/s11042-024-20146-6","DOIUrl":"https://doi.org/10.1007/s11042-024-20146-6","url":null,"abstract":"Detecting breast cancer through histopathological images is time-consuming due to their volume and complexity. Speeding up early detection is crucial for timely medical intervention. Accurately classifying microarray data faces challenges from its dimensionality and noise. Researchers use gene selection techniques to address this issue. Additional techniques like pre-processing, ensemble, and normalization procedures aim to improve image quality. These can also impact classification approaches, helping resolve overfitting and data balance issues. A more sophisticated version could potentially boost classification accuracy while reducing overfitting. Recent technological advances have driven automated breast cancer diagnosis. This research introduces a novel method using Salp Swarm Optimization (SSO) and Support Vector Machines (SVMs) for gene selection and breast tumor classification. The process involves two stages: mRMR preselects genes based on their relevance and distinctiveness, followed by SSO-integrated WSVM for classification. WSVM, aided by SSO, trims redundant genes and assigns weights, enhancing gene significance. SSO also fine-tunes kernel parameters based on gene weights. Experimental results showcase the effectiveness of the mRMR-SSO-WSVM method, achieving high accuracy, precision, recall, and F1-score on breast gene expression datasets. Specifically, our approach achieved an accuracy of 99.62%, precision of 100%, recall of 100%, and an F1-score of 99.10%. Comparative analysis with existing methods demonstrates the superiority of our approach, with a 4% improvement in accuracy and a 3.5% increase in F1-score over traditional SVM-based methods. In conclusion, this study demonstrates the potential of the proposed mRMR-SSO-WSVM methodology in advancing breast cancer classification, offering significant improvements in performance metrics and effectively addressing challenges such as overfitting and data imbalance.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient crack detection and leakage monitoring in liquid metal pipelines using a novel BRetN and TCK-LSTM techniques 使用新型 BRetN 和 TCK-LSTM 技术高效检测液态金属管道中的裂缝并进行泄漏监测

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-06 DOI: 10.1007/s11042-024-20170-6

Praveen Sankarasubramanian

{"title":"An efficient crack detection and leakage monitoring in liquid metal pipelines using a novel BRetN and TCK-LSTM techniques","authors":"Praveen Sankarasubramanian","doi":"10.1007/s11042-024-20170-6","DOIUrl":"https://doi.org/10.1007/s11042-024-20170-6","url":null,"abstract":"Nowadays, the pipeline system has the safest, most economical, and most efficient means of transporting petroleum products and other chemical fluids. But, the faults in pipelines cause resource wastage and environmental pollution. Most of the existing works focused either on the surface Crack Detection (CD) or Leakage Detection (LD) of pipes with limited features. Hence, efficient crack detection and leakage monitoring are proposed based on the Acoustic Emission (AE) signal and AE image features using a new Berout Retina Net (BRetN) and Tent Chaotic Kaiming-centric Long Short Term Memory (TCK-LSTM) methodologies. The process initiates from the gathering of input data, followed by preprocessing. Then, the cracks are detected by utilizing Berout Retina Net (BRetN), and the features of AE signals are retrieved. On the other hand, the AE signal is transformed into an AE image using Continuous Wavelet Transform (CWT). Further, the AE image features are extracted, followed by the integration of both the AE signal and AE image features. Further, the optimal features are chosen by using Gorilla Troops Optimizer (GTO). Eventually, the TCK-LSTM model is used for detecting the leakage level of the pipeline. The experimental outcomes illustrated that the proposed framework detected crack and leakage levels with 98.14% accuracy, 95.37% precision, and 98.84% specificity when analogizing over the existing techniques.","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deception detection with multi-scale feature and multi-head attention in videos 利用视频中的多尺度特征和多头注意力进行欺骗检测

IF 3.6 4区计算机科学

Multimedia Tools and Applications Pub Date : 2024-09-06 DOI: 10.1007/s11042-024-20124-y

Shusen Yuan, Guanqun Zhou, Hongbo Xing, Youjun Jiang, Yewen Cao, Mingqiang Yang

引用次数: 0