Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

From text to image:challenges in integrating vision into ChatGPT for medical image interpretation

Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

Between the City and Images:An Analysis of Mainstream Media’s Paths of Constructing the Cultural Memory of a City:Taking Chengdu Radio and Television’s“Hi Chengdu”as an Example

Mainstream media play a crucial role in constructing the cultural memory of a city.This study used 319 short videos released by“Hi Chengdu,”a new media product of Chengdu Radio and Television,as samples.Based on the grounded theory,a research framework encompassing“content,technology,and discourse”was established to explore the paths through which mainstream media construct the cultural memory.Regarding content,this paper emphasized temporal and spatial contexts and urban spaces,delving deep into the themes of the cultural memory and vehicles for it.In terms of technology,this paper discussed the practice of leveraging audio/visual-mode discourse to stitch together the impressions of a city and evoke emotional resonance to create a“flow”of memory.As for discourse,this paper looked at the performance of a communication ritual to frame concepts and shape urban identity.It is essential to break free from conventional thinking and leverage local culture as the primary driving force to further boost a city’s productivity,in order to excel in cultural communication.

Migration images guided high-resolution velocity modeling based on fully convolutional neural network

Current data-driven deep learning(DL)methods typically reconstruct subsurface velocity models directly from pre-stack seismic records.However,these purely data-driven methods are often less robust and produce results that are less physically interpretative.Here,the authors propose a new method that uses migration images as input,combined with convolutional neural networks to construct high-resolution velocity models.Compared to directly using pre-stack seismic records as input,the nonlinearity between migration images and velocity models is significantly reduced.Additionally,the advantage of using migration images lies in its ability to more comprehensively capture the reflective properties of the subsurface medium,including amplitude and phase information,thereby to provide richer physical information in guiding the reconstruction of the velocity model.This approach not only improves the accuracy and resolution of the reconstructed velocity models,but also enhances the physical interpretability and robustness.Numerical experiments on synthetic data show that the proposed method has superior reconstruction performance and strong generalization capability when dealing with complex geological structures,and shows great potential in providing efficient solutions for the task of reconstructing high-wavenumber components.

The Discourse-Historical Approach of Identity Construction in Corporate Promotional Videos for External Publicity Image

Corporate identity construction of external publicity image is an important part of the development of enterprises.Based on Wodak’s discourse-historical approach,this study takes the text of COFCO’s English promotional video as the research object,analyzes the corporate brand image,media image,organizational image,and environmental image constructed by the enterprises from three steps:linguistic expression,discourse strategy,and theme to provide references for Chinese enterprises to enhance their international influence.

The Problems and Upgrade Strategies for City Symbolic Image of Yibin in Sichuan Province

The connotation of city symbolic image was redefined in this study.Based on this,the main shortages of Yibin City symbolic image were listed and analyzed with an emphasis.And then the specific strategies to improve intrinsic image and symbolic image of the city were proposed with a pertinence,so as to provide reference for establishment of Yibin City symbolic image and development of export-oriented economy.

The Study on Tourist Destination Tourism Image Perception and Evaluation in Xi'an City

[Objective] The aim was to explore the tourist destination image measurement method, and have an empirical study on Xi＇an City. [Method] With combination of non-structural and structured approach to design questionnaires, tourism image of Xi＇an was explored using the SPSS software from both the qualitative and quantitative analyses. [Result] Xi＇an tourism images serve a heritage historical monuments atmosphere with the Terracotta Warriors, City Wall, Big Wild Goose Pagoda,Huaqing Hot Springs, presenting a positive feeling among tourists in general. In the dissemination of travel, tourism infrastructure construction, tourism transportation,travel services have negative effects on the image of Xi＇an tourism. [Conclusion]The evaluation on the tourism resources is highly spoken of by tourists than the scenic spot, indicating that despite high visibility of tourism resources in Xi＇an, it has not formed strong core competitiveness and a complete tourism industrial chain. It is necessary to take the right marketing strategies to enhance tourist familiarity to Xi＇an. Tourists demands, therefore, should be well considered in developing Xi＇an scenic spots and new tourism products and projects be focused on to improve tourists＇ satisfaction.

Analysis of the Tourism Image of Urban Waterfront Nanjing City

The quantitative analysis method was used to measure the tourism image of Nanjing waterfront to make clear its composition.An evaluation method was also adopted in terms of cognitive image,affective image and the whole image,which may offer some references for the evaluation of other tourism images in urban waterfront of the same kind.

Study on Spatial Heterogeneity of Urban Green Land Based on ETM+ Image in Shenzhen City

[Objective] To discuss the influence of the spatial heterogeneity of different green land types on their corresponding ecological efficiency and functions. [Method] ETM+ Images are utilized to classify urban green land into three categories, i.e. park green land, affiliated green land and other green lands; the principles of geostatistics are adopted to analyze their spatial heterogeneity. [Result] Spatial heterogeneity of different green land types differs greatly, which follows the scale law of 'affiliated green land < other green lands < park green land'. [Conclusion] The ecological functions and process of different green land types are different in scale; and the shapes of semi-variance functions are closely related to the spatial range, size and variety of ground objects, which however differ slightly in different directions, look similar or tend to isotropy.

Analysis on the Landscape Image of Pingle Old Town in Chengdu City from the Perspective of City Image

With Kevin Lynch's mode of analysis in The Image of the City,through field research and inductive analysis on the Pingle Old Town,the image of Chengdu Pingyuan Town represented by Pingle Town is summarized.Analysis and conclusion of the characteristics and identifiabilities of the traditional market town are made in five aspects,mainly including the path of the town,edge,district,central node and landmark,which provides a theoretical reference and example for preserving and shaping the traditional market town of region characteristic in Chengdu Plain.

Study of Diesel Spray Particle Velocity Field Using a Particle Image Velocimetry Setup

Aim To investigate the spray particle velocity and its distribution characteristics. Methods\ A set of PIV (particle image velocimetry) system was developed and used to observe and analyze the spray particle velocity field. Results and Conclusion\ Double exposure image of the spray particle within the region of 10-50 mm from the nozzle tip was recorded and analyzed by the IBAS2000 analysis system. Some characteristics of the spray particle velocity and its distribution were obtained.

Tourism Image Reform of Qianling Mountain Park in Guiyang City,China

By analyzing tourism image and its relevant concepts,tourism image was stressed as the core factor attracting the attention of tourists,and attention resources have already become precious economic resources.According to present tourism image of Qianling Mountain Park,problems and major influencing factors,it was found that its dominant tourism image was misoriented,its advantages were not fully demonstrated;its pillar image was not strong enough,tourism functional items were not properly arranged;secondary image was not given adequate attention.So it is imperative to remold the tourism image of Qianling Mountain Park.Based on the above analysis,reform principles were introduced,and influencing factors of its tourism image were analyzed from the perspectives of characteristics of tourist resources,perception and cognition of tourists,spatial competition of tourist sites,orientation and accessibility of tourist market.Furthermore,strategies for the reform design of tourism image of Qianling Mountain Park were summarized in view of the reform of its dominant image,pillar image and secondary image,and it was also stressed that well-coordinated planning and promotion was needed for popularizing the new image.

Reconstruction of Urban Landscapes and Images——A Case Study of the Post-Disaster Reconstruction of Luoshui Township, Shifang City, Sichuan, China

The reconstruction of Luoshui Township after the Wenchuan Earthquake was taken for an example, geological location, cultural context and original image of the town were analyzed on the basis of historical records and full investigation, functional orientations, cultural elements and landscape elements were summarized. It was proposed that the reconstruction of its landscapes and images should pay more attention to the integration of natural environment and town, function and form, as well as the inheritance and promotion of local cultures, stress "human-centered" designs and planning techniques of "integrating into natural landscapes, shaping the soul of culture". On this basis, planning schemes were given, design focuses, overall structure, planar architectural layout, style selection, landscape design and green space system planning were analyzed, moreover, how to deal with the relationship between city and people, city and environment, city and culture was discussed to find a way to the orderly and sustainable development of city.

Dendritic Learning-Incorporated Vision Transformer for Image Recognition

Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks.

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

The Soft X-ray Imager(SXI)on the SMILE Mission

The Soft X-ray Imager(SXI)is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.SMILE is a joint science mission between the European Space Agency(ESA)and the Chinese Academy of Sciences(CAS)and is due for launch in 2025.SXI is a compact X-ray telescope with a wide field-of-view(FOV)capable of encompassing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit.SXI is sensitive to the soft X-rays produced by the Solar Wind Charge eXchange(SWCX)process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere.SWCX provides a mechanism for boundary detection within the magnetosphere,such as the position of Earth’s magnetopause,because the solar wind heavy ions have a very low density in regions of closed magnetic field lines.The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours.SXI is led by the University of Leicester in the United Kingdom(UK)with collaborating organisations on hardware,software and science support within the UK,Europe,China and the United States.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Method for evaluation of geological strength index of carbonate cliff rocks:Coupled hyperspectral-digital borehole image technique

The deterioration of unstable rock mass raised interest in evaluating rock mass quality.However,the traditional evaluation method for the geological strength index(GSI)primarily emphasizes the rock structure and characteristics of discontinuities.It ignores the influence of mineral composition and shows a deficiency in assessing the integrity coefficient.In this context,hyperspectral imaging and digital panoramic borehole camera technologies are applied to analyze the mineral content and integrity of rock mass.Based on the carbonate mineral content and fissure area ratio,the strength reduction factor and integrity coefficient are calculated to improve the GSI evaluation method.According to the results of mineral classification and fissure identification,the strength reduction factor and integrity coefficient increase with the depth of rock mass.The rock mass GSI calculated by the improved method is mainly concentrated between 40 and 60,which is close to the calculation results of the traditional method.The GSI error rates obtained by the two methods are mostly less than 10%,indicating the rationality of the hyperspectral-digital borehole image coupled evaluation method.Moreover,the sensitivity of the fissure area ratio(Sr)to GSI is greater than that of the strength reduction factor(a),which means the proposed GSI is suitable for rocks with significant fissure development.The improved method reduces the influence of subjective factors and provides a reliable index for the deterioration evaluation of rock mass.