Advanced Image Processing Methods Research Articles

Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques

Abstract In the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.

Faster R-CNN-CA and thermophysical properties of materials: An ancient marquetry inspection based on infrared and terahertz techniques

The demand for non-invasive inspection (NII) is ever-increasing in the field of cultural heritage conservation. NII is a two-step procedure, first of data acquisition and second of defect detection. Stand-alone imaging techniques such as infrared thermography (IRT) are often insufficient for performing a complete remote analysis and diagnosis of historic structures and art pieces that are of very high cultural value. On this point, an emerging optical inspection method, terahertz time-domain spectroscopy (THz-TDS), is herein employed to provide more details of deeper defects. The imaging results from THz-TDS and IRT are compared and analyzed by employing advanced image processing methods. Next, to achieve automatic inspection of the test sample, which is an ancient marquetry, a Faster R-CNN with coordinate attention (Faster R-CNN-CA) is proposed and fitted with data from two different sources. Worth noting is that, in order to populate sufficient data for training, samples are simulated using finite element analysis and finite difference time domain method. The experiments demonstrate that the mean average precision of the Faster R-CNN-CA model improves by 6.09% over the traditional Faster R-CNN model.

Passive Vision Detection of Torch Pose in Swing Arc Narrow Gap Welding.

To enhance the synchronous detection of the horizontal and vertical positions of the torch in swing arc narrow gap welding, a torch pose detection (TPD) method is proposed. This approach utilizes passive visual sensing to capture images of the arc on the groove sidewall, using advanced image processing methods to extract and fit the arc contour. The coordinates of the arc contour center point and the highest point are determined through the arc contour fitting line. The torch center position is calculated from the average horizontal coordinates of the arc contour centers in adjacent welding images, while the height position is determined from the vertical coordinate of the arc's highest point. Experimental validation in both variable and constant groove welding conditions demonstrated the TPD method's accuracy within 0.32 mm for detecting the torch center position. This method eliminates the need to construct the wire centerline, which was a requirement in previous approaches, thereby reducing the impact of wire straightness on detection accuracy. The proposed TPD method successfully achieves simultaneous detection of the torch center and height positions, laying the foundation for intelligent detection and adaptive control in swing arc narrow gap welding.

Measurement of Scapholunate Joint Space Width on Real-Time MRI-A Feasibility Study.

The scapholunate interosseous ligament is pivotal for wrist stability, and its impairment can result in instability and joint degeneration. This study explores the application of real-time MRI for dynamic assessment of the scapholunate joint during wrist motion with the objective of determining its diagnostic value in efficacy in contrast to static imaging modalities. Ten healthy participants underwent real-time MRI scans during wrist ab/adduction and fist-clenching maneuvers. Measurements were obtained at proximal, medial, and distal landmarks on both dynamic and static images with statistical analyses conducted to evaluate the reliability of measurements at each landmark and the concordance between dynamic measurements and established static images. Additionally, inter- and intraobserver variabilities were evaluated. Measurements of the medial landmarks demonstrated the closest agreement with static images and exhibited the least scatter. Distal landmark measurements showed a similar level of agreement but with increased scatter. Proximal landmark measurements displayed substantial deviation, which was accompanied by an even greater degree of scatter. Although no significant differences were observed between the ab/adduction and fist-clenching maneuvers, both inter- and intraobserver variabilities were significant across all measurements. This study highlights the potential of real-time MRI in the dynamic assessment of the scapholunate joint particularly at the medial landmark. Despite promising results, challenges such as measurement variability need to be addressed. Standardization and integration with advanced image processing methods could significantly enhance the accuracy and reliability of real-time MRI, paving the way for its clinical implementation in dynamic wrist imaging studies.

Quantitative correlation between temperature difference and sintering neck length in material extrusion-based additive manufacturing

The performance of polymeric parts fabricated via material extrusion-based additive manufacturing (MEAM) is significantly influenced by the sintering neck length between filaments. Despite its importance, elucidating the underlying mechanism governing the formation of inter-filament sintering necks is challenging due to the difficulties in monitoring temperature fields during MEAM. In this work, the thermal condition in MEAM was simplified using a printer with a tubular hotbed, coupled with a high temporal-spatial resolution infrared (IR) camera to monitor temperature field variations during printing. An advanced IR image processing method, integrating multi-directional retrieval and whole-loop average filtering, was developed to accurately measure temperature differences between filaments. By correlating these measurements with micro-CT characterizations, a quantitative relationship between temperature difference and sintering neck length was established. Through single- or multi-loop defect experiments, microscopic defect detection and analysis were conducted, achieving the prediction of sintering neck length variations at a 10 μm level with an 85 % accuracy. This work is believed to provide potential value for further accurate prediction of the mechanical properties of MEAM parts.

A Deep Learning-Based Model for the Detection and Tracking of Animals for Safety and Management of Financial Loss

The paper presents a deep learning-based model for detecting and tracking animals to protect agricultural fields. It addresses the challenge of crop destruction by stray animals, a significant issue for farmers, by proposing an intelligent camera-based solution. This solution utilizes IoT for real-time information transfer and employs advanced image and video processing methods, including YoloV5, for efficient animal detection and classification. The study evaluates various existing solutions and highlights the advantages of the proposed model in terms of accuracy and cost-effectiveness, offering a promising approach to mitigate animal raids on crops.

Advancements in Agricultural Technology: A Comprehensive Review of Machine Learning and Deep Learning Approaches for Crop Management and Disease Detection

Traditional agricultural practices often lack personalized guidance for farmers, which leads to poor crop choices, wasteful fertiliser use, and inadequate disease control. As a result, sustainability and productivity decline. In order to address these issues, this review examines the developments in agricultural technology, with a focus on the fusion of deep learning and machine learning techniques. We look at techniques like ensemble modelling, which optimises crop selection and fertiliser consumption depending on soil properties, and advanced image processing methods, which use leaf images to diagnose plant diseases accurately. Our goal in conducting this study is to provide light on how agricultural technology is developing and how it affects contemporary farming methods. We emphasise the major trends, approaches, and developments in the field of agricultural technology by examining a variety of research papers covering many elements of the subject. Our thorough analysis highlights the potential of deep learning and machine learning techniques to transform agricultural disease diagnosis, fertiliser selection, and crop management. With this investigation, we hope to add to the current conversation about using technology to solve the urgent problems facing the agriculture industry and, in the process, open the door for more effective and sustainable farming methods in the future

ADEROS: Artificial Intelligence-Based Detection System of Critical Events for Road Security

The deployment of artificial intelligence (AI) in Intelligent Transportation Systems (ITS), especially in the field of Intelligent Transportation Cyber-Physical Systems (ITCPS) has a strong potential to achieve higher efficiency, reliability, and increased safety in both transportation and traffic. This work focuses on the real-world implementation of ITCPS, in which structure and elements in combination with advanced image processing methods increase safety and fluidity of road traffic at crossroads and railway crossings. In this work, we present a novel system called Artificial Intelligence-based Detection System for Road Security (ADEROS), which combines elements of CPS systems, object detection, and classification, computer vision (CV) which analyzes vehicle trajectory tracking, vehicle and pedestrian presence, light signaling systems, railway barriers at railway crossings, and railway warnings. The presented system is based on a camera module that is suitably positioned to capture the entire scene. The module uses graphics processing units (GPU) for accelerated image processing techniques and the YOLOv4 deep neural network model to detect traffic participants and then dangerous situations in various crossroads and railway crossings. Our improved unique detector can distinguish between individual types of road users and the status of several safety devices at crossroads and railway crossings (for example, the state of traffic lights (TL) or rail barriers). Furthermore, we present experimental implementation details of the ADEROS system, which includes a central server web interface for live traffic situation monitoring, various communication channels for the camera module, and a central server based on.NET core, Cassandra DB, and different security protocols. All data from risky situations are evaluated and transferred to the central server securely without human intervention. The central server aggregates and archives all risky situational data from connected cameras. Finally, we present our experimental results from a real-world pilot project that consists of a camera module prototype deployed in a real crossroad and an operational central web server.

Cryo-Electron Microscopy for Cancer

A review paper on cryo-electron microscopy (cryo-EM) is essential to assess the recent advancements in this revolutionary imaging technique. As cryo-EM continues to revolutionize structural biology, a comprehensive review can consolidate the knowledge, highlight technical challenges, and offer insights into future developments, promoting better understanding and wider adoption of this cutting-edge technology. The absence of a review paper on the recent innovative approach of applying cryo-electron microscopy for cancer research hinders knowledge dissemination and impedes potential breakthroughs. A comprehensive review would bridge the gap, elucidating the successes and challenges of cryo-EM in cancer studies, fostering collaboration, and inspiring further investigations to combat cancer effectively.” Cryo-electron microscopy enables examining biomolecular structures at almost atomic precision while capturing multiple dynamic states more than 30 years after developing the industry's preferred method for cryo-embedding biological macromolecules under their native conditions. Techniques and equipment have significantly improved. Advanced image-processing methods are employed in research to facilitate the study of biological macromolecular structures and analyze their dynamics; for this, cryo-EM is a potent tool. Cryo-EM must effectively investigate the cellular macromolecular structure, including dynamic analysis using cutting-edge image-processing methods. Modern Analysis of individual particles using electron tomography is even more easily applicable to the procedure. With the development of single particle analysis and electron tomography, it is now more broadly applicable. These techniques have increased the method's applicability even further. Due to its ease of use and capacity to produce intricate and sophisticated data that can be used to comprehend biological structures better, cryo-EM is currently a more well-liked and available research tool. As a result, it serves as a useful tool for both academic and industrial research. Protein complexes, molecular pathways, and viral structures have also been studied using cryo-EM. Due to its adaptability, it has become a useful tool for illness and drug development. Its low cost and simplicity of usage have also made it a crucial research tool.

Remote Estimation of Peripheral Oxygen Saturation and Pulse Rate From Facial Analysis Using a Smartphone Camera.

Vital sign monitoring is an invaluable tool for healthcare professionals, both in the hospital and at home. Traditional measurement devices provide accurate readings but require physical contact with the patient which often is unsuitable, furthermore contact-based devices have been reported to fail by loosing contact due to movement as severe events occur, therefore, a contactless method is necessary.We hypothesize that, in ideal scenarios, it is possible to estimate both SpO2 and pulse rate using only facial video recorded with a smartphone's front-facing camera. To test this hypothesis, a dataset of 10 healthy subjects performing various breathing patterns while being recorded with a smartphone camera was collected during ideal lighting conditions.Using advanced image and signal processing methods to acquire remote photoplethysmography (rPPG) estimates from a patient's forehead, our proposed method can achieve SpO2 estimation results with Arms = 1.34% (accuracy RMS) and MAE ± STD = 1.26 ± 0.68% (mean average error) across a SpO2 range of 92% to 99% (percentage point SpO2) and pulse rate estimation results with Arms = 3.91 bpm (beats per minute) and MAE ± STD = 3.24±2.11 bpm across a pulse rate range of 60 bpm to 90 bpm. We conclude from these results, that remote vital sign estimation using facial videos recorded entirely with a smartphone camera is possible.

Image processing-based classification of pavement fatigue severity using extremely randomized trees, deep neural network, and convolutional neural network

ABSTRACT Fatigue failure is a major structural defect found in the asphalt pavement subjected to repeated traffic loadings. In order to establish cost-effective maintenance plans, timely detection of pavement fatigue and classification of its severity are crucial. This study aims at developing an advanced image processing method based on Gaussian steerable filters, projection integrals, and texture descriptors for automating the tasks of interest. The extremely randomized trees (ERT) and deep neural network (DNN) are used to analyze the features extracted from the aforementioned image processing methods. The performance of ERT and DNN is also benchmarked against that of the convolutional neural network. A dataset consisting of 6000 samples has been collected in Da Nang city (Vietnam) to construct and verify the proposed computer vision approaches. Experimental results supported by Wilcoxon signed-rank tests confirm that the ERT-based method has achieved the most desired classification performance with an accuracy rate > 0.93.

Imaging based pore network modeling of acoustical materials

The acoustical behavior of porous materials is dictated by their underlying pore network geometry. Given the complexity of accurately characterizing the various pore network features, current acoustical models instead rely on indirectly incorporating these features by accounting for them within acoustical transport properties, such as tortuosity, viscous and thermal characteristic lengths, and flow resistivity. In turn, these transport properties are currently identified using inverse characterization techniques or using multiphysics modeling techniques. Here, we propose the use of advanced image processing methods to characterize the pore network of acoustical materials and allow the direct calculation of their transport and acoustical properties. To establish the feasibility of this idea, we create 3D printable CAD models of porous materials with controlled pore geometries and use a Matlab-based watershed segmentation technique to calculate their effective pore and throat size distributions. These distributions are then used to calculate their transport properties and predict their sound absorption coefficients using the Johnson–Champoux–Allard model. For comparison, we calculate the transport properties using the hybrid multiphysics modeling technique and the inverse characterization method. The predictions from the three different methods are then compared with experimental measurements obtained by printing and testing the models using an impedance tube.

Pore-scale investigation on microemulsion-based quasi-miscible flooding for EOR in water-wet/oil-wet reservoirs: A 3D study by X-ray microtomography

Microemulsion flooding has recently gained attention as an effective chemical-based method for enhanced oil recovery (EOR). The high performance of this technique is attributed to the classical mechanism: plugging effect and divergence of preferential flow path. In this study, miscible behavior was identified as a novel mechanism by which oil recovery was controlled by dissolution process instead of capillary forces. To this end, a new type of solvent-based microemulsion phase was prepared by mixing solvent, water, and surfactant as a flooding fluid. A series of experiments with direct water flooding, microemulsion flooding, and water-alternate-emulsion flooding for EOR were carried out for strong water-wet and oil-wet artificial rock samples. X-ray microtomography was used to visualize the oil configurations in three-dimensional pore spaces. An advanced workflow image processing method was developed for the first time to capture the miscible behavior from a pore-scale view. As suggested in the results, solvent-based microemulsion flooding has a miscible displacement front where the oil phase is gradually solubilized. Therefore, capillary forces can be eliminated because the emulsion-oil interface is invisible but has a compositional gradient, leading to higher oil recovery. Compared with the immiscible waterflooding process, the capillary forces remained dominant, which hinders additional oil recovery. Based on EOR scenarios, we found that the new emulsion flooding could effectively solubilize larger well-connected oil clusters instead of small separate ganglia or singlets. Therefore, considering its miscible behavior, the solvent-based microemulsion flooding can be applied to secondary oil recovery regardless of the reservoirs’ wettability, or to tertiary oil recovery in weakly/strongly oil-wet reservoirs with a well-connected oil phase after waterflooding.

Atomic-Resolution Imaging of Small Organic Molecules on Graphene

Here, we demonstrate atomic-resolution scanning transmission electron microscopy (STEM) imaging of light elements in small organic molecules on graphene. We use low-dose, room-temperature, aberration-corrected STEM to image 2D monolayer and bilayer molecular crystals, followed by advanced image processing methods to create high-quality composite images from ∼102-104 individual molecules. In metalated porphyrin and phthalocyanine derivatives, these images contain an elementally sensitive contrast with up to 1.3 Å resolution─sufficient to distinguish individual carbon and nitrogen atoms. Importantly, our methods can be applied to molecules with low masses (∼0.6 kDa) and nanocrystalline domains containing just a few hundred molecules, making it possible to study systems for which large crystals cannot easily be grown. Our approach is enabled by low-background graphene substrates, which we show increase the molecules' critical dose by 2-7×. These results indicate a new route for low-dose, atomic-resolution electron microscopy imaging to solve the structures of small organic molecules.

Estimating maize seedling number with UAV RGB images and advanced image processing methods

Estimating maize seedling number with UAV RGB images and advanced image processing methods

Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.

Pancreatic cancer is one of the most aggressive cancers with approximate 10% five-year survival rate. To reduce mortality rate, accurate detection and diagnose of suspicious pancreatic tumors at an early stage plays an important role. To develop and test a new radiomics-based computer-aided diagnosis (CAD) scheme of computed tomography (CT) images to detect and classify suspicious pancreatic tumors. A retrospective dataset consisting of 77 patients who had suspicious pancreatic tumors detected on CT images was assembled in which 33 tumors are malignant. A CAD scheme was developed using the following 5 steps namely, (1) apply an image pre-processing algorithm to filter and reduce image noise, (2) use a deep learning model to detect and segment pancreas region, (3) apply a modified region growing algorithm to segment tumor region, (4) compute and select optimal radiomics features, and (5) train and test a support vector machine (SVM) model to classify the detected pancreatic tumor using a leave-one-case-out cross-validation method. By using the area under receiver operating characteristic (ROC) curve (AUC) as an evaluation index, SVM model yields AUC = 0.750 with 95% confidence interval [0.624, 0.885] to classify pancreatic tumors. Study results indicate that radiomics features computed from CT images contain useful information associated with risk of tumor malignancy. This study also built a foundation to support further effort to develop and optimize CAD schemes with more advanced image processing and machine learning methods to more accurately and robustly detect and classify pancreatic tumors in future.

A Skin Cancer Detection Interactive Application Based on CNN and NLP

Skin cancer is the most common cancer with several different types. According to current estimations, one in five Americans will develop skin cancer in their lifetime. Therefore, early diagnosis and treatment of it is of crucial significance. Several advanced image processing methods have been applied to predict skin cancer. However, few researchers utilize those methods to build an interactive application. In this work, we implemented an interactive skin cancer diagnosis website, combining the convolutional neural network (CNN) and natural language processing (NLP) technology. The neural network model uses four convolutional layers and dense layers respectively to improve the accuracy. Two max-pooling layers were used to reduce redundant information. To address the severe overfitting problem, we chose to utilize the batch normalization along with dropout layers. Based on our results, 0.9935 in accuracy and 0.0225 loss is realized for training data, and accuracy of 0.8393 and 0.6648 loss for testing data. Natural language processing (NLP) was used to implement a chatbot for interaction with users. We crawled skin cancer related questions and answers from Quora and used them to train our chatbot. Lastly, we combined CNN and NLP to build an interactive skin cancer diagnosis website. VUE.js and Django were used to build the front-end and back-end of our website. These results offer a guideline for combining artificial intelligence with not only medicine but also interactive network, which enables people to get medical care more easily.

Detection of river flow slow-down through sensing system and quasi-real time imaging

Flow slow-down in rivers and artificial canals is a basic aspect to be monitored and kept strictly under control. Flow slow-downs can become a major concern in the event of extreme phenomena. The paper illustrates an advanced image processing method that uses particle tracking velocimetry in conjunction with a monadic approach to better characterize water flow in the presence of waste or debris that block normal water flow within a river. An high-speed camera installed beneath a bridge takes periodic images of the water flow. The measured water level and the images taken by the camera are sent to a central system in real-time. Results demonstrate the capability of the proposed method to accurately detect the presence of debris from the measured water flow.

Urban neighbourhood environment assessment based on street view image processing: A review of research trends

The urban neighbourhood is one of the most important places for public activities and behaviour spaces in cities, and the quantification of their environments is receiving increasing attention from researchers. In the era of big data, numerous urban data sources, represented by street view images, are documenting the evolution of people's lifestyles in various ways. With the rapid development of image processing technology, street view images have become an emerging data source for urban research. Street view image processing can be used to obtain spatial elements of large scale urban neighborhoods, thus enabling rapid urban neighbourhood evaluation. However, no systematic literature review has been conducted so far on the research of street view images application in urban neighbourhood environment. This paper systematically reviews the research trends of existing publications on the use of street view images for the quantitative analysis of urban neighbourhood environments. The number of publications began to grow rapidly in 2010. From 2010 to 2020, the number of publications increased from 6 to 341, with an annual growth rate of approximately 30.4%. Recent studies have focused on five areas: thermal environment, neighbourhood morphology, environmental perception, socio-economic factors, landscape design and environmental evaluation. The publications use experiment and simulation as the main research methods. Deep learning is the mainstream and advanced image processing method, and the data analysis models include numerical analysis and spatial analysis. Finally, the overall research framework and future research trends of street view images in the current quantitative research of urban streets are obtained.

Automatic Detection of Genetics and Genomics of Eye Disease Using Deep Assimilation Learning Algorithm.

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods, there are issues such as lack of noise removal, improper clustering segmentation and less classification accuracy. This can be accomplished by automatic diagnosis of DR using advanced image processing method. The cotton wool spot (CWS), hard exudates (HE) contains a common manifestation of many diseases in retina including DR and acquired immunodeficiency syndrome. In the present work, super iterative clustering algorithm (SICA) is proposed to identify the CWS, HE on retinal image. Feature-based medical image retrieval (FBMIR) datasets are utilized for this purpose. Noises present on the images and histogram-filtering technique is used to convert red, green, and blue (RGB) images into a perfect greyscale image without noise. After pre-processing, SICA is used to identify the CWS, HE detection on retinal images and eliminates unnecessary areas of interest. In the third stage, after detecting CWS and HE, various statistical features are extracted for further classification using deep assimilation learning algorithm (DALA). The performance of DALA technique is examined with various classification parameters like recall, precision, and F-measure. Finally, the false classification ratios are computed to compare the performance of the trained networks. The proposed method produces accurate detection of affected regions with an accuracy ratio of 98.5% and it is higher than the other conventional methods. This method may improve the accuracy of automatic detection and classification of eye diseases.