Image Processing Research Articles

Repeatability of instantaneous position in rotational motion of a submerged Savonius turbine driven by water surface waves using image processing: an experimental investigation

Repeatability of instantaneous position in rotational motion of a submerged Savonius turbine driven by water surface waves using image processing: an experimental investigation

Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence

AbstractThe rapid advancement of technology has brought significant changes to various fields, including medical imaging (MI). This discussion paper explores the integration of computing technologies (e.g. Python and MATLAB), digital image processing (e.g. image enhancement, segmentation and three‐dimensional reconstruction) and artificial intelligence (AI) into the undergraduate MI curriculum. By examining current educational practices, gaps and limitations that hinder the development of future‐ready MI professionals are identified. A comprehensive curriculum framework is proposed, incorporating essential computational skills, advanced image processing techniques and state‐of‐the‐art AI tools, such as large language models like ChatGPT. The proposed curriculum framework aims to improve the quality of MI education significantly and better equip students for future professional practice and challenges while enhancing diagnostic accuracy, improving workflow efficiency and preparing students for the evolving demands of the MI field.

LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

Three-dimensional evaluation of maxillofacial symmetry improvement following orthognathic surgery in patients with asymmetrical jaw deformities.

The aim of this study was to clarify the three-dimensional changes in maxillofacial morphology following orthognathic surgery using 3D-CT in patients with asymmetrical jaw deformities. The subjects were 30 patients with asymmetrical jaw deformities. Three-dimensional images taken preoperatively and 6months postoperatively were re-aligned using image processing software with the horizontal plane, coronal plane, and midsagittal plane as reference axes. Thirty-nine measurements including 28 distances, nine angles, and two volumes were recorded, and differences between preoperative and postoperative measurements and percentage differences between paired measurements on the deviated and unevolved sides were calculated. Translational and rotational movements of the maxillary dentition and mandibular body and the mandibular ramus internally rotating contributed to improved symmetry of the maxillofacial morphology, but the degree and proportion of these changes varied from case to case and mild asymmetry remained even after surgery. 3D-CT analysis of maxillofacial morphology is essential to accurately evaluate the asymmetry of hard and soft tissue morphology in the maxillofacial region and the degree of improvement after orthognathic surgery, and the tooth movement during preoperative orthodontic treatment should be determined taking into account the movement of the upper and lower jaws during orthognathic surgery.

Comprehensive Morphometric Analysis to Identify Key Neuroimaging Biomarkers for the Diagnosis of Adult Hydrocephalus Using Artificial Intelligence

BACKGROUND AND OBJECTIVES: Hydrocephalus involves abnormal cerebrospinal fluid accumulation in brain ventricles. Early and accurate diagnosis is crucial for timely intervention and preventing progressive neurological deterioration. The aim of this study was to identify key neuroimaging biomarkers for the diagnosis of hydrocephalus using artificial intelligence to develop practical and accurate diagnostic tools for neurosurgeons. METHODS: Fifteen 1-dimensional (1-D) neuroimaging parameters and ventricular volume of adult patients with non-normal pressure hydrocephalus and healthy subjects were measured using manual image processing, and 10 morphometric indices were also calculated. The data set was analyzed using 8 machine, ensemble, and deep learning classifiers to predict hydrocephalus. SHapley Additive exPlanations (SHAP) feature importance analysis identified key neuroimaging diagnostic biomarkers. RESULTS: Gradient Boosting achieved the highest performance, with an accuracy of 0.94 and an area under the curve of 0.97. SHAP analysis identified ventricular volume as the most important parameter. Given the challenges of measuring volume for clinicians, we identified key 1-D morphometric biomarkers that are easily measurable yet provide similar classifier performance. The results showed that the frontal-temporal horn ratio, modified Evan index, modified cella media index, sagittal maximum lateral ventricle height, and coronal posterior callosal angle are key 1-D diagnostic biomarkers. Notably, higher modified Evan index, modified cella media index, and sagittal maximum lateral ventricle height, and lower frontal-temporal horn ratio and coronal posterior callosal angle values were associated with hydrocephalus prediction. The results also elucidated the relationships between these key 1-D morphometric parameters and ventricular volume, providing potential diagnostic insights. CONCLUSION: This study highlights the importance of a multifaceted diagnostic approach incorporating 5 easily measurable 1-D neuroimaging biomarkers for neurosurgeons to differentiate non-normal pressure hydrocephalus from healthy subjects. Incorporating our artificial intelligence model, interpreted through SHAP analysis, into routine clinical workflows may transform the diagnostic landscape for hydrocephalus by standardizing diagnosis and overcoming the limitations of visual evaluations, particularly in early stages and challenging cases.

Elliptic Quaternion Matrices: A MATLAB Toolbox and Applications for Image Processing

In this study, we developed a MATLAB 2024a toolbox that performs advanced algebraic calculations in the algebra of elliptic numbers and elliptic quaternions. Additionally, we introduce color image processing methods, such as principal component analysis, image compression, image restoration, and watermarking, based on singular-value decomposition theory for elliptic quaternion matrices; we added these to the newly developed toolbox. The experimental results demonstrate that elliptic quaternionic methods yield better image analysis and processing performance compared to other hypercomplex number-based methods.

Advanced Applications of Polymer Hydrogels in Electronics and Signal Processing

The current state of affairs in the field of using polymer hydrogels for the creation of innovative systems for signal and image processing, of which computing is a special case, is analyzed. Both of these specific examples of systems capable of forming an alternative to the existing semiconductor-based computing technology, but assuming preservation of the used algorithmic basis, and non-trivial signal converters, the nature of which requires transition to fundamentally different algorithms of data processing, are considered. It is shown that the variability of currently developed information processing systems based on the use of polymers, including polymer hydrogels, leads to the need to search for complementary algorithms. Moreover, the well-known thesis that modern polymer science allows for the realization of functional materials with predetermined properties, at the present stage, receives a new sounding: it is acceptable to raise the question of creating systems built on a quasi-biological basis and realizing predetermined algorithms of information or image processing. Specific examples that meet this thesis are considered, in particular, promising information protection systems for UAV groups, as well as systems based on the coupling of neural networks with holograms that solve various applied problems. These and other case studies demonstrate the importance of interdisciplinary cooperation for solving problems arising from the need for further modernization of signal processing systems.

Development of methodology for calculating flooded area and flood volume in small urban areas based on unmanned aerial vehicle images

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests’ detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures.

Defect-engineered and even amorphous two-dimensional (2D) materials have recently gained interest due to properties that differ from their pristine counterparts. Since these properties are highly sensitive to the exact atomic structure, it is crucial to be able to characterize them at atomic resolution over large areas. This is only possible when the imaging process is automated to reduce the time spent on manual imaging, which at the same time reduces the observer bias in selecting the imaged areas. Since the necessary datasets include at least hundreds if not thousands of images, the analysis process similarly needs to be automated. Here, we introduce disorder into graphene and monolayer hexagonal boron nitride (hBN) using low-energy argon ion irradiation, and characterize the resulting disordered structures using automated scanning transmission electron microscopy annular dark field imaging combined with convolutional neural network-based analysis techniques. We show that disorder manifests in these materials in a markedly different way, where graphene accommodates vacancy-type defects by transforming hexagonal carbon rings into other polygonal shapes, whereas in hBN the disorder is observed simply as vacant lattice sites with very little rearrangement of the remaining atoms. Correspondingly, in the case of graphene, the highest introduced disorder leads to an amorphous membrane, whereas in hBN, the highly defective lattice contains a large number of vacancies and small pores with no indication of amorphisation. Overall, our study demonstrates that combining automated imaging and image analysis is a powerful way to characterize the structure of disordered and amorphous 2D materials, while also illustrating some of the remaining shortcomings with this methodology.

“AgriVision” A Flutter -Based Application for RealTime Detection of Pesticides Residues on Fruits

“AgriVision” is a Flutter-based mobile application designed to address the critical issue of pesticide contamination on fruits. The application utilizes real-time image processing and machine learning techniques to detect pesticide residues on the surface of various fruits, ensuring food safety and healthstandards. By capturing and analyzing images through a smartphone camera, the app provides instant and reliablefeedback on the presence of harmful chemicals. The user-friendly interface, combined with the portability of the solution, offers farmers, consumers, and vendors a practical tool to assess fruit quality and promote safer agricultural practices. This project aims to contribute to healthier food choices and enhance consumer safety. Key Words: AgriVision , Flutter application , Real-time detection , Pesticide residues , Fruits , Agricultural technology , Pesticide detection , Mobile app , Food safety , AgriTech , Image processing , Machine learning , Smart agriculture , Fruit quality analysis.

Edge Computing for Smart-City Human Habitat: A Pandemic-Resilient, AI-Powered Framework

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies.

Bionic ankle-assisted rehabilitation training system based on biomechanical evaluation

In modern society, people’s life rhythm is getting faster and faster. Ankle injury would significantly reduce the frequency of people’s activities, which has a great impact on people’s normal work and life. As a new medical method, the bionic ankle rehabilitation training system is used to assist rehabilitation doctors to help patients complete joint flexibility and recovery training. In the research method of ankle biomechanical characteristics, the detection of ankle joint patient’s motion is particularly important. The purpose of this paper is to study how to design a bionic ankle assisted rehabilitation training system based on image processing. Therefore, this paper proposes an image-based moving target detection method, which has the advantages of high reliability and simple operation, and can improve the recognition rate of the ankle joint movement. The experimental results of this paper showed that the system can realize the predetermined trajectory movement and run the system stably. In terms of patient following error, it was kept within 0.03cm. The following error of the ankle joint trajectory was up to 0.03cm and the lowest was 0.01cm, which was almost negligible. In terms of accuracy, the accuracy of the system was also very high, and it can respond and determine the patient’s actions quickly, thereby helping patients to better perform rehabilitation training.

CSF flow measurement in the mesencephalic aqueduct using 2D cine phase-contrast MRI in dogs with communicating internal hydrocephalus, ventriculomegaly, and physiologic ventricular spaces

BackgroundBrachycephalic dogs are overrepresented with ventricular enlargement. Cerebrospinal fluid (CSF) flow dynamics are not completely understood. MRI techniques have been used for the visualization of CSF dynamics including phase-contrast imaging.ObjectivesThis study aimed to determine a causality between CSF flow and ventriculomegaly or hydrocephalus and to compare CSF flow dynamics among dogs with ventriculomegaly, internal hydrocephalus, and physiologic ventricles.AnimalsA total of 51 client-owned dogs were included in the study.MethodsMagnetic resonance imaging (MRI)-based FLASH sequences and phase-contrast images of the brain were obtained, and the ROI was placed at the level of the mesencephalic aqueduct. ECG monitoring was performed parallel to MRI acquisition. Evaluation of flow diagrams and processing of phase-contrast images were performed using commercially available software (Argus VA80A, Siemens AG Healthcare Sector, Erlangen, Germany). Dogs were divided into three groups: Group 1 consisted of brachycephalic dogs with ventriculomegaly (group 1A) or internal hydrocephalus (group 1B), group 2 consisted of brachycephalic dogs with normal ventricles, and group 3 consisted of meso- to dolichocephalic dogs with normal ventricles.ResultsGroup 1 had a higher median Vrost (4.32 cm/s; CI: 2.94–6.33 cm/s) and Vcaud (−6.1 cm/s, CI: 3.99–9.33 cm/s) than group 2 (Vrost: 1.99 cm/s; CI 1.43–2.78 cm/s; Vcaud: 2.91 cm/s, CI: 2.01–4.21 cm/s; p = 0.008; p = 0.03) and group 3 (Vrost:1.85 cm/s, CI: 1.31–2.60 cm/s; Vcaud − 2.46 cm/s, CI 1.68–3.58 cm/s; p = 0.01; p = 0.02). The median Volcaud of group 1 (−0.23 mL/min, CI: 0.13–0.42 mL/min) was higher than that of group 2 (−0.09 mL/min, CI: 0.05 mL/min and 0.15 mL/min) (p = 0.03). Groups 1A and 1B did not differ in Vcaud, Vrost, Volcaud, and Volrost. Group 1A and 1B showed a higher median Vrost (4.01 cm/s, CI: 2.30–7.05 cm/s; 5.94 cm/s, CI: 2.16–7.88 cm/s) than group 2 (1.85 cm/s, CI: 1.24–2.80 cm/s.) (p = 0.03; p = 0.004).Conclusion and clinical importanceIncreased CSF flow velocities in rostral and caudal directions are present in dogs with ventriculomegaly and internal hydrocephalus compared to normal controls.

Internal adaptation assessment of implant infrastructures manufactured through five different techniques (heat-press, milling, lost wax, calcinable cylinder, and CAD/Waxx®): an in vitro pilot study

IntroductionThe aim of this study was to evaluate the fit performance of implant infrastructures manufactured by five different techniques: heat-press (IPS), milling (ZIR), lost wax (CER), calcinable cylinder (CAL), and CAD/Waxx® (CAD).MethodsThe methodology was based on the Replica Technique, which can simulate and evaluate the fit of the infrastructure on the implant component. Thus, each infrastructure was internally filled with low-viscosity silicone addition and seated on the component until its final setting, obtaining the replica of the cementation space. After removing the coping containing the silicone film, light-density silicone was inserted addition in place of the components, and in its surroundings, condensation silicone was applied, establishing support for the assembly. The joint was sectioned mesiodistally, photographed, and analyzed in image processing software in order to measure the thickness of the interface infrastructure/implant at five different areas: marginal opening (M), gingival-axial angle (G-A), axial region (A), axial-occlusal angle (A-O) and occlusal surface (O).ResultsThe lowest and the highest average thickness between groups was, respectively, IPS: 187.5 μm and CAD: 221.6 μm, with statistically significant differences (p < 0.01) among all five groups; the lowest and the highest average of all groups in each point was, respectively, A: 99.86 μm and O: 279.78 μm. The IPS group exhibited the lowest value of the internal space of the infrastructure on the implant. The marginal region of all groups demonstrated a correlation with the findings in the literature, except the CAL group; otherwise, the occlusal region and the angles A-O and G-A exhibited values beyond that expected.DiscussionIt was possible to conclude that the five infrastructure groups presented different adaptations, suggesting possible interference in the internal spaces due to the manufacturing infrastructure processes.

A Computer Vision‐Based Methodology to Estimate Fruit Colour Diversity in Ornamental Pepper (Capsicum spp.)

ABSTRACTFruit colour diversity within different ripening stages confers ornamental value for pepper plants. Using images can be helpful in analysing the fruit colour‐related genetic diversity and enable selecting accessions for ornamental purposes by avoiding subjectiveness. This study aimed to evaluate the phenotypic diversity among accessions of ornamental pepper using image processing techniques to identify fruit colours. Photos from 40 fruits from each of 12 accessions were captured by a camera from a smartphone. Separate channels and colour indices were extracted from the images to analyse the accessions using both dendrogram and principal components analysis. Dendrogram analysis allowed separating accessions with predominance of dark fruits from those whose fruits showed lighter and more vivid colours, with a cophenetic correlation coefficient of 0.811. The VARI and NGRDI vegetation indices were efficient in discriminating fruits with a predominance of green colour, and the BGI could be used to discriminate accessions with predominantly reddish‐brown fruits. The proposed method can be used in small‐scale breeding programs for accurately assessing the development of varieties regarding their fruit colour diversity.

Phenotypic and genetic characteristics of retinal vascular parameters and their association with diseases.

Fundus images allow for non-invasive assessment of the retinal vasculature whose features provide important information on health. Using a fully automated image processing pipeline, we extract 17 different morphological vascular phenotypes, including median vessels diameter, diameter variability, main temporal angles, vascular density, central retinal equivalents, the number of bifurcations, and tortuosity, from over 130,000 fundus images of close to 72,000 UK Biobank subjects. We perform genome-wide association studies of these phenotypes. From this, we estimate their heritabilities, ranging between 5 and 25%, and genetic cross-phenotype correlations, which mostly mirror the corresponding phenotypic correlations, but tend to be slightly larger. Projecting our genetic association signals onto genes and pathways reveals remarkably low overlap suggesting largely decoupled mechanisms modulating the different phenotypes. We find that diameter variability, especially for the veins, associates with diseases including heart attack, pulmonary embolism, and age of death. Mendelian Randomization analysis suggests a causal influence of blood pressure and body mass index on retinal vessel morphology, among other results. We validate key findings in two independent smaller cohorts. Our analyses provide evidence that large-scale analysis of image-derived vascular phenotypes has sufficient power for obtaining functional and causal insights into the processes modulating the retinal vasculature.

Image processing framework for in-process shaft diameter measurement on legacy manual machines

Image processing framework for in-process shaft diameter measurement on legacy manual machines

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Three-Dimensional Hullform Reconstruction from Two-Dimensional Drawings Based on Image Processing Techniques

Abstract Repair shipyards sometimes need three-dimensional geometry for repairs and retrofits. However, they often create models manually from two-dimensional drawings provided by ship owners. In this case, human error leads to inaccuracies, making the process time-consuming and laborious. Therefore, there is a need for research on efficient three-dimensional hullform reconstruction from two-dimensional drawings. This study proposed a method to automatically extract points from two-dimensional lines and visualize them in three dimensions. The proposed method consists of three steps. The first step is a point extraction through image processing, which uses a starting point search algorithm to access overlapping or intersection lines and extracts the points on the lines in the drawing by searching for paths between the starting point and the end point entered by the user. The second step is the transformation of the extracted data, which transforms the points based on pixel coordinate into 3D points through coordinate transformation and scaling by utilizing the stored line data and three-dimensional coordinate information. The last step is to visualize the transformed data as a real three-dimensional model with point visualization. This study demonstrates that the proposed method can be effectively utilized by detecting two-dimensional lines and reconstructing the hullform in three dimensions.