Global sensor feedback for automatic nanohandling inside a scanning electron microscope

Broad ion beam - scanning electron microscopy (BIB-SEM) is an important technology for quantitative description of pore space and detailed investigation of pore structure in mudstones and shales. To obtain quantitative data from BIB-SEM images, image processing is required. However, the effect of different image processing approaches on the segmentation results has not been well documented yet. Particularly the interpretation of pore boundaries approaching the maximum image resolution can represent a major challenge. This study addresses the variability in image processing by comparing conventional thresholding with advanced machine learning methods and analysing the sensitivity of results to different segmentation. The test dataset consists of large area BIB-SEM maps of 12 Middle Miocene mudstone samples from the Vienna Basin, Austria. Segmentation of pore space and organic matter was performed using the thresholding method in ImageJ and the machine learning-based pixel classification in ilastik. The results show that the selected greyscale thresholding algorithm in ImageJ significantly affected the resolved porosity (1.7% to 2.8% variation) relative to the average total porosity of 4.5%. Compared to ImageJ, ilastik provides comparable results with better efficiency in separating pores from organic matter. However, the obtained pore information is inevitably influenced by variations in boundary delineation and feature identification due to inherent differences in the image processing methods and varying representativeness of image maps themselves. Overall, the study findings highlight the importance of carefully selecting image processing methods and considering their potential impact on the accuracy and reliability of pore characterization in mudstones and shales.

Image processing is a method for enhancing unprocessed images from cameras on aircraft, spacecraft, and satellites as well as images taken regularly for a variety of uses. In general, the following strategies can be used to categorize all image processing operations: Images are represented in several ways, which are referred to as image representation, image preprocessing, image enhancement, image restoration, image analysis, picture reconstruction, and image data compression. The first radiometric normalization, geometric distortion correction, and noise removal of the raw image data been discussed in the past. The goal of the information extraction procedures is to automate the identification of tone in a scene by replacing the visual examination of image data with quantitative techniques. This entails analyzing multispectral image data and establishing the earth cover identification of each pixel in an image using statistically based decision procedures. The goal of the classification procedure is to sort all of the pixels in a digital image into one of several different earth cover classes or themes. The purpose of this study is to examine various image processing approaches and algorithms, many sorts of image processing algorithms: Optical Character Recognition (OCR) and Supporting Vector Machine (SVM) a feature extraction technique, on the vehicle classification dataset and had accurate results of 90% for SVM and 95% for OCR, to further improve the performance of machine algorithms in terms of accuracy for image processing technique using a vehicle. This study can be used for vehicle classification research, it also advances and improves the performance of the system in terms of accurate detection.

Chapter 2 - New Image Processing Methods for Advanced Metallization in Micro- and Nano-Electronics

This study aimed to evaluate the impact of UVC (Ultraviolet C Radiation), detergent foam, and alcohol (70%) sterilization methods on the surface morphology of acrylonitrile-butadiene-styrene (ABS) specimens using a novel SEM (Scanning Electron Microscope) image processing approach. Twelve 3D-printed specimens were prepared, and five concentric circular regions of interest (ROIs) per specimen were analyzed. Three quantitative descriptors-defect area fraction, anisotropy ratio, and RMS (Root Mean Square) roughness-were extracted to assess surface alterations. To validate the image-based findings, EDX (Energy-Dispersive X-ray Spectroscopy) elemental analysis for carbon (C), nitrogen (N), and oxygen (O) was employed as a complementary and traditional benchmark technique. Statistical comparisons and p-value heat maps revealed strong convergence between SEM and EDX results. UVC sterilization consistently preserved surface morphology and elemental stability, showing the lowest defect fraction (p = 0.2684), balanced anisotropy (p = 0.02481), and minimal oxygen incorporation (O = 7.6). Foam sterilization produced intermediate effects, with significant anisotropy changes (p = 0.007456) and reduced nitrogen (19.6). Alcohol sterilization induced the most severe damage, characterized by high defect density, increased roughness, and elemental imbalance (N = 17.3, O = 13.9), confirming oxidative degradation. The convergence of SEM and EDX outcomes demonstrates that SEM image processing is a reliable novel method validated by traditional elemental analysis. Together, these approaches provide a robust framework for ranking sterilization efficacy, with UVC identified as the most favorable method, detergent foam as an acceptable alternative, and alcohol as the least effective due to its destabilizing effects.

Frequent and accurate estimation of forest structure parameters, such as number of trees per hectare or total height, are mandatory for sustainable forest management. Unmanned aircraft system (UAS) equipped with inexpensive sensors can be used to monitor and measure forest structure. The detailed information provided by the UAS allows tree level forest inventory. However, tree identification depends on a variety of parameters defining the image processing and tree segmentation algorithms. The objective of our study was to identify parameter combinations that accurately delineated trees and their heights. We evaluated the impact of different tree segmentation and point cloud generation algorithms on forest inventory from imagery collected with a UAS over a mature Douglas-fir plantation forest. We processed the images with two commonly used commercial software packages, Agisoft PhotoScan and Pix4Dmapper, both implementing image processing algorithms called Structure from Motion. For each software we generated photogrammetric point clouds by varying the parameters defining the implementation. We segmented individual trees and heights using three tree algorithms: Variable Window Filter, Graph-Theoretical, and Watershed Segmentation. We assessed the impact of image processing algorithms on forest inventory by comparing the estimated trees with trees manually identified from the point clouds. We found that the type of tree segmentation and image processing algorithms have a significant effect in accurately identifying trees. For tree height estimation, we found strong evidence that image processing algorithms had significant effects, whereas tree segmentation algorithms did not significantly affect tree height estimation.These findings may be of interest to others that are using high-resolution spatial imagery to estimate forest inventory parameters.

A comparative study between two types of tool wear monitoring systems for milling processes is introduced in this paper. The suggested sensory fusion approach includes the implementation of an infrared camera, in addition to force, vibration, sound and acoustic emission sensors. The majority of the research work available in literature and industry focuses on using one dimensional signals, such as force, vibration. Two dimensional data, such as infrared and visual images, are limited in literature in relation to machining operations. This work compares between one dimensional and two dimensional data for the development of a tool condition monitoring system for milling processes. The paper presents a comparative study between the performance of signal and image processing algorithms using neural networks. Fourier Transformation and Wavelets analysis are used to process one dimensional and Two dimensional data respectively. The results indicate that two dimensional data obtained from infrared images has significant capability in comparison to one dimensional data for the detection of tool wear for the selected image and signal processing algorithms.

Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading

Applications of Noncausal Gauss–Markov Random Field Models in Image and Video Processing

Image processing is one of the rapidly growing technologies with operations that can be applied to image data which may be in the form of a 2D, 3D or 4D signals. It has created tremendous opportunities for mathematical modeling, analysis, and computation. A novel approach in image processing has been developed, known as PDE (Partial Differential Equations) based image processing. The PDE approach is very promising for solving many problems in image processing because it provides new and more intuitive mathematical models, gives better approximations to the Euclidean geometry of the problem, and is supported by efficient discrete numerical algorithms based on difference approximations. In this paper, we have discussed how Euler Lagrange Equation plays a significant role in solving PDE based image processing problems. The typical image processing problems such as denoising and inpainting can be solved using EL equation. The ROF and Tikhnov models of denoising and Total Variation based inpainting has been implemented and a comparative study is done with the conventional methods. We have considered various well-known objective quality evaluation metrics used in image processing in this comparison. The results show that PDE based image processing techniques outperforms the conventional approaches.

Reservoir microfacies analysis exploiting microscopic image processing and classification algorithms applied to carbonate and sandstone reservoirs

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.

Chapter 46 - Medical Image Processing Using GPU-Accelerated ITK Image Filters

Application of artificial intelligence algorithms in image processing

Digital radiography systems are capable of capturing a wide range of x‐ray exposures in a single image. The wide latitude of these capture devices dictates that images are digitally processed before display. Attempting to directly print or display the full dynamic range of the captured image will result in an image with low contrast that is unacceptable for diagnostic interpretation. Image processing algorithms are used to identify the range of exposures (represented as code values) that correspond to the diagnostically relevant regions, followed by a rendering process to transform signal values in the diagnostically relevant regions to display values. The image processing algorithms need to perform these steps completely automatically, or with a minimum amount of human intervention, to facilitate technologists' workflow. Image processing performance is ultimately gauged by the ability to automatically and consistently generate images of diagnostic quality. Recognition of the diagnostically relevant exposures consists of segmentation of the collimation, direct exposure, and anatomy regions. This is a challenging computer‐recognition problem because several factors cause the characteristics of the image, and corresponding code value histogram, to vary. These factors include the body part and projection, exposure technique, collimation, use of anti‐scatter devices, use of contrast media, etc. Robust segmentation will also automatically recognize other features such as prosthetic implants, pacemakers, and left‐right markers. Once the image is properly segmented, the signal values for the diagnostically relevant regions are transformed into values for display. Rendering the region of interest for display generally consists of establishing the image grayscale rendition, and signal equalization and contrast enhancement, which is based on spatial frequency decomposition and reconstruction. Because of the tremendous flexibility to render images to different aims, understanding the rendering preferences of radiologists is a critical component of digital radiography image processing. Establishing the aim appearance for different exam types, then adapting the image processing algorithms and database parameters to automatically and consistently deliver the preferred rendering, will affect both the technologists' and the radiologists' satisfaction with the capture device. This course will review fundamentals of image processing for digital radiography. Specific course content will include a discussion of radiographic image segmentation, correlation of image content with different regions of the histogram, and a review of image‐rendering functions, including the tone scale, spatial frequency methods to enhance contrast, and signal equalization. The course will also include a review of the role of image processing in the end‐to‐end image chain (capture through interpretation), with considerations for image quality performance of different capture and display devices, the effect of the human visual system and ambient viewing conditions, and grayscale calibration.

Grape dehydration is practiced widely in the food industry with large yields of sultanas produced globally. This paper proposes an investigation into the microstructure changes of grapes as they are dried by imaging specimens at intervals during dehydration at two temperatures using scanning electron microscopy. Two main methods were developed to obtain the complex boundaries of cells present in grape tissue in over 36 SEM images. Segmentation of the binary image using an adapted watershed function obtained the most consistent and accurate morphological shape. This was compared to a secondary method which used Canny’s edge detection function, morphological closing and skeletonizing to outline the cellular microstructure. MATLAB was utilised to convert these boundaries into measurable areas so that quantitative data on average cell area, perimeter and cell axis lengths were acquired. It was found that over the drying time, the cell area and perimeter were reduced as expected. Some variability in the data was clear due to only single samples being analysed for each temperature and time combination. Trends in cell perimeter, diameter and shape will be used to demonstrate relationships between morphological structure, drying temperature, and duration. Detailed images of the microstructure were obtained, and a unique image processing algorithm was developed to quantitatively analyse the properties of this microstructure. The development of automatic image processing techniques and algorithms will enable quantitative data to be extracted from any image and extend to any plant/food material.