High-throughput image processing software for the study of nuclear architecture and gene expression

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.

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

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

High-resolution structures of membrane and soluble proteins can be obtained by cryo-electron crystallography, given highly-ordered cryo-preparations of perfectly flat 2D crystals are available. Studies of membrane proteins, which are embedded into a lipid membrane, mimicking the native cell membrane, are of particular biological interest. However there are multiple reasons why electron crystallography is far from being a mainstream protein structure determination technique. In this thesis we address three major difficulties of electron crystallography: (i) resolution loss due to not perfectly flat crystals, (ii) reliable high-throughput automatic image processing and (iii) correction of electron beam-induced motion of the sample.
\n
\nThe conventional electron crystallography image processing procedure assumes perfectly flat 2D crystals, which are almost impossible to obtain. Our new processing approach, described in Chapter 2, weakens this assumption tremendously. Traditional processing assigns the same tilt geometry to all proteins of one 2D crystal. Thus local tilt geometry variations, due to not perfectly flat crystals, are neglected. We developed an algorithm that optimizes the tilt geometry of each protein separately, while exploiting the correlation between neighboring proteins. The new method proves the feasibility of this approach, improves the achieved resolution and opens the doors to new studies, i.e. structural studies of membrane proteins embedded into lipid vesicles.
\n
\nRecently a new generation of digital detectors tremendously changed the cryo-electron microscopy field. Beside a significantly increased signal-to-noise ratio these detectors record dose-fractionated movies of the sample under the electron beam. Previous cameras only recorded one image instead. This new exposure mode enables the computational correction for beam-induced sample movements. In Chapter 3 we describe a new “real-time” automation pipeline for electron crystallography using direct electron detectors. The novel pipeline automatically corrects for homogeneous sample drift on frame level and processes the acquired images automatically. Both, the time-to-solution and the quality of the obtained 3D reconstructions are significantly improved.
\n
\nHeterogeneous beam-induced sample movements are the most severe resolution- limiting factor in modern cryo-electron crystallography. In Chapter 4 we present an algorithm, termed movie-mode unbending, which corrects for inhomogeneous beam- induced sample drift. In contrast to the previous homogeneous drift-correction, the novel algorithm can correct for locally varying beam-induced sample motion. This novel approach significantly increases the resolution of electron cryo-crystallographic studies recorded on the latest detectors.
\n
\nChapter 5 covers multiple successful applications of the methods introduced above. For instance the real-time drift-correction module of the developed automation pipeline was used for multiple near-atomic resolution single particle and helical image processing projects. The high-throughput crystal image processing was applied to different kinds of 2D crystals and enabled the qualitative assessment of different sample preparation methods. Additionally we describe the implementation of a high-throughput single particle automation pipeline, which will enable the generation of near-atomic resolution single particle cryo-electron microscopy density maps on a daily basis.

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.

In portable precision visual measurement, processing result of the measurement image has an important influence on final measurement accuracy in large field working space. However, the images taken in large field working space always have the following characteristics: containing a large amount of information, having a high redundancy and including a lot of background noises. In order to solve these problems, precision image processing and recognition algorithm is researched. Firstly, portable precision visual measurement method is introduced. Then, the design method of global control points including noncoding points and coding points is displayed. Finally, the image processing and recognition algorithm is investigated in detail, including the largest variance threshold method used to partly binary the image, the detection algorithm of global control points based on the constraint of distance and acreage and coding value recognition method through correcting the image feature. A large amount of experiments show that the image processing and recognition algorithm is accurate and effective to processing the image taken in the working space.

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

Traditional imaging methods often separate the two technical procedures of optical system design and imaging processing, where the lens subsystem is firstly optimized using an optical measure of performance and the image processing subsystem is optimized subsequently, resulting in the failures in efficient cooperation between the optical and digital elements and missing of the optimal solution. Therefore, an innovative approach is presented to combine optical system design and image processing together to obtain high resolution images from the final system. In this paper, the theoretical foundations for this computing optical design method is analyzed by considering the modulation transfer functions (MTF) and aberrations. According to the wave aberration theory and Zernicke formula, the relationship between the point spread function (PSF) and a certain monochromatic aberration can be obtained. Fourier transformation is taken on these PSFs and then modulus value calculation is adopted to get the MTFs. By analyzing the MTFs of idealized optical systems, the influence of each monochromatic aberration on the image can be concluded. Note that some of the MTFs values have zeros and others do not. Considering that imaging processing algorithm cannot recover the information lost at such zeros, thus some aberrations can be easily corrected through imaging processing algorithm, while others not. Aimed at a single monochromatic aberrations respectively, the optical system with single lens is designed in ZEMAX software and the relevant imaging processing algorithm is adopted. From the comparison among the final images, the theoretical foundations for the computing optical design method considering the MTF and aberrations can be verified. The compensation for aberration of imaging processing can be utilized to reduce the pressure of optical system design. The computing optical design method provides a new approach to simplify structure and reduce cost, as well as to gain high resolution images simultaneously, especially for designing certain complex optical system, which shows promising perspective of industrial application.

The vision sensor which is based on the optical triangulation theory with the laser as an auxiliary light source can detect not only the seam position but the shape of seam. In this study, a vision sensor using the scanning laser beam was investigated. To design the vision sensor which considers the reflectivity of the sensing object and satisfies the desired resolution and measuring range, the equation of the focused laser beam which has a Gaussian irradiance profile was firstly formulated, Secondly, the image formaing sequence, and thirdly the relation between the displacement in the measuring surface and the displacement in the camera plane was formulated. Therefore, the focused beam diameter in the measuring range could be determined and the influence of the relative location between the laser and camera plane could be estimated. The measuring range and the resolution of the vision sensor which was based on the Scheimpflug's condition could also be calculated. From the results mentioned above a vision sensor was developed, and an adequate calibration technique was proposed. The image processing algorithm which and recognize the center of joint and its shape informaitons was investigated. Using the developed vision sensor and image processing algorithm, the shape informations was investigated. Using the developed vision sensor and image processing algorithm, the shape informations of the vee-, butt- and lap joint were extracted.

This paper presents the image processing algorithm for wireless charging between each mobile robot. The image processing algorithm converts Red Green Blue (RGB) format of inputted image to detect edge. It calculates a specific area using Hough Transformation (HT) in detected edge and judges correct charging antenna using Speeded-Up Robust Features (SURF). Accordingly, the image processing algorithm can control position and direction of mobile robot and antenna for wireless charging. The image processing algorithm is implemented wireless charging systems, which are set up on each two mobile robot and it is verified with experiment.

This thesis aims to develop an embedded image processing board, which can be used to realize various image feature extraction and processing algorithms for robotic applications. The circuitry of this image board can be divided into two parts, one is for a digital signal processor (DSP) and the other for a field programmable gate array (FPGA). A low power, small size CMOS image sensor with YUV4:2:2 image format and 512x480 pixel size is adopted for image acquisition. The DSP module is mainly used for implementing image processing algorithms, while the FPGA module for lower level image processing and sensor control functions. In this work, the TMS320C6414TGLZ7 DSP from Texas Instruments Corporation and the EP2C35F672C6 FPGA from Altera Corporation were selected for the image board. In this work, speeded-up robust features (SURF) algorithm has been successfully realized on the developed image board. An USB interface with average throughput of 22MB/sec works to transfer acquired image frames and extracted SURF descriptors to a personal computer, where feature matching is executed. Experimental results show that the refresh period of frame grabbing is about 33.5ms. When the number of SURF descriptor is equal to 64, the computing time is about 417.5ms; when the number of SURF descriptor is equal to 199, the computing time is about 985ms. The typical matching rate varies from 70.2% to 20%, depending on the threshold of the patterns used in feature extraction.

Droplet impingement cooling on flat surfaces finds its applications in spray cooling of hot surfaces, deposition of molten metal on surfaces, plasma coating, cooling of brake pad, turbine blade and in electronic chip cooling. Optimization of such a process requires a thorough understanding of the hydrodynamic process which happens during impingement of the droplet on the surface. Image processing techniques can be employed to understand the process. Image processing is converting an image into digital form and analyzing it, to get an improved image and extracting the desired information from it. The technique is widely used in face detection, remote sensing applications, machine vision and industrial robotics. In the present work an image processing algorithm is developed for a specific surface cooling technique. A hot surface is treated by impinging droplets. The size of the droplets can alter the material characteristics significantly. The objective of the present work is to develop an image processing algorithm to find the droplet dimensions. The variation in radius and height is examined with the help of Matlab software. Edge detection techniques are used to determine the boundaries of the droplet clearly. The algorithm is developed using Matlab toolbox and is capable of measuring droplet height and spreading radius in detail. The advantage of this algorithm is that the technique can be employed to the process in real time.

To solve the problems of low signal to noise ratio (SNR), poor processing effect, and long processing times for traditional image processing algorithms, an image processing algorithm for the agricultural Internet of Things platform based on big data analysis was designed. The big data analysis method was used for agricultural low-quality image recognition, and the Internet of Things platform uses sparse representation and a complete dictionary of low-quality image denoising processing with an improved histogram equalization method to enhance the image denoising result and get a high-quality image. Through this, the image processing algorithm design of the agricultural Internet of Things platform was completed. Experimental results show that the algorithm has high SNR and a good image processing effect, and the image processing time is always below 0.7 s, which meets research expectations.