An integrated microfluidic device for the sorting of yeast cells using image processing

In recent years, several publications on microfluidic devices have focused on the process of electroporation, which results in the poration of the biological cell membrane. The devices involved are designed for cell analysis, transfection or pasteurization. The high electric field strengths needed are induced by placing the electrodes in close proximity or by creating a constriction between the electrodes, which focuses the electric field. Detection is usually achieved through fluorescent labeling or by measuring impedance. So far, most of these devices have only concerned themselves solely with the electroporation process, but integration with separation and detection processes is expected in the near future. In particular, single-cell content analysis is expected to add further value to the concept of the microfluidic chip. Furthermore, if advanced pulse schemes are employed, such microdevices can also enhance research into intracellular electroporation.

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 kind of light-duty automation system is presented for the automation of detecting and grading eggs, namely system of intelligent robot detecting and grading eggs(SIRDGE). This system enables crack detection, inner quality detection and eggs conveying to be combined. All these tasks are done by a joints robot and the whole process of detecting and grading is automatic. The frameworks and work course of the system are presented. Necessary explanation of the main hardware and the software is made, which includes how to capture image by scene video camera and the camera calibration, algorithm for processing images and distilling eigenvalue from them, motion controlling and trajectory planning of the robot when detecting and grading eggs, single chip micyoco program for controlling the suction cup, processing the sounds by tapping egg shell and the crack egg detection, color image processing and inner quality of eggs detection, etc. Some tests are done and the test parameters are given.

Active microfluidic systems for cell sorting and separation

Several problems are encountered in modeling and processing of many life science applications such like tissue engineering, bioacoustics, and food processing detection. Recently, various image processing techniques have been developed in an attempt to solve these problems. Due to the rapid advance and high number of researches included in such field, there is always a room for improvement. In this work, advanced image processing approaches are presented and applied to different life science applications. The first approach utilizes image processing in the generation of micro-scaffolds for tissue engineering modeling. Furthermore, a spectrogram enhancement approach is developed to improve the pattern detection of weevils existence in grain storage, based on their detected and identified sounds. Finally, a novel edge detection method applied on intramuscular fat recognition is presented based on the energy and skewness as two smoothed versions of the meat image. Results, in comparison to experimental data, offer a maximum error of 7.81% in the specified detection process. 1. Material and Method 1.1 Generation of micro-porous approach The difficulty of modeling such complex micro-structures always lies in retrieving the geometry into the solver as edges. Edge detection algorithms are based upon highlighting discontinuities in images and were first developed for satellite images. The technique has become well-known and was further developed and widely used for other image processing applications. During the conventional detection procedures, usually all the image size is processed, which leads to redundant calculations of areas containing no edge information. In this study a redundant avoiding algorithm is developed which searches for the threshold value in the image and then propagates around it in a 2-D lattice in eight directions figure 1.1, until an edge is hit. Figure 1.1 The scanning procedure for gradient changes propagates in an eight directional lattice until an edge is hit 1.2 Spectrogram enhancement in bioacoustics This approach is based on detection of the sound pattern edges in the crest factor image, as shown in figure 1.2 and 1.3, which behaves as a smoothed version of the spectrogram image and avoids the use of edge detectors prior smoothing filters and their scaling constraints. Figure 1.2 Spectrogram with 40 dB limited dynamic range Figure 1.3 Crest factor distributions (left), the scaled crest factor image of the spectrogram (right). 1.3 Edge detection for intramuscular fat recognition A novel edge detection method applied on intramuscular fat is presented based on the energy and skewness as two smoothed versions of the meat image, as shown in the flow chart given in figure 1.4. Figure 1.4 flow chart edge detection 2. Results 2.1.Generation of micro-porous Approach The conventional approach for edge detection is based upon either thresholding the first derivative (gradient) of the image or locating zero crossings of its second derivative (Laplacian).The sample used, figure 2.1, for this simulation was of size 3 mm, 350 μm and 350 μm in z-direction, x-direction and y-direction, respectively. The geometry was uploaded on the solver by collective segmenting and edge detection techniques. The method has proven time saving and as well enables the possibility of parallel processing of the image, as each threshold value can be scanned and dealt with independently. After the predictions of the edges, the 2-d binary edge images are assembled forming a 3-D binary file of the scaffolds figure 2.1. Figure 2.1 From the micro-CT images 430 hundred 2-D binary edge images are assembled forming a 3-D binary file of the scaffolds 2.2.Spectrogram enhancement in bioacoustics The spectrogram enhancement approach is compared to other enhancement approaches, including multiband spectral subtraction and wavelet packet decomposition, for different structure bioacoustics calls including bats (ultrasound range) and birds (acoustic range), with additive white Gaussian noise added across a range of signal to noise ratios. Results measured subjectively and objectively, indicate that the proposed method is promising for efficiently spectrogram enhancing while preserving its temporal and spectral accuracy, as shown in figure 2.2. Figure 2.2 (a) One of Sitta Canadensis bird calls at SNR = 20 dB, and its enhanced spectrogram by (b) BP, (c) MBSS, (d) WF, (e) WPD, and (f) the proposed method approaches. 2.3.Edge detection for intramuscular fat recognition The generation of micro-scaffolds approach is applied on a sample meat image, which is scanned line by line and every noticeable variance in the color values or the grey levels is captured and the local thresholds values are estimated according to the highest drop gradient value. The local threshold values are averaged to estimate the global threshold for the whole image, as shown in figure 2.1. Figure 2.3 an image for a meat with fat (a), the x y scan of the image which does not show all the fat details (b) and the lattice scan of the image showing more details of the fat, as circled by red (c). For intramuscular fat, the developed edge detection approach is used to predict the fat content in marbled meat images, as shown in figure 2.3. Figure 2.4 (a) original marbled meat images, (b) edges images using the proposed method (c) detected fat regions, and (d) the percentage of fat content Conclusion As observed in the different approaches there is always a room for improvement that minimize the error observed in the specified detection process. The methods have proven time saving and as well enables the possibility of parallel processing of the image that leads to a more efficient performance highly desirable for industrial applications.

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.

This conference was a successful continuation of previous conferences such as the Hungarian Cell Analysis Conference, Budapest, 1998 and 2000, and the ISAC-Sponsored International Conference for Flow Cytometry and Image Analysis, Epona, 1999. The Cell Analysis Section of the Hungarian Biophysical Society organized all four conferences. The popularity of the conference was hallmarked by the large number of participants (close to 300). The form of the conference proved to be very attractive: In the mornings, experts from specific fields delivered scientific lectures. In addition to Hungarian scientists, such as Margit Balazs, Gyula Hadlacky, Bela Molnar, Laszlo G. Puskas, Janos Szollosi, and Gyorgy Vereb, experts from abroad significantly raised the scientific standards of the conference. The international speakers included Peter Adorjan, Francis Mandy, Abe Schwartz, Howard M. Shapiro, and the authors of this introduction. In the afternoons, practical demonstrations were presented, ranging from basic techniques to stateof-the-art instrumentation. Practical training included cell culturing; mechanical cell separation for flow cytometry; methods for detection of cell proliferation; detection of apoptosis; fluorescence microscopy, image acquisition, and processing; confocal laser microscopy; fluorescence in situ hybridization; magnetic cell separation; magnetic mRNA isolation; real-time and traditional polymerase chain reaction; and detection of mutation. During the conference the attendees were able to choose six sessions of 11 topics. These demonstrations were headed by one of the speakers and sponsored by commercial companies that provided special kits and/or instruments to ensure a stable basis for successful practical training. An impressive selection of commercial companies provided contacts and improved awareness of advanced analytical cytology for the participants. The purpose of the conference was multifaceted. It served as a forum for presentations from Hungarian scientists, and, with the active participation of foreign experts, it opened a window to state-of-the art methods and techniques. The conference also facilitated the research cooperation between well-established scientists and Ph.D. graduate students, with the major purpose of the conference being education. The average age of the attendees was approximately 30 as more than 50% of the participants were Ph.D. students. Graduate students were able to earn credit points toward their final examination. The venue for the conference was the Medical University of Semmelweis in Budapest, the capital of Hungary. The university has great traditions, with its name reflecting the rich history of scholastic activity in Budapest. It was over 150 years ago that Ignaz Semmelweis completed his classic epidemiologic study in Budapest, studies that led to effective prophylaxis against childbed fever that still stands as the hallmark of evidence-based studies in epidemiology. At the conference both the lectures and the posters proved to be of high quality, demonstrating the high scientific standards of the community. The topics of the posters ranged from oncology (basic and applied research) to signal transduction, genetics, fertility research, and investigation in lower organisms such as Drosophila and earthworm. The methods applied were varied and highly sophisticated. Of 29 poster presentations, three prizes were awarded. Originally the poster awards committee (Margit Balazs and Attila Tarnok) was supposed to give only one award, but the decision was not easy because of the large number of excellent posters. With the financial help of commercial companies, the committee was able to present three poster awards instead of one. These awards were: First prize: Generation and Characterization by Flow Cytometry of Dendritic Cells, by Gizella Veszely, Janos Fent, Agnes Nagy, and Furesz Jozsef, Department of Pathophysiology, Institute for Health Protection of HDF, Budapest. Second prize: Hemocyte-Specific Molecular Markers in the Hematopoiesis and Innate Immunity of Drosophila melanogaster, by Istvan Nagy, Eva Kurucz, and Istvan Ando, Institute of Genetics, Biological Research Center of the Hungarian Academy of Sciences, Szeged. Third prize: Astrocytes Support the Neuronal Differentiation of Neuroectodermal Progenitor Cells, by Vanda Szlavik, Zsuzsanna Kornyei, and Emilia Madarasz, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest. On the following pages, the reader will find the rich program of the Third Hungarian Cell Analysis Conference through the lecture and poster abstracts.

Life Sciences Discovery and Technology Highlights

Polypyrrole based DNA hybridization assays: study of label free detection processes versus fluorescence on microchips

In addition to causing damage to vehicles, road defects are one of the main causes of vehicle accidents which lead to loss of human lives. Many methods of detecting defects have been introduced over the years to reduce the consequences of these defects. One of these methods is image processing. Use of image and video processing has many applications in medicine, science, agriculture, and defect detection in structures. It has been used for defect detection on roads because timely detection and analysis of defect is very important for road serviceability and safety of the people. Detection of a defect by image processing broadly follows some of the basic steps which include feature extraction, edge detection, morphological operators, and training of data. Different approaches are used for various kinds of defect detection and analysis which have replaced the manual inspection method of roads saving time and resources. This chapter discusses the basic steps involved in defect detection using image processing along with existing systems that use machine learning and artificial intelligence for the detection of defects from a distance. To write this chapter, papers on the topic of image and computer vision-based defect detection systems have been consulted.

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

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