Using Image Analysis Systems in Forensics and Security Printing

The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based 3D image processing system. The distributed and networked analysis of volumetric image data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. We present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis. DINAVID is designed using open-source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis and a 3D visualization system. DINAVID is a network-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID enables the image access model of a center hosting image volumes and remote users analyzing those volumes, without the need for remote users to manage any computational resources. The DINAVID system, designed and developed using open-source tools, enables biologists to analyze and visualize 3D microscopy volumes remotely without the need to manage computational resources. DINAVID also provides several image analysis tools, including pre-processing and several segmentation models.

P-glycoprotein is implicated in clinical drug resistance; thus, rapid quantitative analysis of its expression and activity is of paramout importance to the design and success of novel therapeutics. The scope for the application of quantitative imaging and image analysis tools in this field is reported here at "proof of concept" level. P-glycoprotein expression was utilized as a model for quantitative immunofluorescence and subsequent spatial intensity distribution analysis (SpIDA). Following expression studies, p-glycoprotein inhibition as a function of verapamil concentration was assessed in two cell lines using live cell imaging of intracellular Calcein retention and a routine monolayer fluorescence assay. Intercellular and sub-cellular distributions in the expression of the p-glycoprotein transporter between parent and MDR1-transfected Madin-Derby Canine Kidney cell lines were examined. We have demonstrated that quantitative imaging can provide dose-response parameters while permitting direct microscopic analysis of intracellular fluorophore distributions in live and fixed samples. Analysis with SpIDA offers the ability to detect heterogeniety in the distribution of labeled species, and in conjunction with live cell imaging and immunofluorescence staining may be applied to the determination of pharmacological parameters or analysis of biopsies providing a rapid prognostic tool.

The article considers the term "forensic", new for Russian economic theory and practice, that means "forensic science" from English. Its importance and implementation is justified by the increasing number of cases of widespread financial fraud. Forensic is just intended for their regulation and prevention. The conducted systematization of scientific publications testifies to the accumulation and theoretical generalization of materials that provide the basis for the formation of forensic as a separate type of expert research. Forensic is a certain niche in the professional sphere and is endowed with such mandatory attributes as rationality, neutrality and independence. The main goal of the forensic investigation is the disclosure of fraud, in general which includes the identification of those involved, the formation of evidence base, the establishment of facts and mechanisms of fraudulent activity. Forensic is held at the invitation of managers, investors and shareholders to identify not only additional resources, but also possible risks of the organization. It is indicated that underground economy, fraudulent activity can serve as an object of forensic. The forensic classification is proposed for corporate (internal) and forensic counterparties (external). In addition, we should pay attention to a rather new direction in the field of computer forensics with a similar name - forensic, which can be part of the forensic investigation. It is revealed that experts in the field of forensic use to a greater extent accounting and auditing procedures in their activities. Despite the forensic similarity to the audit and forensic accounting expertise, there are still distinctive features that are discussed in the article. Spheres of additional knowledge necessary for a specialist to work in the field of forensic are: sociology, psychology, criminal law, business law, law enforcement, business and finance, information systems, communism. It is concluded that the consolidation at the legislative level of the main provisions and standards concerning specialists in the field of forensic is necessary in the current conditions of management.

Automated determination of poplar chip size distribution based on combined image and multivariate analyses

Received: 2018-06-07 | Accepted: 2018-06-19 | Available online: 2018-06-30 https://doi.org/10.15414/afz.2018.21.02.77-83 The aim of this study was to distinguishing between kernels of maize hybrids by the use of image analysis tools. We analyzed 10 registered Zea mays L. hybrids (5 – dent, 2 – semi-flint to flint, and 3 – semi-flint to dent type). Different parameters on ventral, dorsal, corolla side, and lateral side cross section of kernel were measured. Sample per each hybrid comprised 50 maize kernels. Acquired bio-images were processed by software Zeiss AxioVision Rel. 4.8. We analyzed the segmented regions of interest on the kernels. The data for area (mm 2 ), height and width (mm) were gathered from these regions. The hybrid ZE EDOX significantly differed (p < 0.05) from all other hybrids almost in all traits. It is the hybrid with the smallest area of the whole kernel, floury endosperm proportion, and depressed part on corolla. The new trait the area of the depressed part on the kernel corolla was measured. The hybrids with smaller proportion of floury endosperm had smaller area of depressed part, and vice versa. The image analysis methods can usefully contribute to selection of proper hybrids for different types of use. Keywords: maize, Zea mays L., kernel, image analysis References BLASCHKE, T. et al. (2014) Geographic Object-Based Image Analysis – Towards a new paradigm. In ISPRS Journal of Photogrammetry and Remote Sensing, vol. 87, pp. 180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014 DE CARVALHO, M. L. M., VAN AELST, A. C., VAN ECK, J. W., HOEKSTRA, F. A. (1999) Pre-harvest stress cracks in maize (Zea mays L.) kernels as characterized by visual, X-ray and low temperature scanning electron microscopical analysis: effect on kernel quality. In Seed Science Research, vol. 9, pp. 227–236. DELL´AQUILA, A. (2006) Computerised seed imaging: a new tool to evaluate germination quality. In Communications in Biometry and Crop Science, vol. 1, no. 1, pp. 20–31. ERASMUS, C. (2003) Maize kernel translucency measurement by Image Analysis and its relationship to vitreousness and dry milling performance. PhD thesis. Pretoria: University of Pretoria. FOX, G. and MANLEY, M. (2009) Hardness methods for testing maize kernels. In Journal of Agricultural and Food Chemistry, vol. 57, no. 13, pp. 5647–5657. doi: https://doi.org/10.1021/jf900623w GLASBEY, C. A. and HORGAN, G. W. (2001) Image analysis in agriculture research. In Quantitative Approaches in System Analysis, special issue, vol. 23, pp. 43–54. GUELPA, A., DU PLESIS, A., KIDD, M., MANLEY, M. (2015) Nondestructive estimation of maize (Zea mays L.) kernel hardness by means of an X-ray micro-computed tomography (μCT) density calibration. In Food and Bioprocess Technology, vol. 8, no. 6, pp. 1419–1429. JANDA, J. and MICHALEC, V. (1982) Mayze. Bratislava: Priroda. 408 p. MUTTERER, J. and ZINCK, E. (2013) Quick‐and‐clean article figures with FigureJ. In Journal of Microscopy, vol. 252, pp. 89– 91. doi: https://doi.org/10.1111/jmi.12069 ROBUTTI, J. L., HOSENEY, R. C. and WASSOM, C. E. (1974). Modified opaque-2 corn endosperms. ll. Structure viewed with a scanning electron microscope. In American Association of Cereal Chemists, vol. 51, pp. 173–180. RODRIGUEZ-PULIDO, F. J. et al. (2012) Ripeness estimation of grape berries and seeds by image analysis. In Computers and Electronics in Agriculture, vol. 82, pp. 128–133. SCHINDELIN, J., RUEDEN, C. T., HINER, M. C., ELICEIRI, K. W. (2015) The ImageJ ecosystem: An open platform for biomedical image analysis. In Mol. Reprod. Dev., vol. 82, pp. 518–529. doi: https://doi.org/10.1002/mrd.22489 UCHIDA, S. (2013) Image processing and recognition for biological images. In Development, growth and differentiation, vol. 55, pp. 523–549. doi: https://doi.org/10.1111/dgd.12054 WATSON, S. A. (1987) Structure and composition. In WATSON, S. A. and RAMSTAD, P. E. (Eds.). Corn chemistry and technology. St. Paul: American Association of Cereal Chemists. WIWART M. et al. (2012) Identification of hybrids of spelt and wheat and their parental forms using shape and color descriptors. In Computers and Electronics in Agriculture, vol. 83, pp. 68–76. https://doi.org/10.1016/j.compag.2012.01.015

Development of reddish orange-emitting Y2Sn2O7:Eu3+phosphors for latent fingerprint detection and anti-counterfeit security ink

The finite Zak transform: An efficient tool for image representation and analysis

The recent development of complex chemical and small interfering RNA (siRNA) collections has enabled large-scale cell-based phenotypic screening. High-content and high-throughput imaging are widely used methods to record phenotypic data after chemical and small interfering RNA treatment, and numerous image processing and analysis methods have been used to quantify these phenotypes. Currently, there are no standardized methods for evaluating the effectiveness of new and existing image processing and analysis tools for an arbitrary screening problem. We generated a series of benchmarking images that represent commonly encountered variation in high-throughput screening data and used these image standards to evaluate the robustness of five different image analysis methods to changes in signal-to-noise ratio, focal plane, cell density and phenotype strength. The analysis methods that were most reliable, in the presence of experimental variation, required few cells to accurately distinguish phenotypic changes between control and experimental data sets. We conclude that by applying these simple benchmarking principles an a priori estimate of the image acquisition requirements for phenotypic analysis can be made before initiating an image-based screen. Application of this benchmarking methodology provides a mechanism to significantly reduce data acquisition and analysis burdens and to improve data quality and information content.

Image analysis system for assessment of immunohistochemically stained proliferative marker (MIB-1) in oesophageal squamous cell carcinoma

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

An in-situ image acquisition and analysis system was developed to characterize the dynamics of particle size distribution (PSD) during flocculation. The system consisted of a flocculator coupled with an external flow-through cell for observation and photography, a microscopic charge-coupled device video recorder with backlighting, and an image analyzer. This non-intrusive side-stream setup was used to record the evolution of the PSD to determine the flocculation dynamics of two types of particle systems: Clean kaolin, kaolin coated with natural organic matter (NOM). The results were discussed and the error induced during measurement was evaluated.

The aim of this study is to review the methods being used for image analysis of microscopic views of immunohistochemically stained specimen in medical research. The solutions available range from general purpose software to commercial packages. Many studies have developed their own custom written programs based on some general purpose software available. Many groups have reported development of computer aided image analysis programs aiming at obtaining faster, simpler and cheaper solutions. Image analysis tools namely Aperio, Lucia, Metaview, Metamorph, ImageJ, Scion, Adobe Photoshop, Image Pro Plus are also used for evaluation of expressions using immunohistochemical staining. An overview of such methods used for image analysis is provided in this paper. This study concludes that there is good scope for development of freely available software for staining intensity quantification, which a medical researcher could easily use without requiring high level computer skills.

A new interactive image analysis method for determining tree-ring density and width data for dendrochronological studies is described. The method employs the Prism Image Analysis System (from Dapple Systems, Inc.) for Apple Macintosh microcomputers and uses additional FORTRAN software (program MACDRUID) developed for tree-ring applications. The principal advantages of this system are (i) interactive image analysis, which substitutes computer software for the complex scanning densitometer operations used in previous systems and (ii) high resolution, which is necessary for processing the narrow-ringed, stressed trees often sampled in dendroclimatology. Other advantages are real-time editing, ease of adjustment for angle variation of ring boundaries, and the ability to append individual frame data into homogeneous time series. Comparisons of ring-width and density data determined by this image analysis system with similar measurements derived from other methods indicate that the image analysis data are of equal precision and quality for use in dendrochronological studies. In addition to applications in the fields of dendrochronology and wood science, this image analysis method is applicable for analyzing variations in cyclical banding in other types of geological samples, such as sediment laminae (e.g., varves) and corals.

ABSTRACTPerennial ryegrass (Lolium perenne L.) is widely used in grazing systems across the temperate world. Continued persistence of ryegrass pastures depends on our ability to further our understanding of the key traits or attributes responsible for improved plant performance. To this end, plant improvement is highly dependent on precise phenotyping capabilities. However, in‐field phenotyping is labor intensive, relies on experienced operators, and is often considered cost prohibitive. The aim of this study was to determine the accuracy of an image analysis tool developed to locate and estimate dry matter (DM) production of individual plants growing in the field. Fifty plants were used to develop the image analysis tool and another 1100 to determine its accuracy. Dry matter was measured by weighing oven‐dried harvested plant material and estimated both with the image analysis tool and by visual scoring. The image analysis tool was faster and estimates of plant area were more highly correlated with measured DM values, potentially capturing 25% more of the variation, than those from visual growth scores. The tool was able to effectively locate and distinguish between neighboring plants. The accuracy of the tool was greatest for production values below 15 g, and the tool would benefit from further development to improve accuracy above this threshold.

Tissue-based investigations can prove challenging due to complex tissue architecture and heterogeneous biomarker expression, visual and cognitive “traps” that affect interpretive precision, and subjective assessments that affect reproducibility. A major concern is that these challenges could increase the risk of failure for therapeutic/diagnostic co-development and clinical use, as the biomarker measurements continue to increase in complexity and require increasingly precise diagnostic cut-points. Image analysis tools have been developed to overcome some of the challenges for conventional anatomic pathology practices, capitalizing on the objectivity and computational power of a digital platform. A computer, however, lacks the cognitive ability and experience of a human to interpret tissue architecture and context. Flagship Biosciences’ computational Tissue Analysis (cTA™) platform integrates the power of our tissue Image Analysis (tIA™) technology with the contextual experience of an anatomic pathologist to produce robust, precise, quantitative results that demonstrate biomarker content in the tissue context. Flagship Biosciences envisions the integration of our cTA™ technology into a computer-aided clinical pathology workflow as a method to improve the precision of scoring for even some of the most challenging tissue-based biomarker measurements. In a proof-of-concept study, we evaluated the performance of manual versus digital scoring approaches in a cohort of non-small cell lung carcinoma (NSCLC) samples stained with the IHC protocol for the PD-L1 PharmDx 28-8 complementary diagnostic. A comparison of the 2 modalities demonstrated that in nearly all cases, the within sample standard deviation of the cTA™ digital score results was less than the manual score (median inter-pathologist %CVs were reduced from 124.9% to 7.8% and intra-pathologists from 65.4% to 7.6% for manual and digital scores, respectively). As an additional exploratory examination, the effect of heterogeneity on PD-L1 interpretation was also investigated. Pathologists evaluated the same whole tissue slides within 5 high powered fields (HPFs) using both manual and cTA™ -derived scoring. Results demonstrated that the use of cTA™ provides improves agreement between HPF and whole slide assessments (absolute difference between the manual scores from HPF to whole slide were larger than the absolute differences for the digitally derived scores, at 3.14% and 8.27%, respectively). Taken together, these studies demonstrate that the use of cTA™ can significantly reduce variability in PD-L1 scoring, as compared to a manual scoring approach. Citation Format: Staci Kearney, Joshua Black, Famke Aeffner, Joshua Black, Luke Pratte, Joseph Krueger. Evaluating benefits of PD-L1 image analysis for the clinical setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4582. doi:10.1158/1538-7445.AM2017-4582