Specialized Information Systems for Image Analysis in Physiological Studies

Automated deep learning system for power line inspection image analysis and processing: Architecture and design issues

Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain.

In order to overcome the difficulty of massive high-dimensional image processing, a massive high-dimensional image processing system based on big data technology is proposed in this paper. The system fully combines the big data technology, analyzes the differences between the current massive high-dimensional image processing work and the traditional image processing work, and discusses the difficulties of the current massive high-dimensional image processing work. Based on this, the system also includes parallel image processing model and low delay dynamic processing model, which greatly improves the accuracy and efficiency of static image processing and dynamic image processing. The results demonstrate that the system has strong feasibility, and can indeed process large-scale high-dimensional image data and save the time and cost of data processing.

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

This thesis presents techniques for modeling and control of X-ray image processing tasks, aiming at the fluent execution of a multitude of diagnostic and interventional X-ray imaging applications on a multi-core computing platform. A general trend in medical imaging systems is the execution of image processing on general-purpose programmable platforms, instead of specific dedicated hardware solutions. This even holds for the newest functionality such as image analysis, which performs computations in a more stochastic manner, rather than stream-based processing. The trend of more dynamic image processing and the new analysis functions in X-ray imaging, combined with the desire for more programmable and flexible future systems forms the starting point of our research. The first contribution of this thesis is the modeling and optimization of the processing performance of stream-based medical imaging tasks, in particular image quality enhancement for interventional X-ray. We have defined rules for specifying and dividing image processing tasks for parallel processing, to optimize the related memory communication. Similarly, for the computing architecture, we have specified the detailed timing requirements for data storage and communication, incorporating memory-access times. In the initial situation, the computing system was fully loaded, both in terms of memory and computation, and the latency was strongly time varying and could not be predicted. Our modeling has yielded a good understanding of the actual execution and its critical factors. The model has eventually lead to an approach for task splitting leading to a sharper system optimization with respect to essential parameters, such as latency. However, the method for building the model is time consuming. The same holds for the architecture. Nevertheless, the experiment has been highly valuable since the system optimization is associated with a considerable system cost and the resulting alternative execution architectures have provided ways for cost reduction or adding attractive functionality to the same system, which currently is executed on an additional separate platform. The second contribution of this thesis is the modeling and prediction of the computation time of feature-based medical imaging tasks, where the task complexity is non-deterministic and the resource demands fluctuate with the image content. We have focused in particular on applications for advanced diagnosis with image analysis and motion-compensated subtraction imaging. We demonstrated that the computation time for tasks with purely random resource usage can be successfully predicted with zero-order Markov chains. Furthermore, first-order Markov chains are used if temporal correlation between the computation time statistics exists for only short periods of time. For structural correlations between image frames, scenario-based methods have to be added to the obtained prediction model, extracted from the flow graph of tasks. Alternatively, when the computational complexity depends on external (spatial) factors, the prediction model is based on spatial (look-ahead) prediction. Experimental results have shown that it is possible to predict the computation time of feature-based medical image applications, even if the flow graph dynamically switches between groups of tasks. We have found an average accuracy for two different application scenarios between 95 – 97%, with sporadic excursions of the prediction error up to 20 – 30%. The third contribution of this thesis is the design of a control system for the fluent execution of a set of applications on a multi-core platform, where some of the applications have a fluctuating resource demand and others have strict low-latency requirements. As a possible solution, we have implemented options for scalability in applications for three application scenarios, using task scaling, task skipping and task delaying. A Quality-of-Service (QoS) control system then maintains constant throughput and latency by dynamically switching between application quality modes. A global resource manager maintains the overall resource usage of the system and local application controllers are responsible for resource estimation and quality control of each individual application. The global resource manager is based on a modified version of the Lagrangian relaxation algorithm that searches for suitable combinations of quality levels for a set of concurrent running applications. The research has been validated by executing three medical imaging applications in parallel, from which two are critical in latency. It is interesting to know that the above solution has a high industrial relevance because it can facilitate in reducing system costs for applications that are already released on the market. The work has resulted in a solution featuring a combination of both interventional and diagnostic signal processing, which are executed on a single computing device with nearly the same quality, as compared to two separated computers with those tasks in the original setting. Furthermore, the techniques can be employed for different classes of systems, with different cost-performance points.

With today's advancing technology, support of developing hardware and software systems, the developments in the field of medicine have increased considerably. In particular, medical image analysis and processing systems have taken a considerable lead. The development of an automatic system could provide great convenience for doctors and practitioners in the field. The image processing techniques proposed in this study can contribute to more effective analysis and more accurate diagnosis, regardless of the individual levels of experience of the users or particular situations and conditions such as fatigue or image quality. This paper presents a robust method for retinal blood vessel segmentation and some automatic algorithms for analyzing the vessel network and pixel classification into vessel and non-vessel .The aim of the work extraction or segmentation of retinal blood vessels used computer vision and image processing for getting high accuracy (comparison with manual) .Also We used the preprocessing techniques for enhancement of the image .And used two types of filter for comparing result to get best scenario. Also for simulation result we used the matlab and implement on DRIVE database.

The directions for improving the organizational and legal instruments to ensure public administration in the field of biosafety of Ukraine are considered. It is proposed to create a permanent National Monitoring Centre for Biological Threats taking into consideration that there is no single authorized body in the field of biosafety in Ukraine, and the functions of monitoring epidemic and epidemiological information are assigned to various institutions. The activities of the monitoring centre are designed to support organizationally and functionally the existing system for responding to emergencies of a medical and biological nature and to increase its efficiency by significantly reducing the response time to emergency situations. It is proven that the integration of the existing sources of information on current biological risks in a dynamic mode within a single centre will significantly speed up the response time to the emergence of biological threats. It provides a transition from retrospective analysis to monitoring risks and their prevention. Taking into account that the effectiveness of the mechanism of public administration in the field of biosafety depends on the efficiency of public authorities, the directions for improving the organizational and legal support of public administration in the field of biosafety of Ukraine is presented. It is substantiated that it is expedient to improve the information support of public administration in the field of biological safety, in particular, through the formation of relevant databases covering the field of professional training, human resources, and research activities in the field of biological safety. The need to form the centralized registers is confirmed by foreign experience. The formation of information databases allows creating not only conditions for the controllability of the biological safety system within Ukraine, but also contributes to the formation of an effective mechanism of international cooperation (international mediation in this area).

<p>The article analyzes the basic methods and algorithms of mathematical processing of medical images as objects of computer mathematics. The presented methods and computer algorithms of mathematics relevant and may find application in the field of medical imaging - automated processing of images; as a tool for measurement and determination the optical parameters; identification and formation of medical images database.</p><p>Methods and computer algorithms presented in the article & based on Wolfram Mathematica are also relevant to the problem of modern medical education. As an example of Wolfram Mathematica may be considered appropriate demonstration, such as recognition of special radiographs and morphological imaging. These methods are used to improve the diagnostic significance and value of medical (clinical) research and can serve as an educational interactive demonstration.</p><p>Implementation submitted individual methods and algorithms of computer Wolfram Mathematics contributes, in general, the optimization process of practical processing and presentation of medical images.</p>

Image processing in structural biology : M. Kessel Department of Membrane and Ultrastructure Research, Hebrew University — Hadassah Medical School, Jerusalem 91010, Israel

As the Academician-Secretary of the Department for Nanotechnology and Information Technology of the RAS, I talk on milestones of administrative and scientific work of President of S. P. Korolyov Samara National Research University, Research Director of the Image Processing Systems Institute of the Russian Academy of Sciences, and Academician of the RAS, Victor Alexandrovich Soifer, an outstanding scientist in the field of diffractive computer optics and image processing. I am looking into his contribution into the development of diffractive optics & diffractive nanophotonics and systems for image analysis & understanding.

An increasing number of free software tools have been made available for the evaluation of fluorescence cell micrographs. The main users are biologists and related life scientists with no or little knowledge of image processing. In this review, we give an overview of available tools and guidelines about which tools the users should use to segment fluorescence micrographs. We selected 15 free tools and divided them into stand-alone, Matlab-based, ImageJ-based, free demo versions of commercial tools and data sharing tools. The review consists of two parts: First, we developed a criteria catalogue and rated the tools regarding structural requirements, functionality (flexibility, segmentation and image processing filters) and usability (documentation, data management, usability and visualization). Second, we performed an image processing case study with four representative fluorescence micrograph segmentation tasks with figure-ground and cell separation. The tools display a wide range of functionality and usability. In the image processing case study, we were able to perform figure-ground separation in all micrographs using mainly thresholding. Cell separation was not possible with most of the tools, because cell separation methods are provided only by a subset of the tools and are difficult to parametrize and to use. Most important is that the usability matches the functionality of a tool. To be usable, specialized tools with less functionality need to fulfill less usability criteria, whereas multipurpose tools need a well-structured menu and intuitive graphical user interface.

Recently, we can easily obtain a fast computer and a digital video camera as their prices go down. In this paper, we describe a method and a system for analyzing performance of tennis players of a given singles tennis game. We assume that a movie of the game has been shot by a digital video camera which is fixed at a place such that the camera could film whole area of the tennis court and the two players, and that we have the sequence of the images in the computer. Using the positions of the players and the ball detected by techniques of image processing and partially by human assistance, we analyzed the game by defining an evaluation function. We made a prototype of a tennis-player-evaluation system by implementing the method. The system appropriately evaluated performances of players of some sample tennis games, although the evaluation function cannot analyze the player's tactics enough.

This research introduces an automatic scoring mechanism based on motion analysis of a player by video image processing and database of skills in gymnastics. The automatic scoring system will be made for the horizontal bar which is one of the gymnastics in particular. A scoring result is equivalent to scoring until now according to experience of a referee and the rulebook, or in order to perform highly precise scoring, it does not use the standard of scoring only with defined performance figure or a text. It is the new approach of this research to describe them as a program to be the databases which analyzed them. The result of this research considers scoring of a referee as a correct answer. Thereby, evaluation of a system can be performed, and if the system performance is enough, it will be considered to be useful that it can use for evaluation of a referee etc.

The emergence of social media has provided vast amounts of information that is potentially valuable for emergency management. In the EU-FP7 Project Security Systems for Language and Image Analysis (Slandail), an image analysis system has been developed to recognize the flood water images from the social media resources by incorporating with text analysis. A novel image feature descriptor has been developed to facilitate fast image processing based on incorporation of the "Squiral" (Square-Spiral) Image Processing (SIP) framework with the "Speeded-up Robust Features" (SURF). A new approach is proposed to generate an index from image recognition outcomes based on a moving window average, which presents a temporal change based on the occurrence of flooding water identified by image analysis. The evaluation for computation time and recognition were based on a batch of images obtained from the US Federal Emergency Management Agency (FEMA) media library and Facebook corpus from Germany, and the outcomes show the advantages of the proposed image features. The simulation results demonstrate the concept of the index based on a moving window average, highlighting the potential for application in emergency management.

Identifying and quantifying cell death is the basis for all cell death research. Current methods for obtaining these quantitative measurements rely on established biomarkers, yet the marker-based approach suffers from limited marker specificity, high cost of reagents, lengthy sample preparation and fluorescence imaging. Based on the morphological difference, we developed a Live, Apoptotic, and Necrotic Cell Explorer (LANCE) to categorize cell death status in a label-free manner, by incorporating machine learning and image processing. The LANCE workflow includes cropping individual cells from microscopic images having hundreds of cells, formation of an image database of around 5,000 events, training and validation of the convolutional neural network models using multiple cell lines and treatment conditions. With LANCE, we precisely categorized live, apoptotic, and necrotic cells with a high accuracy of 96.3±0.5%. More importantly, the non-destructive label-free LANCE method allows for tracking time dynamics of the cell death process, which enhances the understanding of subtle cell death regulation at molecular level. Hence, LANCE is a fast, low-cost, and non-destructive label-free method to distinguish cell status, which can be applied to cell death studies as well as many other biomedical applications.