Expert Systems for Image Processing, Analysis, and Recognition: Declarative Knowledge Representation for Computer Vision

There has been a significant increase in commercial products using imaging analysis techniques to solve real-world problems in diverse fields such as manufacturing, medical imaging, document analysis, transportation and public security, etc. This has been accelerated by various factors: more advanced algorithms, the availability of cheaper sensors, and faster processors. While algorithms continue to improve in performance, a major stumbling block in translating improvements in algorithms to faster deployment of image analysis systems is the lack of characterization of limits of algorithms and how they affect total system performance. The research community has realized the need for performance analysis and there have been significant efforts in the last few years to remedy the situation. Our efforts at SCR have been on statistical modeling and characterization of modules and systems. The emphasis is on both white-box and black box methodologies to evaluate and optimize vision systems. In the first part of this paper we review the literature on performance characterization and then provide an overview of the status of research in performance characterization of image and video understanding systems. The second part of the paper is on performance evaluation of medical image segmentation algorithms. Finally, we highlight some research issues in performance analysis in medical imaging systems.

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

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.

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.

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.

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.

This review looks at a variety of different ways in which computers can be used to assist in the interpretation of radiological images and in radiological decision-making. The issues involved in the design of computerized decision aids are introduced and four criteria proposed for evaluating such aids: need, practicality, veracity and relevance. These criteria are used to assess research into decision aids based on: image databases, numerical methods, expert systems, image processing and image understanding systems. Possible directions for research leading to aids of practical value are discussed in the conclusion.

and medicine is one of the most important study tools, as investigations of many current questions in a wide range of scientific directions, including physiology, use images as the initial data form. The first stage in the establishment of the Image Processing Center at the Pavlov Institute of Physiology of the Russian Academy of Sciences in 1996 has been discussed previously [2]. The center was developed as part of Project No. 225 (April 13, 2001) under the auspices of the Federal “State Support for the Integration of Higher Education and Basic Science” program (3.2, “Creation of collective industrial-academic centers with state-of-the-art equipment”). The center became the “Collective Biological and Medical Image Processing Center.” The main user was the Institute of Physiology, while others included St. Petersburg State University and St. Petersburg State Medical University. Financial support for the center under the heading “Development and introduction of information systems for biological image processing and analysis” was continued in 2002 in the framework of the “Mechanisms of adaptive behavior: intracellular signal transduction and gene expression” program of the Presidium of the Russian Academy of Sciences. Expansion of the computerized resources of the center provided biological and medical science research institutions and companies with the ability to process images, as well as facilities for training students and involving them in investigations in the framework of the integration of academic and industrial science. Scientific research programs involving students, post-graduate scientists, and post-doctoral scientists were developed using state-of-the-art computerized image processing methods. The main aim of the present article is to inform colleagues working in different areas of physiology and obtaining experimental data as images of the new capabilities of the center, particularly specialized information systems (SIS) which have been developed and to invite them to take part in collaborative studies. New video-based computer systems are continually being created at the center for processing images in different laboratories of the institute, and new specialized SIS have been developed. These support image processing, formation of databases in subject areas, and the export of results into statistical analysis programs. The facilities at the center support the processing of static and dynamic images of any physiological and biological nature. They provide for the analysis of images reflecting the structural-functional organization of neural networks in the brain, the structural-functional organization of autonomic processes, the neurogenic mechanisms of activity of the internal organs, and the genetic mechanisms of organisms. Images reflecting the mechanisms of sensory and cognitive processes are analyzed, as are those related to the psychophysical image recognition patterns, the mechanisms of neuroendocrine regulation, and the formation of pathological CNS states. Our approach in creating SIS for image processing consists of using the physiological principles of the organiNeuroscience and Behavioral Physiology, Vol. 41, No. 7, September, 2011

Somatic embryogenesis is among the most promising means of large scale plant micropropagation. The development of somatic embryos is characterized by their morphological changes. Embryos in culture usually exhibit high heterogeneity and abnormality. As conventional microscopic observation is laborious and subjective, an objective and quantitative morphokinetic description is important for further advancement of this important process technology. We developed an image analysis system capable of measuring morphological and size features of embryos. Subtle environmental effects on embryo development, which are often masked by the subjectivity of microscopic observation, are now discernible by statistically comparing the distributions of these morphological and size features. This image analysis and pattern recognition system was applied to examine the kinetics of a fed-batch culture.

We have developed and introduced a digital multi-modality image infrastructure in a multi-vender networked environment. This cardiology PACS (THOPACS) provides facilities for network communication between all DICOM-image generating systems in the Thoraxcentre. It facilitates hospital-wide access to all cardiology images and analysis results. THOPACS is integrated in the medical workstation of our hospital offering simultaneous presentation of images and related reports along with test results. This paper focuses mainly on the solutions for handling huge datasets, the archiving strategy, the automatic uploading of images from other hospitals, workflow support and the interoperability with image analysis systems.

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.

Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.

Decaying confidence functions for aging knowledge in expert systems

Light microscope analysis of cytological smears of suspicious lesions of the oral cavity is used as a method for detecting early cancer in the oral cavity. The sensitivity of this approach can be improved by quantitative analysis of the cells in the cytological smears. We have compared the efficiency of planimetry and the Vids V system of image analysis, as quantitative methods for discriminating between normal and abnormal cells in cytological smears of suspicious lesions in the oral cavity. Both methods detected an increase in nuclear area and a decrease in cytoplasmic area in abnormal epithelial cells from dysplastic lesions of increasing severity. However, image analysis was better able to discriminate between benign and malignant cells on the basis of nuclear size. Thus the Vids V system of image analysis is more appropriate than planimetry for quantitative analysis of cytological smears from the oral cavity.