AccessScope project: Accessible light microscope for users with upper limb mobility or visual impairments.

PURPOSE: A web-based application was developed to remotely view slide specimens and control all functions of a research-level light microscopy workstation, called AccessScope. Students and scientists with upper limb mobility and visual impairments are often unable to use a light microscope by themselves and must depend on others in its operation. METHOD: Users with upper limb mobility impairments and low vision were recruited to assist in the design process of the AccessScope personal computer (PC) user interface. Participants with these disabilities were evaluated in their ability to use AccessScope to perform microscopical tasks. AccessScope usage was compared with inspecting prescanned slide images by grading participants' identification and understanding of histological features and knowledge of microscope operation. RESULTS: With AccessScope subjects were able to independently perform common light microscopy functions through an Internet browser by employing different PC pointing devices or accessibility software according to individual abilities. Subjects answered more histology and microscope usage questions correctly after first participating in an AccessScope test session. CONCLUSIONS: AccessScope allowed users with upper limb or visual impairments to successfully perform light microscopy without assistance. This unprecedented capability is crucial for students and scientists with disabilities to perform laboratory coursework or microscope-based research and pursue science, technology, engineering, and mathematics fields.

XML-based data model and architecture for a knowledge-based grid-enabled problem-solving environment for high-throughput biological imaging.

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

Knowledge representation and data mining for biological imaging.

Biological and pharmaceutical research relies heavily on microscopically imaging cell populations for understanding their structure and function. Much work has been done on automated analysis of biological images, but image analysis tools are generally focused only on extracting quantitative information for validating a particular hypothesis. Images contain much more information than is normally required for testing individual hypotheses. The lack of symbolic knowledge representation schemes for representing semantic image information and the absence of knowledge mining tools are the biggest obstacles in utilizing the full information content of these images. In this paper we first present a graph-based scheme for integrated representation of semantic biological knowledge contained in cellular images acquired in spatial, spectral, and temporal dimensions. We then present a spatio-temporal knowledge mining framework for extracting non-trivial and previously unknown association rules from image data sets. This mechanism can change the role of biological imaging from a tool used to validate hypotheses to one used for automatically generating new hypotheses. Results for an apoptosis screen are also presented.