Diabetic retinopathy (DR) is the most common complications of diabetes; if untreated the DR can lead to a vision loss. The treatment options for DR are limited and the development of newer therapies are of considerable interest. Drug screening for the retinopathy treatment is undertaken using animal models in which the quantification of acellular capillaries (capillary without any cells) is used as a marker to assess the severity of retinopathy and the treatment response. The traditional approach to quantitate acellular capillaries is through manual counting. The purpose of this investigation was to develop an automated technique for the quantitation of acellular capillaries using computer-based image processing algorithms. We developed a custom procedure using the Python, the medial axis transform (MAT) and the connected component algorithm. The program was tested on the retinas of wild-type and diabetic mice and the results were compared to single blind manual counts by two independent investigators. The program successfully identified and enumerated acellular capillaries. The acellular capillary counts were comparable to the traditional manual counting. In conclusion, we developed an automated computer-based program, which can be effectively used for future pharmacological development of treatments for DR. This algorithm will enhance consistency in retinopathy assessment and reduce the time for analysis, thus, contributing substantially towards the development of future pharmacological agents for the treatment of DR.
Three dimensional microscopy visualization has the potential of playing a significant role in the study of 3D cellular structures in biomedical research. Such potential, however, has not been fully realized due to the difficulties of current visualization methods in coping with the unique nature of microscopy image volumes, such as low image contrast, noise and unknown transfer functions. In this paper, we present a new 3D microscopy imaging approach that integrates volume visualization and 3D image processing techniques for interactive 3D data exploration and analysis. By embedding 3D image enhancement procedures into the volume visualization pipeline, we are able to automatically generate image- dependent transfer functions to reveal subtle features that are otherwise difficult to visualize. It allows the users to interactively manipulate a small number of parameters to achieve desired visualization effects. Other 3D image processing techniques, such as quantification and segmentation, may also be integrated within the data exploration process for interactive image analysis.
This paper describes the major components of the grasp augmented vision system. Grasp is an object-oriented system written in C++, which provides an environment both for exploring the basic technologies of augmented vision, and for developing applications that demonstrate the capabilities of these technologies. The hardware components of grasp include video cameras, 6-D tracking devices, a frame grabber, a 3-D graphics workstation, a scan converter, and a video mixer. The major software components consist of classes that implement geometric models, rendering algorithms, calibration methods, file I/O, a user interface, and event handling.
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