1,052 research outputs found
Data compression techniques applied to high resolution high frame rate video technology
An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended
A Rule Based Segmentation Approaches to Extract Retinal Blood Vessels in Fundus Image
The physiological structures of the retinal blood vessel are one of the key features that visible in the retinal images and contain the information associate with the anatomical abnormalities. It is accepted all over the world to judge the cardiovascular and retinal disease. To avoid the risk of visual impairment, appropriate vessel segmentation is mandatory. Here has proposed a segmentation algorithm that efficiently extracts the blood vessels from the retinal fundus image. The proposed segmentation algorithm is performed Lab and Principle Component (PC) based gray level conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations, Local Property-Based Pixel Correction (LPBPC). For appropriate detection proposed vessels correction algorithm LPBPC that check the feature of the vessels and remove the wrong vessel detection. To measure the appropriateness of the proposed algorithm, the experimental results are compared with the corresponding ground truth images. The experimental results have shown that the proposed blood vessel algorithm is more accurate than the existing algorithms
Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images
Cataloged from PDF version of article.More rapid and accurate high-throughput screening
in molecular cellular biology research has become possible with
the development of automated microscopy imaging, for which
cell nucleus segmentation commonly constitutes the core step. Although
several promising methods exist for segmenting the nuclei
of monolayer isolated and less-confluent cells, it still remains an
open problem to segment the nuclei of more-confluent cells, which
tend to grow in overlayers. To address this problem, we propose a
new model-based nucleus segmentation algorithm. This algorithm
models how a human locates a nucleus by identifying the nucleus
boundaries and piecing them together. In this algorithm, we
define four types of primitives to represent nucleus boundaries
at different orientations and construct an attributed relational
graph on the primitives to represent their spatial relations. Then,
we reduce the nucleus identification problem to finding predefined
structural patterns in the constructed graph and also use the
primitives in region growing to delineate the nucleus borders.
Working with fluorescence microscopy images, our experiments
demonstrate that the proposed algorithm identifies nuclei better
than previous nucleus segmentation algorithms
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