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      Measurement of functional microcirculatory geometry and velocity distributions using automated image analysis

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          Abstract

          This study describes a new method for analyzing microcirculatory videos. It introduces algorithms for quantitative assessment of vessel length, diameter, the functional microcirculatory density distribution and red blood-cell (RBC) velocity in individual vessels as well as its distribution. The technique was validated and compared to commercial software. The method was applied to the sublingual microcirculation in a healthy volunteer and in a patient during cardiac surgery. Analysis time was reduced from hours to minutes compared to previous methods requiring manual vessel identification. Vessel diameter was detected with high accuracy ( >80%, d > 3 pixels). Capillary length was estimated within 5 pixels accuracy. Velocity estimation was very accurate (>95%) in the range [2.5, 1,000] pixels/s. RBC velocity was reduced by 70% during the first 10 s of cardiac luxation. The present method has been shown to be fast and accurate and provides increased insight into the functional properties of the microcirculation.

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          Most cited references32

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          Ridge-based vessel segmentation in color images of the retina.

          A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kappaNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. The results show that our method is significantly better than the two rule-based methods (p < 0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.
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            Comparing methods of measurement: why plotting difference against standard method is misleading.

            When comparing a new method of measurement with a standard method, one of the things we want to know is whether the difference between the measurements by the two methods is related to the magnitude of the measurement. A plot of the difference against the standard measurement is sometimes suggested, but this will always appear to show a relation between difference and magnitude when there is none. A plot of the difference against the average of the standard and new measurements is unlikely to mislead in this way. We show this theoretically and by a practical example.
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              An unbiased detector of curvilinear structures

              J Steger (1998)
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                Author and article information

                Contributors
                j.g.dobbe@amc.uva.nl
                Journal
                Med Biol Eng Comput
                Medical & Biological Engineering & Computing
                Springer-Verlag (Berlin/Heidelberg )
                0140-0118
                1741-0444
                22 April 2008
                July 2008
                : 46
                : 7
                : 659-670
                Affiliations
                [1 ]Department of Physiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
                [2 ]Department of Medical Physics, Academic Medical Center, University of Amsterdam, Room no. L0-113-3, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
                [3 ]Department of Ophthalmology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
                Article
                349
                10.1007/s11517-008-0349-4
                2441502
                18427850
                bf16b409-6787-4591-95fb-f32844a86575
                © The Author(s) 2008
                History
                : 4 October 2007
                : 4 April 2008
                Categories
                Original Article
                Custom metadata
                © International Federation for Medical and Biological Engineering 2008

                Biomedical engineering
                orthogonal polarized spectral (ops) imaging,vessel density,side-stream dark field (sdf) imaging,blood velocity,space–time diagram

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