10 research outputs found

    False negative classification rate.

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    <p>FNR of microvessel classification for synthetic data as a function of image entropy. Average FNR = 7.49.</p

    Example result given by the segmentation algorithm.

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    <p>Shown is an IHC image plane within the bregma stained for GLUT1 expression in vascular structures. Green contours identify microvessels exhibiting significant GLUT1 concentration.</p

    GLUT1 stained image examples.

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    <p>(top) Synthetic and (bottom) non-synthetic images with varying global pixel entropy (<i>H</i>) Local spatial frequency tends to increase with local entropy. IHC images with higher <i>H</i> usually exhibit more spatially complex surface geometries and/or possess increased surface noise due to the staining protocol.</p

    Real parts of the Gabor filter bank.

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    <p>Generated for different combinations of <i>θ</i> (in radians) and <i>f</i> (in Hz) with , and <i>ϕ</i> = 0.</p

    Overview of the workflow.

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    <p>Input immunohistochemistry (IHC) images are pre-segmented to identify candidate structures of interest, which are represented within a generated mask image. Candidate structures within the mask image are filtered using a decision tree derived from training sessions to produce a fully segmented IHC image. For further details, see text.</p

    Pixel-wise classification of the hippocampal region using k-means clustering.

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    <p>White pixels mark regions potentially containing vascular structures of interest. Black pixels mark non-vascular structures.</p

    Image Entropy Range and False Positive Rate of microvessel classification.

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    <p>(top) Boxplot showing the median, standard deviation, and range of entropy values for both synthetic and non-synthetic datasets. (bottom) False positive rate (FPR) of microvessel classification for synthetic and non-synthetic IHC images as a function of image entropy.</p

    Processing time.

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    <p>The red line marks 5 megapixels, which correlates to the 2592×1944 brightfield IHC images acquired.</p
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