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image thinning using matlab function bwmorph  (MathWorks Inc)


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    Structured Review

    MathWorks Inc image thinning using matlab function bwmorph
    Ground truth edge image generation. From an input image (first row) with ground truth semantic segmentation (second row), we identify all boundary pixels (third row) and then apply image thinning (e.g. MATLAB’s <t>bwmorph)</t> to obtain the ground truth edge image (last row).
    Image Thinning Using Matlab Function Bwmorph, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/image+thinning+using+matlab+function+bwmorph/pmc07295132-355-25-25
    Average 90 stars, based on 1 article reviews
    image thinning using matlab function bwmorph - by Bioz Stars, 2026-11
    90/100 stars

    Images

    1) Product Images from "REDN: A Recursive Encoder-Decoder Network for Edge Detection"

    Article Title: REDN: A Recursive Encoder-Decoder Network for Edge Detection

    Journal: IEEE access : practical innovations, open solutions

    doi: 10.1109/access.2020.2994160

    Ground truth edge image generation. From an input image (first row) with ground truth semantic segmentation (second row), we identify all boundary pixels (third row) and then apply image thinning (e.g. MATLAB’s bwmorph) to obtain the ground truth edge image (last row).
    Figure Legend Snippet: Ground truth edge image generation. From an input image (first row) with ground truth semantic segmentation (second row), we identify all boundary pixels (third row) and then apply image thinning (e.g. MATLAB’s bwmorph) to obtain the ground truth edge image (last row).

    Techniques Used:

    Related Articles

    other:

    Article Title: A microscopy-based approach for determining growth probability and lag time of individual bacterial cells.
    Article Snippet: The development of relevant predictive models for single-cell lag time and growth probability near growth limits is of critical importance for predicting pathogen behavior in foods.. The classical methods for data acquisition in this field are based on turbidity measurements of culture media in microplate wells inoculated with approximately one bacterial cell per well.. Yet, these methods are labour intensive and would benefit from higher throughput.

    Article Title: Automatic extraction of actin networks in plants
    Article Snippet: To avoid connected branches having the same label, we first temporarily remove the branch points using the bwmorph MATLAB function.

    Article Title: Improving skeleton algorithm for helping Caenorhabditis elegans trackers
    Article Snippet: We decided to use the Matlab bwmorph function as an example of the classic image skeletonisation method.

    Article Title: Improving skeleton algorithm for helping Caenorhabditis elegans trackers
    Article Snippet: For the analysis, the above-described IoU index1 and 2 were used to compare the manually obtained reconstructed bodies of skeletons (reference), the skeletons obtained with the new method (new Skel) and the skeletons obtained using Matlab function bwmorph (classic Skel).

    Article Title: Tensional twist-folding of sheets into multilayered scrolled yarns
    Article Snippet: The image of a crosssection is first thresholded to obtain a binary image composed of 0s and 1s, and, then, processed by a skeletonized morphological operation using the Matlab bwmorph function.

    Article Title: A biologically inspired approach for recovering the trajectory of off-line handwriting
    Article Snippet: In order to assess to which extent the loss of performance depends on the trajectory recovery step, we designed a second scenario (ideal skeleton), where we bypassed the preprocessing module and compute the image of the skeleton by connecting the on-line coordinates with a digital line, on which we applied the bwmorph Matlab© function for finding the connected components, which were passed to the unfolding-segmentation loop module.

    Article Title: Optical Coherence Tomography Angiography–Derived Flux As a Measure of Physiological Changes in Retinal Capillary Blood Flow
    Article Snippet: VSD was calculated by symmetrically reducing the binary image to a 1-pixel-wide skeletonized representation of each vessel ( C), using the “skel” operation of the MATLAB bwmorph function.

    Construct:

    Article Title: Propulsive cell entry diverts pathogens from immune degradation by remodeling the phagocytic synapse
    Article Snippet: After setting negative pixel values to 0, rough Toxoplasma boundaries were constructed using the built-in MATLAB edge detection function with the Laplacian of Gaussian based zero-crossing algorithm. .. Morphological closing was then applied to binary images of constructed edges using the bwmorph MATLAB function to patch any noncontinuous edge sections. .. Edges corresponding to distinct Toxoplasma were then grouped using the bwboundaries MATLAB function, followed by user defined edge thickness padding to control final ROI mask sizes.



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    MathWorks Inc image thinning using matlab function bwmorph
    Ground truth edge image generation. From an input image (first row) with ground truth semantic segmentation (second row), we identify all boundary pixels (third row) and then apply image thinning (e.g. MATLAB’s <t>bwmorph)</t> to obtain the ground truth edge image (last row).
    Image Thinning Using Matlab Function Bwmorph, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/image+thinning+using+matlab+function+bwmorph/pmc07295132-355-25-25
    Average 90 stars, based on 1 article reviews
    image thinning using matlab function bwmorph - by Bioz Stars, 2026-11
    90/100 stars
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    Ground truth edge image generation. From an input image (first row) with ground truth semantic segmentation (second row), we identify all boundary pixels (third row) and then apply image thinning (e.g. MATLAB’s bwmorph) to obtain the ground truth edge image (last row).

    Journal: IEEE access : practical innovations, open solutions

    Article Title: REDN: A Recursive Encoder-Decoder Network for Edge Detection

    doi: 10.1109/access.2020.2994160

    Figure Lengend Snippet: Ground truth edge image generation. From an input image (first row) with ground truth semantic segmentation (second row), we identify all boundary pixels (third row) and then apply image thinning (e.g. MATLAB’s bwmorph) to obtain the ground truth edge image (last row).

    Article Snippet: To obtain the ground truth edges, we first identify all the boundary pixels, treat them as a binary image and then apply image thinning using MATLAB function bwmorph (see examples in ). fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Fig. 3: caption a7 Ground truth edge image generation.

    Techniques: