AI Article Synopsis

  • The paper introduces an Interval Iteration Multilevel Thresholding (IIMT) method that enhances image segmentation by focusing on smaller sub-regions instead of the entire image.
  • This new framework specifically applies IIMT to brain MRI images, starting by decomposing the original image into a base layer.
  • Finally, the method combines segmentation results from both the original and base layer to improve accuracy, demonstrating superior performance compared to traditional Otsu-based and other optimization methods.

Article Abstract

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid - layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623348PMC
http://dx.doi.org/10.3390/e23111429DOI Listing

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