Accelerated Algorithms for Growing Segments from Image Regions

New algorithms for merging superpixel regions into segments are proposed. The main idea of merging superpixels is as follows. Firstly, a strategy is used in which a segment is grown from neighboring regions while the conditions for merging are met, and, secondly, when merging regions, the applied information quality measure should not increase. Three algorithms based on the specified strategy are proposed, which differ in the conditions for making a decision on merging superpixels. A computational experiment is carried out on test images. The experiment showed that the proposed algorithms accelerate the segmentation process compared to the procedure used earlier with acceptable losses of information quality measures of the resulting partitions.

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Publication Details

Journal
Computational Mathematics and Mathematical Physics
Published
2024-11-01
DOI
https://doi.org/10.1134/s0965542524701495
Citations
1
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
0.25
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article

Accelerated Algorithms for Growing Segments from Image Regions

Dmitry Murashov
1 citations
Computational Mathematics and Mathematical Physics
Medical Image Segmentation Techniques
0.25
article

Accelerated Algorithms for Growing Segments from Image Regions

Dmitry Murashov
article en
1 citations

Abstract

New algorithms for merging superpixel regions into segments are proposed. The main idea of merging superpixels is as follows. Firstly, a strategy is used in which a segment is grown from neighboring regions while the conditions for merging are met, and, secondly, when merging regions, the applied information quality measure should not increase. Three algorithms based on the specified strategy are proposed, which differ in the conditions for making a decision on merging superpixels. A computational experiment is carried out on test images. The experiment showed that the proposed algorithms accelerate the segmentation process compared to the procedure used earlier with acceptable losses of information quality measures of the resulting partitions.

Computational Mathematics and Mathematical PhysicsVol. 64(11)
Russian Academy of Sciences (RU)
Openalex Percentile: Top 43%
Medical Image Segmentation Techniques
0.25
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