Splitting and Merging for Active Contours: Plug-and-Play

This study tackles the challenge of splitting and merging in parametric active contours or snakes. The proposed method comprises three stages: (1) fully 4-connected interpolation, (2) snake splitting, and (3) snakes merging. For this purpose, first, the coordinates of snake points are separated into two corrupted 1D signals, with missing X/Y samples in the signals representing missing snakes’ coordinates. These missing X/Y samples are estimated using a constrained Tikhonov regularisation model, ensuring fully 4-connected snakes. Next, crossing points are identified by plotting snake points onto a raster matrix, detecting overlaps where multiple snake points occupy the same raster cell. Finally, snakes are split or merged by extracting snake points between crossing snake points that form a loop using a heuristic approach. Experimental results on the boundary detection of enamel in Micro-CT images and coronary arteries’ lumen in CT images demonstrate the proposed method’s ability to handle contour splitting and merging effectively.

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Publication Details

Journal
Mathematics
Published
2025-03-18
DOI
https://doi.org/10.3390/math13060991
Primary Topic
Computational Geometry and Mesh Generation
Type
article
Field-Weighted Citation Impact
0.00

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article

Splitting and Merging for Active Contours: Plug-and-Play

Mojtaba Lashgari, Hossein Rabbani, Abhirup Banerjee
Mathematics
Computational Geometry and Mesh Generation
article

Splitting and Merging for Active Contours: Plug-and-Play

Mojtaba Lashgari, Hossein Rabbani, Abhirup Banerjee
article en

Abstract

This study tackles the challenge of splitting and merging in parametric active contours or snakes. The proposed method comprises three stages: (1) fully 4-connected interpolation, (2) snake splitting, and (3) snakes merging. For this purpose, first, the coordinates of snake points are separated into two corrupted 1D signals, with missing X/Y samples in the signals representing missing snakes’ coordinates. These missing X/Y samples are estimated using a constrained Tikhonov regularisation model, ensuring fully 4-connected snakes. Next, crossing points are identified by plotting snake points onto a raster matrix, detecting overlaps where multiple snake points occupy the same raster cell. Finally, snakes are split or merged by extracting snake points between crossing snake points that form a loop using a heuristic approach. Experimental results on the boundary detection of enamel in Micro-CT images and coronary arteries’ lumen in CT images demonstrate the proposed method’s ability to handle contour splitting and merging effectively.

MathematicsVol. 13(6)
Isfahan University of Medical Sciences (IR), University of Oxford (GB)
Royal Society
Openalex Percentile: Top 91%
Computational Geometry and Mesh Generation
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