Segmentation of Coronal Holes Using Active Contours Without Edges

Abstract: An application of active contours without edges is presented as an efficient and effective means of extracting and characterizing coronal holes. Coronal holes are regions of low-density plasma on the Sun with open magnetic field lines. As the source of the fast solar wind, the detection and characterization of these regions is important for both testing theories of their formation and evolution and from a space weather perspective. Coronal holes are detected in full disk extreme ultraviolet (EUV) images of the corona obtained with the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA). The proposed method detects coronal boundaries without determining any fixed intensity value in the data. Instead, the active contour segmentation employs an energy-minimization in which coronal holes are assumed to have more homogeneous intensities than surrounding active regions and quiet Sun. The segmented coronal holes tend to correspond to unipolar magnetic regions, are consistent with concurrent solar wind observations, and qualitatively match the coronal holes segmented by other methods. The means to identify a coronal hole without specification of a final intensity threshold may allow this algorithm to be more robust across multiple datasets, regardless of data type, resolution, and quality.

• The paper introduces active contours without edges for segmenting coronal holes, eliminating reliance on fixed intensity thresholds for solar image analysis.
• The methodology leverages energy minimization to balance contour length and intensity homogeneity, validated using magnetic unipolarity and solar wind correlations.
• The approach shows promising alignment with established methods like SPoCA, highlighting potential improvements for space weather forecasting.

Segmentation of Coronal Holes Using Active Contours Without Edges

Introduction

The paper explores an innovative approach to segmenting coronal holes (CHs) on the Sun's surface using active contours without edges (ACWE), which departs from traditional threshold-based methods. Coronal holes, characterized by regions of low-density plasma with open magnetic field lines, play a crucial role in shaping the solar wind and influencing space weather. The presented method leverages the concept of energy minimization to achieve segmentation, assuming that CHs exhibit more homogeneous intensities compared to adjacent solar regions.

Methodology

The ACWE approach models segmentation as an energy minimization problem where the energy functional focuses on balancing several terms: the contour length, and the homogeneity of intensity inside and outside the contour. Distinctively, this method does not rely on a fixed intensity threshold, which allows for greater adaptability across varying datasets. Instead, an initial contour is seeded through low-intensity thresholding, which is then evolved iteratively using the ACWE algorithm.

The paper details the process of setting initial parameters for ACWE and overcoming challenges such as off-disk pixel inclusion by setting these to a mean intensity value. The procedure uses MATLAB's activecontour function, with specific attention to parameter choices ($\lambda_i/\lambda_o$ ratio indicating the weighting of homogeneity terms) and initialization constants.

Results and Validation

The proposed segmentation method is validated through multiple avenues:

1. Magnetic Unipolarity: Analyzing the skewness of the magnetic flux underlying the segmented CHs confirmed that most low-latitude CHs exhibit significant unipolarity, consistent with expectations of CHs correlating with reduced EUV intensity and open magnetic field lines.
2. Comparison with Solar Wind: The segmentation was cross-verified against high-speed solar wind measurements from ACE satellite data, showing temporal correlation between segmented CHs and elevated solar wind speeds observed at Earth a couple of days later.
3. Qualitative Comparison with SPoCA: The ACWE method's segmentation outcomes were compared visually against the Spatial Possibilistic Clustering Algorithm (SPoCA) results, showing good agreement in capturing CH morphology.

Computational Considerations

The paper highlights computational aspects, advocating for an efficient MATLAB implementation while estimating potential scaling challenges with higher resolution images. The method showed promising speed when downsampling images, although real-time processing remains aspirational without further optimization.

Implications and Future Work

The study suggests that ACWE can complement threshold-based methods, potentially yielding richer insights into CH properties and dynamics by avoiding fixed threshold limitations. The research opens prospects for further exploration into integrating magnetic field information directly into the segmentation energy functional.

Calls for comprehensive comparisons with other segmentation strategies and exploration of CH characteristics in connection with solar wind parameters are underscored. Such endeavors could foster deeper understanding of solar-terrestrial interactions and enhance space weather forecasting models.

Conclusion

The paper contributes a novel perspective on CH segmentation in solar imaging, offering a robust alternative to threshold-dependent frameworks. The demonstrated adaptability and qualitative alignment with existing methods underscore the potential for broader applications and further refinement in the study of solar phenomena. Future efforts are encouraged to converge on unified testing and validation frameworks to enhance cross-methodological insights within the scientific community.