Abstract
We developed an automated pattern recognition code that is particularly well suited to extract one-dimensional curvilinear features from two-dimensional digital images. A former version of this Oriented Coronal Curved Loop Tracing (OCCULT) code was applied to spacecraft images of magnetic loops in the solar corona, recorded with the NASA spacecraft, Transition Region And Coronal Explorer (TRACE), in extreme ultra-violet wavelengths. Here, we apply an advanced version of this code (OCCULT-2), also, to similar images from the Solar Dynamics Observatory (SDO), to chromospheric H-α images obtained with the Swedish Solar Telescope (SST) and to microscopy images of microtubule filaments in live cells in biophysics. We provide a full analytical description of the code, optimize the control parameters and compare the automated tracing with visual/manual methods. The traced structures differ by up to 16 orders of magnitude in size, which demonstrates the universality of the tracing algorithm.
Highlights
Image segmentation is an image processing method that subdivides an image into its constituent regions or objects, which can have the one-dimensional geometry of curvilinear (1D) segments or the two-dimensional (2D) geometry of areas
This code was applied to a Transition Region And Coronal Explorer (TRACE) image, and a total of 57 coronal loops were detected in a solar active region, which supposedly outline the dipolar magnetic field
The efficiency and accuracy of automated curvilinear tracing can be controlled by a number of tuning or control parameters
Summary
Image segmentation is an image processing method that subdivides an image into its constituent regions or objects, which can have the one-dimensional geometry of curvilinear (1D) segments or the two-dimensional (2D) geometry of (fractal) areas. Since there exists no omni-potent automated pattern recognition code that works for all types of images well, we have to customize suitable algorithms for each data type individually by taking advantage of the particular geometry of the features of interest, using a priori information from the data. We optimize an automated pattern recognition code to extract magnetized loops from images of the solar corona with the aim of optimum completeness and fidelity. We will demonstrate that the same code works well for microscopic images in biophysics. The particular geometric property of the extracted features is the relatively large curvature radius of coronal magnetic field lines, which generally do not have sharp kinks and corners, but exhibit continuity in the variation of the local curvature radius along their length. The content of this paper includes a brief description of the automated tracing code (Section 2), applications to images in solar physics (Section 3), to images in biophysics (Section 4), discussion and conclusions (Section 5) and a full analytical description of the code in Appendix A
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