AUTOMATED CORONAL LOOP SEGMENTATION USING SNAKE-BASED ALGORITHM Woon Khang Tang A Thesis Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE August 2010 Committee: Dr. Jong Kwan Lee, Advisor Dr. Ray Kresman Dr. Hassan Rajaei ii ABSTRACT Dr. Jong Kwan Lee, Advisor In this thesis, an automated technique, G-snake, for segmenting the coronal loops from intensity images of the Sun's corona is studied. G-snake involves use of the active contour model (snake) based on the Gaussian-like coronal loop cross-sectional intensity profiles. It also utilizes the principal component analysis in the snake process. We study the effectiveness of G-snake using synthetic datasets as well as real coronal images. iii ACKNOWLEDGMENTS First and foremost I express my deepest appreciation to my advisor, Dr. Jong Kwan \Jake" Lee, who introduced me to the vast world of Computer Graphics-related areas. I would like to specially thank him for his guidance during my study at the Bowling Green State University. Without his encouragement and support, this thesis would not have been possible. I would also like to take this opportunity to thank Dr. Ray Kresman and Dr. Hassan Rajaei for their insightful comments. Last but not least, I would like to thank my parents and my friends for their support and love. iv Table of Contents CHAPTER 1: INTRODUCTION 1 1.1 Background . 1 1.2 Automated Coronal Loop Segmentation . 2 1.3 Organization . 4 CHAPTER 2: RELATED WORK 5 2.1 Oriented Connectivity Method (OCM) . 5 2.2 Dynamic Aperture-based Method (DAM) . 7 2.3 Unbiased Detection Method of Curvilinear Structures (UDM) . 8 2.4 Incremental Random Hough Transform (I-RHT) . 8 2.5 Supervised Neural Learning Strategies with Classifiers (SNLS) . 9 2.6 Oriented Coronal CUrved Loop Tracing (OCCULT) . 10 2.7 2D Wavelet Transform Modulus Maxima Method (WTMM) . 11 2.8 Proposed Work . 12 CHAPTER 3: NEW AUTOMATED CORONAL LOOP SEGMENTATION 13 3.1 Active Contour Model (Snake) . 13 3.1.1 Internal Energy . 14 3.1.2 External Energy . 14 3.1.3 Snake Energy Minimization . 16 3.2 New Approach: Gaussian-snake (G-snake) . 16 v 3.2.1 Internal Energy . 17 3.2.2 External Energy . 18 3.2.3 Energy Minimization of G-snake . 23 3.3 Preprocessing and Post-processing . 25 CHAPTER 4: EXPERIMENTAL RESULTS 27 4.1 Synthetic Dataset Experiment . 27 4.2 Real Corona Image Experiment . 40 CHAPTER 5: CONCLUSION AND FUTURE WORK 51 5.1 Contribution . 51 5.2 Future Work . 52 BIBLIOGRAPHY 54 vi List of Figures 1.1 Sample corona image . 3 3.1 Illustration of Gaussian curve fitting using 10 intensity values sampled from a (pre-processed) TRACE image. 20 3.2 Example of estimating a loop point's directionality . 21 3.3 Directionality image of coronal image shown in Figure 1.1 . 22 3.4 Example of G-snake's local minimization process for a point Pi in a 3 × 3 region 24 3.5 Preprocessing steps . 26 4.1 Synthetic datasets . 29 4.2 Results of G-snake overlaid (in orange) on the synthetic datasets . 34 4.3 Real corona images . 41 4.4 Results of G-snake overlaid (in orange) on the real images . 44 4.5 Manual segmentation overlaid (in red) on real images . 47 vii List of Tables 4.1 Global positional error (GPE) measures on synthetic datasets . 39 4.2 Global positional error (GPE) and false positive error measures on real corona images . 50 1 CHAPTER 1 INTRODUCTION Automated object and feature segmentation techniques have been used and studied widely in many applications. For example, some of the practical applications include fingerprint recognition [1, 2, 3], face recognition [4, 5, 6], medical imaging [7, 8, 9], etc. In this thesis, we focus on the automated segmentation of arching loop structures in images of the Sun's corona. In this chapter, we give an introduction to automated segmentation of coronal loops. We first describe some background about the Sun and the coronal loops. Then, we discuss the importance of studying the topic and discuss how automated segmentation techniques can be beneficial. In addition, we discuss the difficulties of automated coronal loop segmentation. 1.1 Background The Sun provides light and heat for life on Earth. The Sun's outer layers consist of three layers: the photosphere, the chromosphere, and the corona. The photosphere is the visible surface of the Sun. The temperature in the photosphere is about 4; 500 − 6; 000 ◦K. The chromosphere is a relatively thin layer above the photosphere. It extends 2,000 kilometers above the Sun's surface. The temperature in the chromosphere increases gradually with altitude, up to 20; 000 ◦K near the top. The corona is the outermost layer of the Sun. It 2 extends for several millions of kilometers above the Sun's surface. The average temperature of the corona is very hot, up to 2; 000; 000 ◦K. In the lower region of the corona, there are curvilinear structures. These structures are called the corona loop structures. The Transition Region and Coronal Explorer (TRACE) [11] is a NASA space mission that captures high resolution images of the Sun. The images from TRACE are a preferred source of solar coronal images, as TRACE collects high-resolution intensity images of up to 1024 by 1024 pixels from the Sun's corona many times per hour. Here, we note that we have used the TRACE images for our coronal loop segmentation. A sample corona image of TRACE is shown in Figure 1.1. The bright arching structures are the coronal loops. As seen in Figure 1.1, coronal loops appear as tube-like thin arc structures in TRACE images. Many solar scientists study the images taken from TRACE to analyze various phe- nomenon around the Sun; many of them focus on the Sun's magnetic field study as the Sun's magnetic field is the source of many solar activities that impact the near-Earth envi- ronment [12]. The coronal loop structures provide evidences of the solar magnetic field [10]. Thus, the automatic segmentation of these structures can aid solar scientists in studying and analyzing the Sun's solar magnetic field. Here, we note that there are also many other solar studies besides the automatic segmentation of the coronal loops. However, since those studies are not closely-related to our work, we do not discuss about them in this thesis. 1.2 Automated Coronal Loop Segmentation Recently, there has been an increasing importance in the study of automated coronal loop segmentation techniques in solar physics, largely due to the escalating growth of available solar imaging data. These data can be used in scientific analysis to understand the Sun's dynamic activities. Since the Sun's dynamic activities directly influence the Earth's atmo- sphere, the available solar image data are of high interest to the scientists. One example of solar activity impacting the Earth is the solar storms. Solar storms can cause disturbance in 3 Figure 1.1: Sample corona image near-Earth space weather, damage satellite sensors, and disrupt communications on Earth. Scientists have found that many solar phenomena are ultimately caused by the changes in the Sun's solar magnetic fields [12]. Solar scientists study the solar magnetic fields by ob- serving the arching coronal loop structures found in solar imagery since these structures are vestiges of the solar magnetic field [14]. By developing automated coronal loop segmentation techniques, large number of coronal image analysis can be realized and the segmentation re- sults can be compared in a consistent and effective way. Although manual methods can be employed, it is unrealistic as it is extremely laborious and error prone. Moreover, the manual results are most likely not self-consistent due to human bias. Automating the process of coronal loops segmentation is a challenging task. First, coronal images have a wide range of intensity distribution and different noise distribution across the image. Developing an automated segmentation technique that works on all different dynamic coronal images is very difficult. Second, the coronal loop structures can overlap each other. The overlapping of loops is caused by the projection of 3D structures on to 2D image. The segmentation of overlapped loops is difficult as the overlapped portions can exhibit different 4 pixel properties than those of the other loop pixels. Third, the boundaries of the coronal loop structures do not have distinct edges and are often blurry. In addition, some loops have relatively lower contrast in sub-segments of its loop and the loop structure shapes can also be very complex. Finally, besides the image noises, there are other non-loop features on the corona image. For instance, part of the corona image often contain bright structures called solar moss [15]. Solar moss looks like a sponge-like wide white spots in the image and is often bright. Distinguishing these solar moss from the corona loops is very difficult. 1.3 Organization The rest of this thesis is organized as follows. In Chapter 2, we discuss the related work. In Chapter 3, we discuss a new technique for automated coronal loop segmentation. In Chapter 4, we report the experimental results on synthetic and real images. Chapter 5 concludes this thesis and discusses future work. 5 CHAPTER 2 RELATED WORK There have been a large number of automated pattern recognition techniques for region-based features or curvi-linear features (e.g., automatic recognition of human faces and facial ex- pressions [13] and unbiased detection method of curvi-linear structures [19]). In this chapter, we summarize work done by others on automated coronal loop segmentation approaches.