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Automated Midline Shift Detection on Brain Ct Images for Computer-Aided Clinical Decision Support Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2013 AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT Xuguang Qi Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Computer Sciences Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/504 This Dissertation is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT © Xuguang Qi 2013 All Rights Reserved AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT A research dissertation submitted in partial fulfillment of the requirements for the degree of Dorctor of Philosophy at Virginia Commonwealth University by XUGUANG QI Doctor of Philosophy in Computer Science School of Engineering Director: Assoc. Prof. Kayvan Najarian Department of Computer Science Virginia Commonwealth University Richmond, VA May 2013 2 Acknowledgement I deeply appreciate the people who have been supporting and helping me in different ways throughout the entire process of my doctoral studies as well as completing this dissertation. First of all, I would like to express my deepest gratitude to my academic advisor, Dr. Kayvan Najarian, for his continuous support and guidance on this program. During these years in my program, he continuously shared his insights and helped me with his ideas. I also appreciate the help and support from Sharad Shandilya, Dr. Wenan Chen, and Dr. Ashwin Belle; in particular for many useful research discussions I had with them. Special thanks to Dr. Yang Tang M.D. for professional clinical suggestions and his clinical interpretation of images. I would also like to acknowledge Dr. Rosalyn S Hobson, Dr. Meng Yu, Dr. Alen Docef, Dr. Wanyu Zang, and M.D. Kevin Ward for their helpful advice and support. I am indebted to Dr. Marcel P Jackowski in University of São Paulo for providing very helpful suggestions and advice. In addition I would like to thank Sardar Ansari, Dr. Yurong Luo, Dr. Jie Wu, Leila Ghaedi, Mahsa Zahery, Michael 3 Pfaffenberger, Machel Roberts and Eric Myer and all other friends and member of Biomedical Signal and Image Processing Lab. The generous assistance from them and the pleasant cooperation with them was one of the most important reasons for enabling me to complete this work. Most importantly, I would like to offer my special thanks to my parents for their endless love and concern. Most of all, I would especially thank my dear wife, Dr. Hongrui Liu, for her everlasting love and encouragement as well as the most reliable support. This work would not have been possible without her. 4 Table of Contents Acknowledgement ........................................................................................................... 3 List of Tables .................................................................................................................. 8 List of Figures ................................................................................................................. 9 Abstract ......................................................................................................................... 13 Novelty and Contribution .............................................................................................. 15 Chapter 1: Introduction .................................................................................................. 19 1.1 Motivation and background.............................................................................. 19 1.1.1 Brain midline shift and its medical applications ..................................... 19 1.1.2 Computed Tomography (CT) technique ................................................ 21 1.1.3 Ventricle system in the human brain ...................................................... 23 1.1.4 Automated computer aided clinical decision support systems ................ 25 1.1.5 Medical image segmentation ................................................................. 26 1.2 System validation methods............................................................................... 30 1.3 Proposed method ............................................................................................. 32 Chapter 2 CT Slice Selection Algorithm ........................................................................ 34 2.1 Background and introduction ........................................................................... 34 2.2 Methodology.................................................................................................... 35 2.2.1 Skull detection ...................................................................................... 37 5 2.2.2 Closed skull inspection .......................................................................... 41 2.2.3 Intracranial area detection...................................................................... 43 2.2.4 Convexity inspection ............................................................................. 45 2.2.5 Ventricle visibility inspection ................................................................ 47 2.3 Result and discussion ....................................................................................... 51 2.3.1. Data ..................................................................................................... 51 2.3.2. Results and discussion .......................................................................... 51 2.4 Summary ......................................................................................................... 56 Chapter 3 Ideal Midline Detection ................................................................................. 57 3.1 Background and introduction ........................................................................... 57 3.2 Methodology.................................................................................................... 58 3.2.1 Approximate IML detection .................................................................. 59 3.2.2 Refined ideal midline detection ............................................................. 61 3.3 Results and discussion ..................................................................................... 64 3.3.1 Data ...................................................................................................... 64 3.3.2 Results and discussion ........................................................................... 64 3.4 Summary ......................................................................................................... 69 Chapter 4 Actual midline detection and midline shift estimation .................................... 70 4.1 Background and introduction ........................................................................... 70 4.2 Methodology.................................................................................................... 72 4.2.1 Window Selection Algorithm ................................................................ 74 4.2.2 Weighted Median Filter ......................................................................... 76 4.2.3 Ventricle segmentation based on level set method ................................. 77 6 4.2.4 Ventricle identification and actual midline estimation............................ 81 4.3 Results and Discussion..................................................................................... 83 4.3.1 Data ...................................................................................................... 83 4.3.2 Results and discussion ........................................................................... 83 4 .4 Summary ........................................................................................................ 92 Chapter 5 Application: Intracranial Pressure Prediction ................................................. 93 5.1 Background and introduction ........................................................................... 93 5.2 Methodology.................................................................................................... 93 5.2.1 Candidate features ................................................................................. 93 5.3 Results and discussion ................................................................................... 101 5.3.1 Data .................................................................................................... 101 5.3.2 Results and Validation ......................................................................... 101 5.3.3 Discussion ........................................................................................... 103 5.4 Summary ....................................................................................................... 104 Chapter 6: Summary .................................................................................................... 105 References ................................................................................................................... 108 7 List of Tables Table 3.1 Comparison on the accuracy of IML estimation ..................................... 67 Table 3.2 Comparison on the mean
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