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Automated Machine Learning Based Analysis Of AUTOMATED MACHINE LEARNING BASED ANALYSIS OF INTRAVASCULAR OPTICAL COHERENCE TOMOGRAPHY IMAGES by RONNY SHALEV Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Adviser: Dr. David L. Wilson Department of Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY May, 2016 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the dissertation of Ronny Y. Shalev Candidate for the degree of Ph.D*. Committee Chair M. Cenk ÇAVUŞOĞLU Committee Member David L. Wilson Committee Member Hiram G. Bezerra Committee Member Soumya Ray Committee member Francis Merat Date of Defense February 23rd, 2016 *We also certify that written approval has been obtained for any proprietary material contained therein 2 Contents Chapter 1 Background ............................................................................................... 19 1.1 Coronary Artery Disease ......................................................................... 19 1.2 Atherosclerotic Plaques ........................................................................... 20 1.3 Imaging Technologies for Assessing CAD .............................................. 23 1.4 Optical Coherence Tomography (OCT) ................................................. 23 1.5 IVOCT in Intravascular Imaging ........................................................... 29 1.6 Plaque Characterization Using IVOCT ................................................. 31 1.7 Machine Learning .................................................................................... 34 1.7.1 Overview .................................................................................................. 34 1.7.2 Feature Selection ..................................................................................... 35 1.7.3 Evaluating classifier performance .......................................................... 36 1.7.4 Learning Curve and Performance Measures .......................................... 38 1.7.5 Machine Learning That Matters ............................................................. 39 1.8 Thesis Overview ........................................................................................ 40 Chapter 2 Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT .................. 42 2.1 Introduction .............................................................................................. 42 2.2 Methods ..................................................................................................... 44 3 2.2.1 Image acquisition and selection of volumes of interest (VOIs) ............... 44 2.3 Image Analysis Algorithms ...................................................................... 45 2.3.1 IVOCT Pipeline ....................................................................................... 45 2.4 Results ........................................................................................................ 50 2.5 Discussion .................................................................................................. 52 Chapter 3 Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular OCT 3D pullbacks .............. 55 3.1 Introduction .............................................................................................. 55 3.2 Algorithms and Data Analysis ................................................................. 58 3.2.1 Speckle noise characterization and reduction ........................................ 58 3.2.2 Baseline ................................................................................................... 60 3.2.3 IVOCT signal model and catheter correction ......................................... 60 3.2.4 Pixel shift correction for oblique incident beam ..................................... 61 3.2.5 Estimation of optical properties .............................................................. 62 3.2.6 Plaque Classification .............................................................................. 62 3.3 Experimental Methods ............................................................................. 63 3.3.1 Image Acquisition .................................................................................... 63 3.3.2 VOI Selection ........................................................................................... 64 3.4 Results ........................................................................................................ 65 3.5 Discussion .................................................................................................. 73 4 Chapter 4 Machine Learning Plaque Classification from Intravascular OCT Image Pullbacks ........................................................................................ 76 4.1 Introduction .............................................................................................. 76 4.2 Algorithms ................................................................................................. 80 4.2.1 Preprocessing .......................................................................................... 80 4.2.2 Moving Box (mBox) Processing .............................................................. 81 4.2.3 Feature Extraction .................................................................................. 81 4.2.4 Classifier ................................................................................................. 85 4.3 Experimental Methods ............................................................................. 89 4.3.1 IVOCT Image Acquisition and VOI Selection ......................................... 89 4.3.2 Clinical Training Dataset ........................................................................ 90 4.3.3 Independent Validation Dataset .............................................................. 90 4.3.4 Optimization and Evaluation of the Plaque Classifier ............................ 92 4.3.5 Post Processing ....................................................................................... 93 4.4 Results ........................................................................................................ 94 4.4.1 Training and Model Creation on Clinical Data Set ................................ 95 4.4.2 Evaluation on Independent Validation Dataset ...................................... 99 4.4.3 Visualization of Automatic Classification ............................................. 101 4.5 Discussions .............................................................................................. 103 4.6 Conclusion ............................................................................................... 109 5 Chapter 5 Classification of calcium in intravascular OCT images for the purpose of intervention planning ......................................................................... 110 5.1 INTRODUCTION .................................................................................. 110 5.2 Algorithms ............................................................................................... 113 5.2.1 Image Processing for extraction of DGAS features .............................. 113 5.2.2 Calcium Texton Dictionary Creation .................................................... 114 5.2.3 Model Creation ..................................................................................... 116 5.3 Classification Rule: One-class Support Vector Machine (OC-SVM) 118 5.4 Experimental Methods ........................................................................... 120 5.4.1 IVOCT Image Acquisition and selection of regions for SI extraction... 120 5.5 Training, Testing and Dictionary Datasets .......................................... 122 5.6 Experiments ............................................................................................ 123 5.7 Results ...................................................................................................... 125 5.7.1 Algorithm Parameters ........................................................................... 125 5.7.2 Training and Model Creation using Regions from Clinical Dataset .... 127 5.7.3 Evaluation on Independent Validation Dataset .................................... 128 5.8 Discussion ................................................................................................ 130 5.9 Conclusion ............................................................................................... 133 5.10 Acknowledgement .................................................................................. 133 6 Chapter 6 Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography (OCT) ............................................... 134 6.1 Introduction ............................................................................................ 134 6.2 Optical Coherence Tomography (OCT) ............................................... 136 6.3 Representing an OCT Image ................................................................. 138 6.4 The Plaque-Type Classifier ................................................................... 142 6.5 Empirical Evaluation ............................................................................. 142 6.6 Results and Discussion ........................................................................... 145 6.7 Conclusion ..............................................................................................
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