Imaging White Blood Cells Using a Snapshot Hyper-Spectral Imaging System
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Graduate Theses, Dissertations, and Problem Reports 2016 Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System Christopher John Robison Follow this and additional works at: https://researchrepository.wvu.edu/etd Recommended Citation Robison, Christopher John, "Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System" (2016). Graduate Theses, Dissertations, and Problem Reports. 6519. https://researchrepository.wvu.edu/etd/6519 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System Christopher John Robison Thesis submitted to the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University In partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Jeremy M. Dawson, Ph.D., Chair Thirimachos Bourlai, Ph.D. Christopher J. Kolanko, Ph.D. Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2016 Keywords: Hyper-Spectral Imaging, Medical Imaging, Copyright 2016 Christopher John Robison Abstract Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System by Christopher Robison Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells’ features are most prominent in the 428- 442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting. DEDICATION I would like to dedicate this thesis work to my advisory committee members: Dr. Jeremy Dawson, Dr. Thirimachos Bourlai, and Dr. Christopher Kolanko. Without your expertise and guidance, none of this work would be possible. I would like to thank James Robison, Janet Robison, Curtis Robison, and Jessica Robison for their constant encouragement as I don’t know where I would be without their support. I would also like to thank my friends and colleagues who have lent a helping hand at many points during the course of my graduate career. I would notably like to thank the following: Kyle Smith, Anand Kadiyala, Matthew Pachol, Cameron Whitelam, Amanda Holbert, James Searls, Michael Martin, Seth Leffel, Brianna Maze, Jordan Martin, Curtis Sheets III, Ronnie Sanford, Stallone Sabatier, Morgan Trester, Jacob Tyo, the CyberWVU team, the LCSEE Nanophotonics group, and LCSEE faculty members for their hard work keeping this department a well-oiled machine. iii ACKNOWLEDGMENTS The author wishes to acknowledge Rebellion Photonics, WVU LCSEE Nanophotonics, and West Virginia University for their contributions to this work. iv Table of Contents CHAPTER 1: INTRODUCTION ...................................................................................................1 1.1 PROBLEM STATEMENT: 2 1.2 MANUAL BLOOD CELL COUNTING: BACKGROUND AND LIMITATIONS 8 1.2.1 BLOOD COUNT LIMITATIONS ................................................................................................ 10 1.3 BLOOD COUNT AUTOMATION 11 1.3.1 RAPID BLOOD COUNTING SYSTEMS ...................................................................................... 12 1.3.2 PHOTOTHERMAL TOMOGRAPHY METHODS OF CELL DIFFERENTIATION ............................. 13 1.3.3 OTHER SYSTEMS ................................................................................................................... 13 1.4 REMOTE SENSING IMPACTS ON BLOOD CELL COUNTING 14 1.4.1 HYPERSPECTRAL IMAGERY ................................................................................................... 15 1.4.2 BLOOD TISSUE OPTICAL PROPERTIES ................................................................................... 17 1.5 SIMILAR APPLICATIONS 17 1.5.1 BLOOD CELL DETECTION AND CLASSIFICATION USING AN RGB IMAGE PROCESSING APPROACH ..................................................................................................................................... 17 1.5.1.1 A Framework for WBC Segmentation in Microscopic Blood Images using Digital Image Processing ...................................................................................................................................... 18 1.5.1.2 A New White Blood Cell Segmentation using Mean Shift Filter and Region Growing Algorithm ....................................................................................................................................... 18 1.5.1.3 White Blood Cell Segmentation by Color-Space-Based K-Means Clustering .................. 19 1.5.1.4 Visual Saliency Attention for Localization and Segmentation in Stained Leukocyte Images ............................................................................................................................................ 19 1.5.1.5 A Summary of RGB Blood Cell Detection Methods ......................................................... 19 1.5.2 BLOOD CELL DETECTION AND CLASSIFICATION USING HYPERSPECTRAL IMAGING SYSTEMS ....................................................................................................................................................... 20 1.5.2.1 Blood Cell Classification Using a Hyperspectral Imaging Technique .............................. 20 1.5.2.2 Leukocyte identification based on molecular HS imaging ................................................ 22 1.5.2.3 Hyperspectral Image Classification via contextual deep learning ..................................... 23 1.6 SYSTEM PROPOSAL 25 1.6.1 SYSTEM OPERATION.............................................................................................................. 26 1.6.2 SYSTEM DESIGN .................................................................................................................... 27 1.6.3 SYSTEM IMPACT: ................................................................................................................... 28 1.7 THESIS CONTRIBUTION 30 1.8 THESIS ORGANIZATION 33 CHAPTER 2: THEORY ............................................................................................................. 34 2.1 BLOOD CELLS & THE HUMAN IMMUNE SYSTEM 35 2.1.1 OPTICAL PROPERTIES OF LEUKOCYTES ................................................................................ 37 2.2 ENDMEMBER STORAGE DATABASE 39 2.2.1 HIERARCHICAL DATA FORMAT ............................................................................................. 40 2.3 BLOOD CELL SEGMENTATION TECHNIQUES 40 2.4 HYPERSPECTRAL IMAGING SYSTEMS: 41 2.4.1 LINE SCAN HYPERSPECTRAL IMAGING SYSTEMS ................................................................. 41 2.4.2 SPECTRAL SCANNING HYPERSPECTRAL IMAGING SYSTEMS ................................................ 43 2.4.3 SNAPSHOT HYPERSPECTRAL IMAGING SYSTEMS .................................................................. 44 2.4.4 DATACUBE ............................................................................................................................ 47 2.5 SPECTRAL MIXING ................................................................................................................... 48 2.6 HYPERSPECTRAL IMAGE PREPROCESSING 49 2.6.1 FAST FOURIER TRANSFORM FILTERING ................................................................................ 50 2.6.2 ENDMEMBER CONSTRUCTION ............................................................................................... 51 v 2.7 FEATURE EXTRACTION TECHNIQUES 52 2.7.1 SPECTRAL ANGLE MAPPER ................................................................................................... 52 2.7.2 PRINCIPAL