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An Interactive Visual Analytics Framework for Diagnostic Gaze Data on Volumetric Medical Images : 3차원 의료 영상 판독 시선 정보의 대화형 시각적 분석 프레임워크
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 서진욱 | - |
| dc.contributor.author | 송현주 | - |
| dc.date.accessioned | 2017-07-13T07:14:44Z | - |
| dc.date.available | 2017-07-13T07:14:44Z | - |
| dc.date.issued | 2016-02 | - |
| dc.identifier.other | 000000133115 | - |
| dc.identifier.uri | https://hdl.handle.net/10371/119180 | - |
| dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 서진욱. | - |
| dc.description.abstract | We propose an interactive visual analytics framework for diagnostic gaze data on volumetric medical images. The framework is designed to compare gaze data from multiple readers with effective visualizations, which are tailored for volumetric gaze data with additional contextual information. Gaze pattern comparison is essential to understand how radiologists examine medical images and to identify factors influencing the examination. However, prior work on diagnostic gaze data using the medical images acquired from volumetric imaging systems (e.g., computed tomography or magnetic resonance imaging) showed a number of limitations in comparative analysis. In the diagnosis, radiologists scroll through a stack of images to get a 3D cognition of organs and lesions that resulting gaze patterns contain additional depth information compared to the gaze tracking study with 2D stimuli. As a result, the additional spatial dimension aggravated the complexity on visual representation of gaze data. A recent work proposed a visualization design based on direct volume rendering (DVR) for gaze patterns in volumetric images | - |
| dc.description.abstract | however, effective and comprehensive gaze pattern comparison is still challenging due to lack of interactive visualization tools for comparative gaze analysis.
In this paper, we first present an effective visual representation, and propose an interactive analytics framework for multiple volumetric gaze data. We also take the challenge integrating crucial contextual information such as pupil size and windowing (i.e., adjusting brightness and contrast of image) into the analysis process for more in-depth and ecologically valid findings. Among the interactive visualization components, a context-embedded interactive scatterplot (CIS) is especially designed to help users to examine abstract gaze data in diverse contexts by embedding medical imaging representations well-known to radiologists in it. We also present the results from case studies with chest and abdominal radiologists | - |
| dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Background 1 1.2 Research Components 5 1.3 Radiological Practice 6 1.4 Organization of the Dissertation 8 Chapter 2 Related Work 9 2.1 Visualization Combining 2D and 3D 9 2.2 Eye Tracking Data Visualization 14 2.3 Comparative Data Analysis 16 2.4 Gaze Analysis in the Medical field 18 Chapter 3 GazeVis: Volumetric Gaze Data 21 3.1 Visualization of Stimuli and Gaze Data 23 3.1.1 Computation of Gaze Field 26 3.1.2 Visualization of Gaze Field 29 3.1.3 Gaze Field for Interactive Information Seeking 30 3.2 Interactions and Dynamic Queries 32 3.2.1 Interaction Design 32 3.2.2 Spatial Filtering 34 3.2.3 Temporal Filtering 34 3.2.4 Transfer Function Control 36 3.2.5 Gaussian Blur Control 38 3.3 Implementation 38 3.4 Evaluation with Radiologists 38 3.4.1 Case Study Protocol 39 3.4.2 Datasets 41 3.4.3 Apparatus 42 3.4.4 Chest Radiologists 42 3.4.5 Abdominal Radiologists 45 3.5 Discussion 49 3.5.1 Spatial Data Structure and Flexibility 49 3.5.2 Interacting with Contextual Data 51 Chapter 4 GazeDx: Interactive Gaze Analysis Framework 53 4.1 Design Rationale 54 4.2 Overviews for Comparative Gaze Analysis 57 4.2.1 Spatial Similarity 57 4.2.2 Qualitative Similarity Overview 58 4.2.3 Multi-level Temporal Overview 60 4.3 In-depth Comparison of Gaze Patterns 65 4.3.1 Detail Views for Individual Readers 65 4.3.2 Aggregation for Group Comparison 67 4.4 CIS: Context-embedded Interactive Scatterplot 68 4.4.1 Flexible Axis Configuration 68 4.4.2 Focus Attention with Familiar Representations 69 4.4.3 Scatterplot Matrix with CIS 72 4.5 Interactive Selection and Filtering 72 4.5.1 Selection by Freehand Drawing 73 4.5.2 Selection by Human Anatomy 74 4.6 Implementation 76 4.7 Case Studies 77 4.7.1 Case Study Protocol 78 4.7.2 Apparatus 80 4.7.3 Case Study 1: Chest Radiologists 81 4.7.4 Case Study 2: Abdominal Radiologists 85 4.8 Discussion 88 Chapter 5 Conclusion 91 Bibliography 94 Abstract in Korean 105 | - |
| dc.format | application/pdf | - |
| dc.format.extent | 3660305 bytes | - |
| dc.format.medium | application/pdf | - |
| dc.language.iso | en | - |
| dc.publisher | 서울대학교 대학원 | - |
| dc.subject | Eye tracking | - |
| dc.subject | Gaze visualization | - |
| dc.subject | Gaze pattern comparison | - |
| dc.subject | Volumetric medical images | - |
| dc.subject | Context-embedded interactive scatterplot | - |
| dc.subject | Interactive temporal chart | - |
| dc.subject.ddc | 621 | - |
| dc.title | An Interactive Visual Analytics Framework for Diagnostic Gaze Data on Volumetric Medical Images | - |
| dc.title.alternative | 3차원 의료 영상 판독 시선 정보의 대화형 시각적 분석 프레임워크 | - |
| dc.type | Thesis | - |
| dc.description.degree | Doctor | - |
| dc.citation.pages | 106 | - |
| dc.contributor.affiliation | 공과대학 전기·컴퓨터공학부 | - |
| dc.date.awarded | 2016-02 | - |
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