1. Texture-based Deformable Snake Segmentation of the Liver Aaron Mintz Daniela Stan Raicu, PhD Jacob Furst, PhD

2. Overview Objectives and Incentives Objectives and Incentives Tested Texture Methods Tested Texture Methods Tested Snake Deformations Tested Snake Deformations Numerical Evaluation Numerical Evaluation Future Work Future Work

3. Motivations Important Diagnostic Aid to Radiologist Important Diagnostic Aid to Radiologist Liver Cancer: Extremely Deadly Liver Cancer: Extremely Deadly Hypothesis: Texture vs. Intensity-based snake deformation Hypothesis: Texture vs. Intensity-based snake deformation Pixel-to-Pixel area information Pixel-to-Pixel area information Results Show up to 48% Increase in Segmentation Accuracy (Gabor) Results Show up to 48% Increase in Segmentation Accuracy (Gabor)

4. Process and Methods

5. Data Archive Original Computed- Tomography Scans Original Computed- Tomography Scans 25 Individual Patients 25 Individual Patients Greatly Varying Patient Sets Greatly Varying Patient Sets DICOM Format DICOM Format Binary Ground Truth Binary Ground Truth 2916 Image-Ground Pairs 2916 Image-Ground Pairs

6. Image Pre-processing: Gabor Filter Gabor Filter Gabor Filter Gaussian x Sinusoid Gaussian x Sinusoid Various Parameters Various Parameters Aspect Ratio Aspect Ratio Standard Deviation Standard Deviation Wavelength Wavelength Orientation Orientation

7. Image Pre-processing: Haralick Feature Extraction Locally-Calculated Process Locally-Calculated Process Bin Large Range of Intensity Values Bin Large Range of Intensity Values Window-Based Quantification of Intensity-Value Co-Occurrence Window-Based Quantification of Intensity-Value Co-Occurrence Numerical Analysis of Each Corresponding Matrix to Derive Features Numerical Analysis of Each Corresponding Matrix to Derive Features 9 Features Calculated 9 Features Calculated 14332 21422 32142 32213 32111 1433221422 32142 32213 32111 123412013 24200 30410 40210

8. Image Pre-processing: Markov Random Fields Also Locally-Calculated Also Locally-Calculated Estimate “Markovianity” of Windowed Regions Estimate “Markovianity” of Windowed Regions Orientation-based Texture Model Orientation-based Texture Model

9. Snake Constraints Limited Input Limited Input Too Many Corresponding Filters/Features per Image Pixel Too Many Corresponding Filters/Features per Image Pixel Principle Components Analysis Principle Components Analysis Equivalent Number of Principle Components Returned Equivalent Number of Principle Components Returned

10. Snake Input All Principle Components Evaluated Individually All Principle Components Evaluated Individually Gradient Value Edge Map Gradient Value Edge Map Second Gradient Edge Map Second Gradient Edge Map Automatic Initial Curve Point Selection Automatic Initial Curve Point Selection

11. Snake Segmentation Methods Traditional Vector Field Model Traditional Vector Field Model Gradient Vector Flow (GVF) Gradient Vector Flow (GVF) Level-Set Evolution Level-Set Evolution

12. Snake Segmentation Methods (cont.) Balance of Energy Equation Balance of Energy Equation Disadvantages of GVF, Level-Set Disadvantages of GVF, Level-Set

13. Metrics and Results Computationally Difficult to Evaluate Meaningfully Computationally Difficult to Evaluate Meaningfully Straightforward Measurement of Accuracy Straightforward Measurement of Accuracy 3-Dimensional Analysis 3-Dimensional Analysis Volumetric Overlap Volumetric Overlap Average Distance Average Distance Root-Mean-Square Distance Root-Mean-Square Distance Hausdorff Distance Hausdorff Distance

16. Results Effectiveness of Texture Heavily Dependent on Region of Liver Depicted Effectiveness of Texture Heavily Dependent on Region of Liver Depicted Gabor Statistics Across 20-Patient Dataset

17. Future Work Expanding Base of Co-Occurrence and Markov Comparison Expanding Base of Co-Occurrence and Markov Comparison Attempt Combined Principle Components Analysis Attempt Combined Principle Components Analysis Combined Approach – New Automatic Initial Point Selection Combined Approach – New Automatic Initial Point Selection

18. Credits Carl Philips Carl Philips Dr. Raicu, Dr. Furst Dr. Raicu, Dr. Furst Chenyang Xu, Jerry L. Prince, Chunming Li Chenyang Xu, Jerry L. Prince, Chunming Li

19. Questions?

21. Haralick Features Entropy: Entropy: Energy: Energy: Contrast: Contrast: Sum Average: Sum Average: Variance: Variance: Correlation: Correlation: Maximum Probability: Maximum Probability: Inverse Difference Moment: Inverse Difference Moment: Cluster Tendency: Cluster Tendency: