Eric K. T. Hui University of Waterloo, M.A.Sc. Seminar Wednesday, November 12, 2003

1. Region-Based Feature Extraction of Prostate Ultrasound Images: A Knowledge-Based ApproachUsing Fuzzy Inferencing Eric K. T. Hui University of Waterloo, M.A.Sc. Seminar Wednesday, November 12, 2003 4:30 PM in DC 2584

2. Outline • Introduction • Medical Background • Related Researches • Problem Formulation • Proposed Feature Extraction • Analysis • Conclusions • Future Works • Questions and Comments

3. Introduction- Prostate Cancer - • Prostate cancer is the most frequently diagnosed cancer in Canadian men: • 18,800 will be newly diagnosed. • 4,200 will die of it. • Exact cause remains unknown. • Early detection is the key in controlling and localizing cancerous cells.

4. Introduction- TRUS - • Digital transrectal ultrasonography (TRUS) • One of the early detection techniques. • Low cost, high availability, high safety, immediate results. • TRUS can be used to plan and guide prostate biopsy. • This thesis tries to automate the cancerous region detection process.

5. Introduction- Features - • Feature: • Measurement of some characteristics (e.g. darkness, texture). • A good feature should be discriminative so that, ideally, the cancerous regions are mapped to a different range of feature values in the feature space than the non-cancerous regions. feature value benign cancerous

6. Introduction- This Thesis - • This thesis proposes a new feature extraction method: • Spatial location, symmetry, and other geometric measurements of the regions-of-interest, in addition to the greylevel and texture. • Uses a semi-automatic fuzzy inferencing system (FIS) to relate all the features and mimic radiologists’ knowledge. Outline

7. vas deferens ureter bladder seminal vesicles ejaculatory ducts prostate gland urethra bulbourethral gland rectum testis Medical Background- Male Reproductive System - penis

8. Medical Background- Prostate Zonal Anatomy - vas deferens bladder seminal vesicles anterior fibromuscular stroma (AFMS) central zone (CZ) ejaculatory duct transition zone (TZ) verumontanum peripheral zone (PZ) rectum urethra

9. Young and healthy prostate: Prostate with Benign prostatic hyperplasia (BPH): AFMS AFMS CZ CZ ejaculatory duct ejaculatory duct C C C’ C’ TZ TZ PZ PZ urethra urethra AFMS urethra urethra TZ TZ CZ CZ PZ PZ ejaculatory ducts ejaculatory ducts Back Medical Background- BPH -

10. urethra TZ CZ PZ ejaculatory ducts Medical Background- Prostate Cancer - • Prostate cancer involves the growth of malignant prostate tumours and can be life threatening. • Uneven statistical distribution: • 70% originates in PZ. • 10% originates in CZ. • 20% originates in TZ. • Cancer tends to be localized in the early stage, any asymmetry on the axial view might suggest cancer development.

11. hypoechoic … + anechoic isoechoic hyperechoic Medical Background- TRUS Imaging - • Echoicities:

12. Medical Background- TRUS Imaging - • TRUS imaging: • About 80% of prostate cancer tissues consist of hypoechoic tissues (mixed with other echoicities). • Different probes (e.g. end-fire, side-fire) give different shapes of the captured image of the prostate. Image

13. Medical Background- Summary - • Uneven cancer statistical distribution. • Asymmetry of regions-of-interest. • TRUS echoicities. • Different probes give different prostate shapes. Outline

14. Transform-Based Fourier Transform Gabor Transform Wavelet Transform Statistic-Based First-Order Statistics Second-Order Statistics Related Researches

15. Related Researches- Fourier Transform - • Fourier Transform: • Decompose into pure frequencies: • Not localized in spatial domain. • A global operator. Chapter Outline

16. Related Researches- Gabor Transform - • Gabor Transform: • “Windowed Fourier Transform”. • Trade off between spatial and frequency resolutions. Frequency Domain 0 Spatial Domain 0

17. Related Researches- Gabor Transform - • Gabor Filter: • A variation of the Gabor Transform. • Translate the window in the frequency domain to capture different frequency components.

18. Related Researches- Gabor Transform - • Gabor Filter: • It’s anisotropic (i.e. orientation dependent). texture orientation path of ultrasound wave Chapter Outline

19. Related Researches- Wavelet Transform - • Wavelet Transform: • Multiresolution Analysis (MRA). • Different dilations of basis functions to analyze different scales.

20. Related Researches- Transform-Based Limitations - • Limitations of transform-based methods: • Similar frequency spectrum. Frequency Domain Spatial Domain Chapter Outline

21. Related Researches- First-Order Statistics - • First-Order Statistics: • Greylevel of each pixel. • One of the most discriminative features. Cancerous Region TRUS Image Chapter Outline

22. Back Related Researches- Second-Order Statistics - • Second-Order Statistics: • Statistics on two neighbouring pixels. • Requires a window defining the neighbourhood. • Greylevel Difference Matrix (GLDM): • Contrast (CON): • Mean (MEAN): • Entropy (ENT): • Inverse Difference Moment (IDM): • Angular Second Moment (ASM):

23. Related Researches- Summary - • All these methods were successfully applied to extract features from: • Modalities with good resolution and image quality, such as CT and MRI. • High-level structures such as the overall prostate or large regions (at least 64×64 pixels). • However, …

24. Related Researches- Summary - • However, they are not suitable for extracting features of low-level structures in ultrasound images. • Any size of the window or wavelet basis: • Too large for region boundary integrity. • Too small for reliable statistics. Outline

25. Problem Formulation- Resources - • Average image size 188.6×346.3 pixels. • Average cancerous region size 2920.3 pixels; that is smaller than a circle with radius of 30.5 pixels! Original TRUS Image Prostate Outline TZ Outline Cancerous Region Outline

26. Problem Formulation- Objectives - • To come up with a new set of features that can help differentiate cancerous regions in a TRUS image from the rest of the prostate. • Desirable criteria: • The features can be applied to analyze low-level structures, such as the cancerous regions (~30-radius circle). • The boundary integrity of each region-of-interest should be well preserved. • The features should be isotropic. • The features should be discriminative enough to differentiate cancerous regions from the benign regions. Outline

27. Proposed Feature Extraction Method- Overview - input Region Segmentation Image Registration Raw-Based Feature Extraction Model-Based Feature Extraction Greylevel Texture Region Geometry Symmetry Spatial Location design only Feature Evaluation FIS Feature Design Parameters PDF Estimation Membership Functions output MI Evaluation Fuzzy Rules Feature Selection Outline

28. Proposed Feature Extraction Method- Region Segmentation - • Some region segmentation methods that I have tried: • Graph-theory-based method by constructing Minimum Spanning Tree (MST). • Thresholding on histogram. Graph-theory-based method Thresholding-based method

29. Proposed Feature Extraction Method- Region Segmentation - • Thresholding-based method: Original Gaussian Blurred Histogram Greylevel Segmentation Zonal Segmentation Morphological Operators: “open” and “holes” Resulting Segmentation Overview

30. Proposed Feature Extraction Method- Image Registration - • Prostates have different shapes on TRUS images due to: • Different physical shapes. • Different probes (e.g. side-fire, end-fire). • Prostates may not be located at the centre of the image.

31. Proposed Feature Extraction Method- Image Registration - • The idea is to deform all the prostates into a common model shape: • The model shape should allow the ease of specifying the relative spatial location of a given point with respect to the whole prostate. • The model shape should be similar to an average prostate outline. • The model shape should be reflectionally symmetric about the vertical axis located at the centre of the image.

32. Proposed Feature Extraction Method- Image Registration - • A compromise:

33. Proposed Feature Extraction Method- Image Registration - Affine Transformation Outline-Based Texture-Based Fluid-Landmark-Based Transformation Define Landmarks Model-Based Estimate Optimal Trajectories Calculate Velocity Vectors Interpolate Missing Pixels

34. Proposed Feature Extraction Method- Image Registration - • Define landmarks: • 16 equally spaced landmarks on the prostate outline. • 2 equally spaced landmarks on the vertical axis. • No medical knowledge of the anatomical structure is required.

35. Proposed Feature Extraction Method- Image Registration - • Lagrangian trajectory: • The initial, intermediate, and final positions. • Velocity vectors: • Displacement of the position of a landmark (in a unit of time).

36. Proposed Feature Extraction Method- Image Registration - • Estimate optimal trajectories: • Minimize: • Iterative gradient decent:

37. Proposed Feature Extraction Method- Image Registration - • Interpolate the optimal velocity vectors for the whole image space: • Optimal velocity vectors of the landmarks: • Optimal velocity vectors of the whole image space:

38. Proposed Feature Extraction Method- Image Registration - • Optimal velocity vectors: • Interpolate the optimal Lagrangian trajectories for the whole image:

39. Proposed Feature Extraction Method- Image Registration - • Interpolating missing pixels in the resulting image using linear interpolation. After deformation Before deformation

40. Proposed Feature Extraction Method- Image Registration - • Now, we can easily measure spatial location and symmetry! • Original images: • Registered Images: Overview

41. Proposed Feature Extraction Method- Greylevel - • Blur with Gaussian filter. • Design parameter: • Take average over each region-of-interest. TRUS Pixel-Based Greylevel (GL) Region-Based Greylevel (GL) Overview

42. Proposed Feature Extraction Method- Texture - • GLDM with different window size. • Design parameter: Equations Pixel-Based Region-Based CON MEA ENT IDM ASM Overview

43. Proposed Feature Extraction Method- Symmetry - • Difference from flipped feature images. • Design parameter: none. Greylevel- Symmetry (GS) Texture- Symmetry (GS) Pixel-Based before inverse-deformation Pixel-Based Region-Based Overview

44. Proposed Feature Extraction Method- Spatial Location - • Define coordinate system using a “cone”. • Design parameter:

45. Proposed Feature Extraction Method- Spatial Location - • Spatial Radius (SR): 0 at origin, 1 at the perimeter. • Spatial Angle (SA): 0 at top, 1 at bottom. Spatial- Radius (SR) Spatial- Angle (SA) Pixel-Based before inverse-deformation Pixel-Based Region-Based Overview

46. Proposed Feature Extraction Method- Region Geometry - • Region Area (RA) = number of pixels. • Region Roundness (RR) = • “perimeter of a circle with the same area” divided by • “perimeter of the region”. Region Area (RA) Region Roundness (RR) Overview

47. Proposed Feature Extraction Method- Feature Evaluation - • How to fine-tune design parameters? • How to evaluate each feature? • How to compare the features? Original TRUS Expected Cancerous Region SR ASM

48. Proposed Feature Extraction Method- PDF Estimation - • We can analyze its probability density function (PDF). • Parzen Estimation is used. P(x|Cancerous) P(x|Benign) P(x)

49. Proposed Feature Extraction Method- MI Evaluation - • Entropy: • Measures the degree of uncertainty. • Mutual information between feature and class: • Measures the decrease in entropy with an introduction of a feature F. • Measures the interdependence between class and feature. • Bounds:

50. Proposed Feature Extraction Method- Feature Design Parameters - • Using MI(F;C), the optimal design parameter for each feature can be selected more objectively.