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Robert J. Tamburo, BS Bioengineering University of Pittsburgh

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Presentation on theme: "Robert J. Tamburo, BS Bioengineering University of Pittsburgh"— Presentation transcript:

1 Gradient-Oriented Boundary Profiles for Shape Analysis Using Medial Features
Robert J. Tamburo, BS Bioengineering University of Pittsburgh Under the Advisement of: George D. Stetten, MD, PhD U. Pitt. Bioengineering CMU Robotics Institute

2 Overview Background Part I Gradient-Oriented Boundary Profiles
Validation of Boundary Profiles Background Part II Boundary Profiles and Shape Analysis Results on Synthetic and RT3D Ultrasound Data Future Work Conclusion

3 Clinical Motivation In 1999:
Cardiovascular Disease (CVD) contributed to one-third of worldwide deaths CVD ranks as the leading cause of death in the U.S. responsible for 40% of deaths per year 62 million Americans live with some form of cardiovascular disease Americans were expected to pay about $330 billion in CVD-related medical costs this year *CDC/NCHS and the American Heart Association Causes of Death for All Americans in the United States, 1999 Final Data

4 Image Analysis Left ventricular (LV) and myocardial volume to calculate cardiac function parameters: - cardiac output - stroke volume - ejection fraction Myocardial thickness and motion can be monitored Diagnoses of CVD, including cardiomyopathy, arrhythmia, ischemia, valve disease, myocardial infarction, and congestive heart failure

5 Medical Imaging 2D ultrasound 3D ultrasound Cine-CT
Gating to the electrocardiogram Mechanically scanned Cine-CT 50 ms/slice (400 ms for full volume) Real-time three-dimensional (RT3D) ultrasound 22 frames/sec (45 ms)

6 Goals Automatically identify and measure structures RT3D ultrasound data Develop “intelligent” boundary points: Gradient-Oriented Boundary Profiles Apply to Profiles to a shape analysis routine

7 Boundary Detection First step in most Image Analysis routines
Convolution with kernel in spatial domain High-pass frequency filters in frequency domain Spatial domain detection: is computationally less expensive often yields better results

8 Gradient Based Detectors
Gradient magnitude is rotationally insensitive Gradient magnitude sensitive to: object intensity background intensity overall image contrast

9 Common Gradient Based Detectors
Roberts Cross 2x2 kernel Very sensitive to noise Very fast Sobel 3x3 kernel Somewhat sensitive to noise Slower than Roberts Cross Both amplify high-frequency noise (derivative)

10 Gradient Based Boundary Detectors With Smoothing
Marr Gaussian Smoothing Laplacian of Gaussian Canny Gaussian smoothing Ridge tracking Both require multiple applications Some fine detail lost

11 Algorithm for Classifying Boundaries
Find candidate boundary points Create an intensity profile Fit a cumulative Gaussian to the intensity profile Eliminate blatantly “bad” profiles Calculate measures of confidence Classify the boundary

12 Difference of Gaussian (DoG) Detector
Gradient magnitude Gaussian smoothing Difference between 3 same-scale Gaussian kernels Measures gradient direction components in 3D

13 Finding Candidate Boundary Points
Over sample with small sampling interval Apply gradient detector (DoG) Accept those above pre-determined threshold

14 Algorithm for Classifying Boundaries
Find boundary candidates Create an intensity profile Fit a cumulative Gaussian to the intensity profile Eliminate blatantly “bad” profiles Calculate measures of confidence Classify the boundary

15 Generating an Intensity Profile
Sample voxels in a neighborhood Partition sampling region Project voxels (splat) to the major axis

16 Sampling Voxels Ellipsoidal or cylindrical Centered at boundary point
Major axis in direction of gradient Reduces the effect of image noise

17 Splatting Voxel Intensity
Triangular or Gaussian footprint Store weights of contribution Profile of average voxel intensity

18 The Intensity Profile

19 Algorithm for Classifying Boundaries
Find boundary candidates Create an intensity profile Fit a cumulative Gaussian to the intensity profile Eliminate blatantly “bad” profiles Calculate measures of confidence Classify the boundary

20 Fitting the Profile Choice of function
Should parameterize boundary Should be intuitive Image acquisition blurs boundaries Convolution with a Gaussian kernel Step function becomes a cumulative Gaussian

21 Fitting the Profile cont.’d
Image Acquisition Real Boundary Image Boundary

22 Derivation of Cumulative Gaussian

23 Cumulative Gaussian A function of 4 parameters Mean, m
Standard deviation, s Asymptotic value for one side, I1 Asymptotic value for other side, I2

24 Boundary Parameterization
m - boundary location s - boundary width I1 - intensity far inside boundary I2 - intensity far outside boundary

25 Curve Fitter AD Model Builder from Otter Research, Inc.*
Quasi-Newton non-linear optimization Auto-differentiation Rapid and robust *http://otter-rsch.com/admodel.htm

26 Algorithm for Classifying Boundaries
Find boundary candidates Create an intensity profile Fit a cumulative Gaussian to the intensity profile Eliminate blatantly “bad” profiles Calculate measures of confidence Classify the boundary

27 Eliminating “Bad” Profiles
“Bad” – profile for which parameters are unacceptible I1 or I2 is outside range for the imaging modality  m is outside of the ellipsoidal sample region These profiles are rejected and no longer considered

28 Algorithm for Classifying Boundaries
Find boundary candidates Create an intensity profile Fit a cumulative Gaussian to the intensity profile Eliminate blatantly “bad” profiles Calculate measures of confidence Classify the boundary

29 Establishing Intrinsic Measures of Confidence
Based on location and width of boundary within sampling region Place thresholds on measures of confidence Accept high-confidence parameters

30 Measures of Confidence for I1 and I2

31 Measure of Confidence for m
zmin = min(z1, z2) Sufficient samples exist on both sides of m

32 Algorithm for Classifying Boundaries
Find boundary candidates Create an intensity profile Fit a cumulative Gaussian to the intensity profile Eliminate blatantly “bad” profiles Calculate measures of confidence Classify the boundary

33 Classify the Boundary Classify boundary with high-confidence parameters Boundary is classified by: Intensity on both sides of boundary Estimate of true boundary location

34 Application to Test Data
3D data set 8-bit voxels 100x100x100 Generated sphere radius of 30 voxels interior value of 32 exterior value of 64

35 Validation on Sphere Ellipsoidal vs. Cylindrical sampling regions
Triangle vs. Gaussian footprints Measures of confidence determined Validation of improved boundary location

36 Radius RMS Errors Neighborhood Type Splat Type RMS Cylindrical
Gaussian 0.092 Triangle 0.104 Ellipsoidal 0.086 0.078

37 95% of profiles estimate radius to less than 1 voxel

38 23% of points estimate radius to less than 1 voxel

39 Boundary Points and Profiles
90 secs DoG boundary points Boundary profiles

40

41 The distribution of error in estimating the intensity values on either side of the boundary as a function of m

42 > 1.5 results in m error < 1

43 A threshold of guarantees

44 guarantees A threshold of

45 Boundary profiles with high-confidence m estimates

46 Medial-Based Shape Analysis
Medial axis by Blum Medialness by Pizer Robust against image noise and shape variation* Stetten automatically identified LV and measured volume *Morse, B.S., et al., Zoom-Invariant vision of figural shape: Effect on cores of image disturbances. Computer Vision and Image Understanding, : p

47 Core Atom Computationally efficient
1 b 2 center Computationally efficient Statistically analyzed to extract medial properties of the core Require a priori knowledge of object intensity Can not differentiate between objects of different intensity

48 Core Profiles Form independent of background intensity
Multiple objects of differing intensities can be found Better boundary location

49 Medial Requirements Distance between boundary profiles within range
Face-to-faceness is close to 1 is the orientation of the ith boundary profile

50 Medial Requirements Boundary profiles have high-confidence estimates
1. 2. 3. where is an intensity tolerance Constraint 3 is for homogeneous core profiles

51 Medial Requirements Solid lines are homogeneous
Dashed lines are heterogeneous exhibiting lateralness

52 Basic Core Configurations

53 Measuring Medial Properties
Population of core profiles analyzed Eigenvalues define dimensionality of the core Eigenvectors define population orientation

54 Lambda Triangle Constraints: 1. 2.

55 Hollow Sphere Models cardiac data To calculate volumes 3D data set
Left Ventricle Myocardium Epicardium Endocardium Hollow Sphere Models cardiac data To calculate volumes 3D data set 8-bit voxels 100x100x100 Hollow sphere inner radius of 15 voxels (intensity of 32) outer radius of 30 voxels (intensity of 128) background of intensity 64

56 Hollow Sphere - Boundaries
as DoG Boundary Points Boundary Profiles

57 Hollow Sphere – Core Profiles

58 Hollow Sphere - Medialness

59

60 Hollow Sphere – Core Profile Radii
The center of the sphere is at 0 and the center of the slab between the spheres is at 22.5

61 Hollow Sphere – Radius Errors
96% of the total profiles vs. 29% of the total DoG points estimated a boundary location within one voxel

62 Hollow Sphere – Core Profile Scale

63 Hollow Sphere – Volume Measures
Core atoms applied twice Volume measures are both fairly accurate Standard deviation of scales shows consistency

64 Concentric Ellipsoids
Models RT3D phantom Determines expected medialness Illustrate non-parametric volume measure techniques

65 Concentric Ellipsoids – Profiles
Homogeneous Boundary Profiles

66 Concentric Ellipsoids – Medialness
Cylindricalness and slabness of concentric ellipsoids

67 Concentric Ellipsoids – Volume
2 proposed techniques Rely on dense core profiles or medial node population

68 Search and Count Method
Construct ellipsoids around core profiles Average intensity of core profile Add voxel to volume count if within tolerance of average Requires dense core profile population

69 Medial Region Fill Construct spheres around each medial node
Deform sphere to an ellipsoid in direction orthogonal to pop. Expand ellipsoid until they collide with object boundaries Count voxels within ellipsoid for volume measure

70 Real-Time 3D Ultrasound
Developed in the early 90’s at Duke University Matrix array of transducer elements Captures pyramid of data at approximately 22 frames per second Rapid enough to acquire cardiac data throughout its cycle

71 RT3D Cardiac Phantom Phantom from OHSU Two latex balloons
Ultrasound Gel solution between balloons Water in inner balloon Myocardium C-mode slice Left Ventricle B-mode slices

72 RT3D Cardiac Phantom Homogeneous boundary profiles
Population of core profiles

73 RT3D Cardiac Phantom Two passes
Medial nodes found from long core profiles Slabness found from short core profiles

74 RT3D Cardiac Phantom Single pass Applying constraints
Resulting medial nodes

75 Future Work Improve computational speed of profiles
Construct models from medial nodes Compute volumes from models

76 Insight Toolkit (ITK) Sponsored by National Library of Medicine
Open-source registration and segmentation toolkit Architecture for large datasets Generic programming Boundary profiles have been contributed

77 Conclusions Gradient-Oriented Boundary profiles:
accurately parameterize boundaries improve the results of core atoms can locate boundaries in noisy data computationally expensive Measures of confidence shown to eliminate low-confidence parameters

78 Acknowledgments Dr. Stetten Aaron Cois Damion Shelton Wilson Chang
Dr. Sclabassi Dr. Li And….

79 YOU! YOU!


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