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Diploma Thesis

Diploma Thesis. Image-Based Verification of Parametric Models in Heart-Ventricle Volumetry Martin Urschler Institut für Maschinelles Sehen u. Darstellen Techn. Universität Graz In Zusammenarbeit mit Prof. Rainer Rienmüller Univ. Klinik f. Radiologie, LKH Graz. Agenda. Introduction

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Diploma Thesis

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  1. Diploma Thesis Image-Based Verification of Parametric Models in Heart-Ventricle Volumetry Martin Urschler Institut für Maschinelles Sehen u. Darstellen Techn. Universität Graz In Zusammenarbeit mit Prof. Rainer Rienmüller Univ. Klinik f. Radiologie, LKH Graz

  2. Agenda • Introduction • Medical Image Data & Problems • Volumetry • Parametric Model (2-axis-method, Greene) • Segmentation-Based Models • Implementation • Overview • LiveWire Approach • Results • Conclusion

  3. left ventricle sliced heart Introduction • Goal: Measure volume of heart‘s left ventricle • Parametric vs. Segmentation-Based • Purpose: • Heart-Disease Diagnose • stroke volume -> important function parameter

  4. Acquisition: Ultrafast CT Scanner 8 slices • 8 Long-Axis image locations 1 heartbeat • 10 Images per location (1 Heartbeat, ECG-triggered) 10 images Medical Image Data • DICOM fileformat

  5. Example Image Data Set NK

  6. Weak gradient information gradient Problems • Partial Volume Effect • Distinction between left ventricle and surrounding tissue

  7. Measure ellipse parameters • Calculate volume of modi-fied rotational ellipsoid width height V = PI/6 * width * height^2 Volumetry (I) - Parametric Model • Locate image with max. projected ventricle area

  8. Volumetry (II) - Segmentation • Basic Methods: • Thresholding • Edge Detecting Filters (Sobel, Canny) • Region Growing • Active Contours (Snakes) [Kass et al 88] • LiveWire [Barret92][Udupa,Falcao92] • Volume by Simpson Rule: • count segmented image pixels • multiply with voxel size

  9. Implementation (II) - Thresholding • weak performance due to • partial volume, weak contrast, non-trivial separation of chambers

  10. Implementation (III) – Snakes • problems due to: • partial volume, weak contrast • non-intuitive parameterization, only possible after minimi-zation of contour • outliers attracted to high gradients • heavily depending on initial contour

  11. Implementation (IV) - LiveWire • Seems to be very suitable for application! Graph-theoretic, highly interactive approach

  12. LiveWire Approach (I) • Segmentation consists of: • obj. recognition -> human better • obj. delineation -> machine/algorithm better • LiveWire combines human recognition and automatic delineation!

  13. 2 adjacent pixel -> directed arcs of graph • arcs are weighted by cost function • cost(p,q) = w1*fz + w2*fg + w3*fd • p,q ... adjacent pixels (4- or 8-neighbours) • w1,w2,w3 ... weights • fz ... Laplacian Zero Crossing • fg ... Image gradient magnitude • fd ... Image gradient direction cost(b,e) b cost(a,e) c a e cost(c,e) d cost(d,e) LiveWire (II) - Ingredients • Image pixel -> node of graph

  14. End point End point LiveWire (III) - Algorithm • 2 steps: 1.Compute all shortest paths in image to a selected start-point 2. While moving mouse, current position is end point -> select shortest path connecting start and end point • Find shortest paths -> Dijkstra Shortest-Path map Start point

  15. LiveWire(V) - More Features • Path cooling for intermed. points • Real Time segmentation possible (show demo!) • LiveWire Disadvantage: • Segmenting 16 images is faster than manual segmentation but still time-consuming!

  16. Results (I) • Evaluation of 31 data sets • Volumes achieved by • Parametric model • Manually drawn contours (Prof. Rienmüller) • Thresholding • Contours after Snake segmentation • Contours after LiveWire segmentation

  17. Results (II) • LiveWire contours vs. parametric model • Similar results for Snake- and manually drawn contours

  18. Results (III) • Comparison btw. LiveWire & manual contours • High correlation, fast & accurate reproduction of Prof. Rienmüller‘s contours!

  19. Results (IV)

  20. Summary & Conclusion • Comparison segmentation-based vs. parametric volume estimation • Algorithms: • Thresholding, Snakes • LiveWire • LiveWire shows excellent behaviour, it would be powerful for reducing segmentation time in the hands of a radiologist! • Future: 3D Region Growing?

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