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

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

5. Example Image Data Set NK

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

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

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. LiveWire (II) - Ingredients Image pixel -> node of graph a b c e d 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) cost(a,e) cost(d,e) cost(c,e) 2 adjacent pixel -> directed arcs of graph –arcs are weighted by cost function

14. 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 Start point End point Shortest-Path map

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?