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Diploma ThesisDiploma Thesis
Image-Based Verification of Parametric Models in Heart-Ventricle Volumetry
Martin UrschlerInstitut für Maschinelles Sehen u. Darstellen
Techn. Universität Graz
In Zusammenarbeit mit
Prof. Rainer Rienmüller
Univ. Klinik f. Radiologie, LKH Graz
AgendaAgenda
Introduction Medical Image Data & Problems Volumetry
– Parametric Model (2-axis-method, Greene)– Segmentation-Based Models
Implementation– Overview– LiveWire Approach
Results Conclusion
IntroductionIntroduction
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
Medical Image DataMedical 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
Example Image Data Set NKExample Image Data Set NK
Problems Problems
Partial Volume Effect Distinction between left ventricle
and surrounding tissue
gradient
Weak gradient information
Volumetry (I) - Parametric ModelVolumetry (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
Volumetry (II) - SegmentationVolumetry (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
Implementation (II) - ThresholdingImplementation (II) - Thresholding
weak performance due to– partial volume, weak contrast, non-trivial
separation of chambers
Implementation (III) – SnakesImplementation (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
Implementation (IV) - LiveWireImplementation (IV) - LiveWire
Seems to be very suitable for application!
Graph-theoretic, highly interactive
approach
LiveWire Approach (I)LiveWire Approach (I)
Segmentation consists of:– obj. recognition -> human better– obj. delineation -> machine/algorithm
better LiveWire combines human recognition
and automatic delineation!
LiveWire (II) - IngredientsLiveWire (II) - Ingredients
Image pixel -> node of graph
a
b
ce
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
LiveWire (III) - AlgorithmLiveWire (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 pointEnd point
Shortest-Path map
LiveWire(V) - More FeaturesLiveWire(V) - More Features
Path cooling for intermed. pointsReal Time segmentation possible
(show demo!)
LiveWire Disadvantage:– Segmenting 16 images is faster than
manual segmentation but still time-consuming!
Results (I)Results (I)Evaluation of 31 data setsVolumes achieved by– Parametric model– Manually drawn contours (Prof. Rienmüller)– Thresholding– Contours after Snake segmentation– Contours after LiveWire segmentation
Results (II)Results (II)
LiveWire contours vs. parametric model
Similar results for Snake- and manually drawn contours
Results (III)Results (III) Comparison btw. LiveWire & manual contours
High correlation, fast & accurate reproduction of Prof. Rienmüller‘s contours!
Results (IV)Results (IV)
Summary & ConclusionSummary & 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?