András Horváth Segmentation of 3D ultrasound images of the heart Diagnostic ultrasound imaging

Image segmentation Determine sets of pixels according tocertain visual characteristics Simplify the representation of an image Separate ‘important’ parts

3D cardiac ultrasound Image analysis in cardiac images: Segmentation Motion Analysis. Strain Analysis. Extra spatial information vs Computational complexity (curse of dimensionality) Artifacts: 1. Contrast inhomogeneity and data drop-out. 2. Spurious signal from side lobes. ‘Extra’ goals: Machine/operator independence Handle dropout and inhomogeneity Handle boundary shape Non-local methods Automatic segmentation

1980-: Edge Detectors, connected boundaries, local algorithms 1990-: “Snake” + Template matching algorithms Mildly-disconnected boundaries, local algorithms :Probabilistic algorithms (Bayesian) region-based, non-local algorithms. Discriminative machine-learning algorithms. Spatial inhomogeneity, curse of dimensionality, “drag-and-drop” segmentation. Some history G. Stetten, � eal-Time 3D Ultrasound Methods for Shape Analysis and Visualization, � Methods: Special Issue on Real-Time Signal Processing in the Neurosciences (in press, 11/2001).

Edge-based methods Step edge vs focal blur, shading artifacts! Gaussian smoothed edge Simplest, but difficult method even with high resolution(MR,CT) 2D vs 3D edges Fisrt order methods: Gradient operators Higher order methods: Differential edge detection Computationaly expensive (Preprocessing, noise filtering) Parameter dependent different intensites

Snakes, template matching, active-contour techniques G. Hamarneh, J. Hradsky, "DTMRI Segmentation using DT-Snakes and DT- Livewire", The 6th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Keyword(s): Segmentation, Deformable Models, Diffusion Tensor MRI. Online 3-D Reconstruction of the Right Atrium From Echocardiography Data via a Topographic Cellular Contour Extraction Algorithm Daniel Hillier, Zsolt Czeilinger, Andras Vobornik, and Csaba Rekeczky IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 57, NO. 2, FEBRUARY

Bayesian Approach Advantages: more region-based than edge-based The segmentation is optimal (minimum variance) if the generative model is correct No ad-hoc techniques Canonical introduction of prior shape information (can be weak) Disadvantages: Can get trapped in spurious local maxima Spatially in-homogenous generative models are cumbersome Many extra parameters Optimization is difficult First-order Ultrasound Statistics Determining region from: Mean Std deviation acts/tagare_poster.pdf

Tracking 4D echocardiorgaphy Pairwise active appearance model and its application to echocardiography tracking S. Kevin Zhou, J. Shao, B. Georgescu, and D. Comaniciu

Shape-based Motion Analysis Works well for left ventricle (simple geometry) Works even with simple mathemathical description More modelbased than observation based Not applicable for other shapes

Cooperative methods Self organising maps Self organizing maps

Segmentation results dependend on: - Previous expectations (Blood flow, strain, valves) -Processing time - Cost (architecture) Which one is the best solution? Hybrid methods

Biblograpy Apart from the previously mentioned links E Angelini, S Homma, "Segmentation of real-time threedimensional ultrasound for quantication of ventricular function: a clinical study on right and left ventricles," Ultrasound in Medicine and Biology, pp , Danping Peng, Barry Merriman and M. Kang, pde based fast local level set methods. Department of Mathematics, University of California at Los Angeles, Los Angeles, California , Ch. Brechbuhler and O. Kubler, "Parametrization of closed surfaces for 3-dshape description," Communication Technology Laboratory Image Science SwissFederal Institute of Technology (ETH), p Albert R, "Topology of evolving networks local events and universality,„ Physical Review Letters, p. 5234,