Segmentation of mouse dynamic PET images using a multiphase level set method

  • Cheng-Liao J
  • Qi J
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Abstract

Image segmentation plays an important role in medical diagnosis. Here we propose an image
segmentation method for 4-dimension mouse dynamic PET images. We consider that voxels
inside each organ have similar time activity curves. The use of tracer dynamic information allows
us to separate regions that have similar integrated activities in a static image but with different
temporal responses. We develop a multiphase level set method that utilizes both the spatial and
temporal information in a dynamic PET data set. Different weighting factors are assigned to each
image frame based on the noise level and activity difference among organs of interest. We used a
weighted absolute difference function in the data matching term to increase the robustness of the
estimate and to avoid over-partition of regions with high contrast. We validated the proposed
method using computer simulated dynamic PET data, as well as real mouse data from a microPET
scanner, and compared the results with those of a dynamic clustering method. The results show
that the proposed method results in smoother segments with less number of misclassified voxels.

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Authors

  • Jinxiu Cheng-Liao

  • Jinyi Qi

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