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This work presents a means of incorporating prior shape information into the geodesic active contour method of medical image segmentation

Statistical Shape Influence in Geodesic Active Contours

CVPR, (2002): 1316-1323

引用1668|浏览88
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摘要

A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a prob- ability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero level set of a higher dimensional surfa...更多

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简介
  • The anatomical structures that appear in magnetic resonance (MR) or computed tomography (CT) scans are often explicitly extracted or segmented from the image for use in surgical planning, navigation, simulation, diagnosis, and therapy evaluation.
  • The authors refer to the process of labeling individual voxels in the volumetric scan by tissue type, based on properties of the observed intensities as well as anatomical knowledge about normal subjects.
  • With CT data, segmentation of some structures can be performed just using intensity thresholding or other low-level image processing.
  • The distribution of intensity values corresponding to one structure may overlap those of another structure, defeating intensity-based segmentation techniques
重点内容
  • The anatomical structures that appear in magnetic resonance (MR) or computed tomography (CT) scans are often explicitly extracted or segmented from the image for use in surgical planning, navigation, simulation, diagnosis, and therapy evaluation
  • This work presents a means of incorporating prior shape information into the geodesic active contour method of medical image segmentation
  • The shape representation of using Principal Component Analysis on the signed distance map was chosen with the intention of being robust to slight misalignments without requiring exact point-wise correspondences
  • The representation and the curve evolution technique merge well together since the evolution requires a distance map of the evolving curve, which is inherent in the shape model
  • A different statistical shape model could be tied into the evolution method, or a different method of model-based matching could be used with the proposed shape model
结果
  • The authors have tested the segmentation algorithm on synthetic and real shapes, both in 2D and in 3D.
  • A training set of rhombi of various sizes and aspect ratios was generated to define a shape model.
  • Test images were constructed by embedding the shapes of two random rhombi with the addition of Gaussian speckle noise and a low frequency diagonal bias field.
  • Figure 9 illustrates several steps in the segmentation of the synthetic objects.
  • The yellow curve illustrates the MAP shape and pose at each time step.
结论
  • This work presents a means of incorporating prior shape information into the geodesic active contour method of medical image segmentation.
  • The representation and the curve evolution technique merge well together since the evolution requires a distance map of the evolving curve, which is inherent in the shape model.
  • These two modules need not be coupled.
  • A different statistical shape model could be tied into the evolution method, or a different method of model-based matching could be used with the proposed shape model
表格
  • Table1: Partial Hausdorff distance between our segmentation and the manually-segmented ground truth
Download tables as Excel
基金
  • This work was supported by NSF Contract IIS-9610249, NSF Contract DMS-9872228, and NSF ERC (Johns Hopkins University agreement) 8810-274
  • Shenton’s NIMH grants, K02 M-01110 and R01 MH-50747, and Dr
  • McCarley’s grant, R01-40799, and the Brockton Schizophrenia Center for the Department of Veterans Affairs
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