  Object This study proposes a scale space based algorithm for automated
segmentation of single-shot tagged images of modest SNR. Furthermore the
algorithm was designed for analysis of discontinuous or shearing types of
motion, i.e. segmentation of broken tag patterns.
  Materials and methods The proposed algorithm utilizes non-linear scale space
for automatic segmentation of single-shot tagged images. The algorithm's
ability to automatically segment tagged shearing motion was evaluated in a
numerical simulation and in vivo. A typical shearing deformation was simulated
in a Shepp-Logan phantom allowing for quantitative evaluation of the
algorithm's success rate as a function of both SNR and the amount of
deformation. For a qualitative in vivo evaluation tagged images showing
deformations in the calf muscles and eye movement in a healthy volunteer were
acquired.
  Results Both the numerical simulation and the in vivo tagged data
demonstrated the algorithm's ability for automated segmentation of single-shot
tagged MR provided that SNR of the images is above 10 and the amount of
deformation does not exceed the tag spacing. The latter constraint can be met
by adjusting the tag delay or the tag spacing.
  Conclusion The scale space based algorithm for automatic segmentation of
single-shot tagged MR enables the application of tagged MR to complex
(shearing) deformation and the processing of datasets with relatively low SNR.
