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Lung tumor tracking in fluoroscopic video based on optical flow
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Image of FIG. 1.
FIG. 1.

Vector interpretation of optical flow between two frames. The position of the object in the new frame (lower left) is obtained by displacing the original object position (upper right) by the average optical velocity within it.

Image of FIG. 2.
FIG. 2.

Flowchart of the motion tracking algorithm.

Image of FIG. 3.
FIG. 3.

Tumor contour in the initial reference frame for patient A.

Image of FIG. 4.
FIG. 4.

Cranial-caudal and lateral displacements for patient A. Vertical scale is pixel and the pixel resolution is .

Image of FIG. 5.
FIG. 5.

Displacement validation of the tracked objects in direction (positions in pixels with resolution).

Image of FIG. 6.
FIG. 6.

Comparison between displacement and CC with different reference frames from patient B (shift in pixels). The respiratory signal in the middle is generated using the reference frame selected a few frames after the EOI. The arrow shows the position of the new reference frame at the EOI and the bottom is the respiratory signal generated using the new reference frame in which the double peaks disappear.

Image of FIG. 7.
FIG. 7.

Comparison between displacement and CC from patient B. The reference frame is chosen at the EOE so that both displacement and CC have the same phase.

Image of FIG. 8.
FIG. 8.

Comparison of tracking errors between our method and Cui et al. (Ref. 62).


Generic image for table

Error analysis for the tracking results using validated displacements.

Generic image for table

Error analysis for different breathing phases. All units are pixel.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Lung tumor tracking in fluoroscopic video based on optical flow