Segmentation
of prostate Cone Beam CT (CBCT) images is an essential step towards real-time
adaptive radiotherapy (ART). It is challenging for Calypso patients, as more
artifacts generated by the beacon transponders are present on the images. We herein propose a novel
wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT
images with implanted Calypso transponders. For a given CBCT, aMoving Window-Based Double Haar
(MWDH) transformation is applied first to obtain the wavelet coefficients.
Based on a user defined point in the object of interest, a cluster algorithm
basedadaptive thresholding is applied to the low
frequency components of the wavelet coefficients, and a Lee filter theory based
adaptive thresholding is applied on the high frequency components.For the next step, the wavelet reconstruction is
applied to the thresholded wavelet coefficients.A binary (segmented) image of the object of interest is therefore
obtained. 5 hypofractionated Calypso prostate patients with daily CBCT were
studied. DICE, Sensitivity, Inclusiveness and ΔV were used to evaluate the
segmentation result.
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