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Int J Med Phys Clin Eng Radiat Oncol. 2017 Aug;6(3):336-343. doi: 10.4236/ijmpcero.2017.63030.

Novel Wavelet-Based Segmentation of Prostate CBCT Images with Implanted Calypso Transponders.

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Shandong Communication and Media College, Jinan, China.
Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Radiation Oncology, North Shore Long Island Jewish Health System, New Hyde Park, NY, USA.


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, a Moving 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 based adaptive 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.


CBCT; MWDH; Prostate Segmentation; Wavelets

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