IJMPCERO  Vol.6 No.3 , August 2017
Novel Wavelet-Based Segmentation of Prostate CBCT Images with Implanted Calypso Transponders
Abstract: 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.
Cite this paper: Liu, Y. , Saleh, Z. , Song, Y. , Chan, M. , Li, X. , Shi, C. , Qian, X. and Tang, X. (2017) Novel Wavelet-Based Segmentation of Prostate CBCT Images with Implanted Calypso Transponders. International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, 6, 336-343. doi: 10.4236/ijmpcero.2017.63030.

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