Author(s)

Yingxia Liu¹, Ziad Saleh², Yulin Song², Maria Chan², Xiang Li², Chengyu Shi², Xin Qian³, Xiaoli Tang²

¹Shandong Communication and Media College, Jinan, China.
²Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
³Radiation 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, Prostate Segmentation, Wavelets, MWDH

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.