TITLE:
Novel Wavelet-Based Segmentation of Prostate CBCT Images with Implanted Calypso Transponders
AUTHORS:
Yingxia Liu, Ziad Saleh, Yulin Song, Maria Chan, Xiang Li, Chengyu Shi, Xin Qian, Xiaoli Tang
KEYWORDS:
CBCT, Prostate Segmentation, Wavelets, MWDH
JOURNAL NAME:
International Journal of Medical Physics, Clinical Engineering and Radiation Oncology,
Vol.6 No.3,
August
30,
2017
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.