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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.

Author information

1
Shandong Communication and Media College, Jinan, China.
2
Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
3
Radiation Oncology, North Shore Long Island Jewish Health System, New Hyde Park, NY, USA.

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.

KEYWORDS:

CBCT; MWDH; Prostate Segmentation; Wavelets

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