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

• 作者: Chengyu Shi Maria Chan Xiang Li Xiaoli Tang Xin Qian Yingxia Liu Yulin Song Ziad Saleh
• 来源期刊: 医学物理学、临床工程、放射肿瘤学(英文)
• 学科: 医学
• 关键词: CBCT PROSTATE SEGMENTATION WAVELETS MWDH
• 年，卷（期）: 2017,（3）
• 页码范围: 336-343
• 页数: 8页
• 分类号: R73

摘要

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.