A Stationary Wavelet Transform Based Approach to Registration of Planning CT and Setup Cone beam-CT Images in Radiotherapy

Abstract

Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT).

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

“…Te advantage of the TQWT is that it does not require the adjustment of the wavelet base function and can easily be adjusted according to the signal [32]. SWT shows the local time-frequency characteristics of a signal and has multiresolution analysis capability [33]. Te EWT method is an adaptive wavelet method that uses a wavelet subdivision scheme.…”

Accurate Non-rigid Registration of Lung Images Based on Mutual Information

“…The wavelet analyses were wildly used in image registration, which could speed up the calculation [18]. The 2-dimensional discrete wavelet transform (DWT) was suit for the CT and SPECT images [19,20].…”

Research on high precision image registration method based on line segment feature and ICP algorithm

“…The algorithm are easy to implement, but too sensitive to grayscale changes and have a large amount of calculation; 2. The second way is based on transform domain Merlin transform, Walsh transform, wavelet and other methods [3], they have the advantages of high computational efficiency and are insensitivity to noise, but have high requirements for registration images, usually requiring more than 50% overlapping areas; 3.Feature-based image registration Broad comparison like: SIFT, SURF, LSD, LBD [4][5][6]to extract the salient features of the image make the calculation amount small, fast, and have good robustness to grayscale changes.4. Image registration methods based on deep learning, such methods require high data sets to achieve high-precision registration, the model parameters are too complex, and the training speed in specific scenarios has no advantage over traditional image registration methods [7].…”