Evaluation Method for the Optimization of 3D Rigid Image Registration on Multimodal Image Datasets
Bhumika Handa1, Gaganpreet Singh2, Rose Kamal3, Arun S Oinam4, Vivek Kumar5

1Bhumika Handa, Centre for Medical Physics, Panjab University, Chandigarh, India.
2Gaganpreest Singh, Centre for Medical Physics, Panjab University, Chandigarh, India.
3Rose Kamal, Centre for Medical Physics, Panjab University, Chandigarh, India.Email:
4Arun S Oinam*, Department of Radiotherapy, P.G.I.M.E.R, Chandigarh, India.
5Vivek Kumar*, Centre for Medical Physics, Panjab University, Chandigarh, India. Email: vivek@pu.ac.in
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 5539-5545 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2078109119/2019©BEIESP | DOI: 10.35940/ijeat.A2078.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Optimization based three dimensional (3D) rigid image registration (RIR) is one of the most commonly used methods of image registration in radiotherapy. Interpolator and similarity metric plays a crucial role in optimization image registration process. In this paper, the efficiency of image registration algorithm is analyzed by using various combinations of interpolators and similarity metric in terms of quantitative measures and is compared with commercially available image registration algorithm in radiotherapy. Computed Tomography (CT) and Cone Beam Computed Tomography (CBCT) image datasets were registered by image registration algorithm written in python language using simple image tool kit (SITK). Different combinations of similarity metric and interpolator such as mean square difference (MSD), mutual information (MI), demons and nearest neighbor (NN), linear, B- spline respectively were used in this study. The efficiency of the algorithm was quantified in terms of mean square error (MSE), structural similarity index (SSI), normalized cross correlation (NCC) and mutual information (MI). The image registration algorithm with most efficient combination of similarity metric and interpolator was selected for comparison with the commercially available image registration algorithm. The algorithm for multimodal (CTCBCT) 3D image registration with NN interpolator and MI similarity metric showed the highest values of SSI, NCC and MI as 0.865, 0.933, 1.223 respectively among other combination of interpolator and similarity metric. Further this algorithm when compared and statistically analyzed with commercially available image registration algorithm of Treatment Planning System (TPS. most commonly used for radiotherapy treatment) resulted in no significant difference (F value NCC-3.18, MI-4.010, SSI- 2.776) in their quantitative measures. The present study is limited to 3D RIR and can be extended for deformable image registration.
Keywords: Rigid Image Registration, Computed Tomography, Cone Beam Computed Tomography, Mutual Information, Nearest Neighbor, Treatment Planning System.