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An efficient similarity metric for 3D medical image registration

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Abstract

In this paper, we develop an efficient mutual information based similarity metric for 3D medical image registration. The efficiency of the metric lies in the computation of mutual information, which uses modified algorithms for calculating entropy and joint entropy. We implemented the newly developed Efficient Entropy Mutual Information (EEMI) metric in SimpleITK, which is an open source image registration and segmentation toolkit. We designed 24 medical image registration frameworks using four similarity metrics, namely Mean Squares (MS), Joint Histogram Mutual Information (JHMI), Mattes Mutual Information (MattesMI) and our proposed EEMI, and six optimizers namely, Gradient Descent (GD), Conjugate Geadient Line Search (CGLS), 1+1 Evolutionary, Powell, Nelder-Mead (Amoeba) and Limited Memory Broyden Fletcher Goldfarb Shannon (LBFGS2). Using these frameworks, we performed elaborate comparative evaluation and analysis of EEMI in terms of registration accuracy and computation time. We used four different medical image data sets for our experiments. Data consisted of intra-modal and inter-modal image pairs of the brain and thorax obtained from different medical institutions as well as publicly available databases. Registration results prove the superiority and consistency of performance of EEMI compared to MS and JHMI. When compared with the benchmark MattesMI metric, performance of EEMI is on a par with respect to Dice score, Jaccard score and Hausdorff distance. With respect to computation time, EEMI is faster with GD, CGLS and Amoeba optimizers with 61.29%, 42.67% and 10.92% gain in computation times respectively. Among the other three optimizers, EEMI is more consistent with Powell’s optimizer. Mean, median and STD of TRE values of EEMI with Powell’s optimizer are respectively 67.42%, 65.84% and 74.63% less than that of MattesMI. Hausdorff distance of EEMI is 47.47% less than MattesMI, with Powell’s optimizer.

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Availability of Data

The data set analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We are thankful to the Department of Nuclear Medicine, AIIMS Delhi, India, Panacea Hospital, Kanpur India and Hi-Care Diagnostics, Kanpur, India for providing data for our work and for assisting us with their expertise. We are also thankful to the Department of Radiology, SSKM Kolkata, India for helping us in testing the work.

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  1. Department of Information Technology, IIEST, Shibpur, Howrah, 711103, WB, India

    Debapriya Sengupta & Arindam Biswas

  2. GLA University, Mathura, 281406, UP, India

    Phalguni Gupta

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  1. Debapriya Sengupta
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Correspondence to Debapriya Sengupta.

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Sengupta, D., Gupta, P. & Biswas, A. An efficient similarity metric for 3D medical image registration. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18710-1

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