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Segmentation-based Registration of Pre and Intra-operative Brain Ultrasound Images Using Particle Swarm Optimization

Segmentation-based Registration of Pre and Intra-operative Brain Ultrasound Images Using Particle... PurposeBrain ultrasound (BUS) is a plausible solution for both pre-operative and intra-operative brain imaging during neurosurgery. The registration between pre-operative and intra-operative images resolves the adverse effect of the brain shift. But this registration is challenging due to various artefacts present in the intra-operative BUS images. This paper presents a segmentation-based approach for the registration of pre- and intra-operative brain ultrasound images that reduces the effect of artefacts present in both the images.MethodsThe similarity between pre-operative brain ultrasound image (pBUS) and intra-operative brain ultrasound(iBUS) images is poor because of the speckle and present artefacts. It makes registration challenging. It was observed that the hyper-echoic (HE) regions in pBUS and iBUS images get less affected by the artefacts, and these regions have considerable similarity. We apply a patch-based segmentation approach to both the pBUS and the iBUS images to separate those HE regions. Rigid registration is performed between the segmented HE regions. The logarithm of the mean squared error(MSE) captures the similarity between registering image pairs. Different heuristic optimization algorithms such as biogeography-based algorithm (BBO), simulated annealing (SA), and particle swarm optimization (PSO) were applied to minimize the logarithm of MSE. Comparing the results, we found that PSO outperforms all other algorithms in order to find the registration parameters.ResultsExperiments were conducted on the images from the BITE and the RESECT datasets. Results of the proposed method were compared with two other existing methods. Common tag-points were marked with the help of an expert radiologist for evaluation of the performance of the registration. The mean target registration error (mTRE) and structural similarity index (SSIM) were measured before and after the registration for the comparable methods. The average mTRE was computed on 43 pairs of images. The mTRE before registration was 5.87 mm that reduced to 4.8 mm by the method of Chel and Bora (2017), whereas the proposed method reduced it to 2.91 mm. The feature-based method (Machado et al. 2018) failed because of improper feature matching between the registering image pairs.ConclusionUnlike other methods, the proposed method considers the effects of artefacts present in the registering pBUS and iBUS image pairs. The proposed method adopts a patch-based segmentation approach which is robust to noise and extracts similar HE regions from the registering image pairs. Registration is performed between the segmented HE regions from the pBUS and iBUS images. Performance of the proposed method was compared with another PSO-based method (Chel and Bora 2017) and a feature-based method (Machado et al. 2018). The proposed method outperformed the other two methods in reducing the mTRE and SSIM between the registering images. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Segmentation-based Registration of Pre and Intra-operative Brain Ultrasound Images Using Particle Swarm Optimization

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-023-00304-9
Publisher site
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Abstract

PurposeBrain ultrasound (BUS) is a plausible solution for both pre-operative and intra-operative brain imaging during neurosurgery. The registration between pre-operative and intra-operative images resolves the adverse effect of the brain shift. But this registration is challenging due to various artefacts present in the intra-operative BUS images. This paper presents a segmentation-based approach for the registration of pre- and intra-operative brain ultrasound images that reduces the effect of artefacts present in both the images.MethodsThe similarity between pre-operative brain ultrasound image (pBUS) and intra-operative brain ultrasound(iBUS) images is poor because of the speckle and present artefacts. It makes registration challenging. It was observed that the hyper-echoic (HE) regions in pBUS and iBUS images get less affected by the artefacts, and these regions have considerable similarity. We apply a patch-based segmentation approach to both the pBUS and the iBUS images to separate those HE regions. Rigid registration is performed between the segmented HE regions. The logarithm of the mean squared error(MSE) captures the similarity between registering image pairs. Different heuristic optimization algorithms such as biogeography-based algorithm (BBO), simulated annealing (SA), and particle swarm optimization (PSO) were applied to minimize the logarithm of MSE. Comparing the results, we found that PSO outperforms all other algorithms in order to find the registration parameters.ResultsExperiments were conducted on the images from the BITE and the RESECT datasets. Results of the proposed method were compared with two other existing methods. Common tag-points were marked with the help of an expert radiologist for evaluation of the performance of the registration. The mean target registration error (mTRE) and structural similarity index (SSIM) were measured before and after the registration for the comparable methods. The average mTRE was computed on 43 pairs of images. The mTRE before registration was 5.87 mm that reduced to 4.8 mm by the method of Chel and Bora (2017), whereas the proposed method reduced it to 2.91 mm. The feature-based method (Machado et al. 2018) failed because of improper feature matching between the registering image pairs.ConclusionUnlike other methods, the proposed method considers the effects of artefacts present in the registering pBUS and iBUS image pairs. The proposed method adopts a patch-based segmentation approach which is robust to noise and extracts similar HE regions from the registering image pairs. Registration is performed between the segmented HE regions from the pBUS and iBUS images. Performance of the proposed method was compared with another PSO-based method (Chel and Bora 2017) and a feature-based method (Machado et al. 2018). The proposed method outperformed the other two methods in reducing the mTRE and SSIM between the registering images.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Sep 19, 2023

Keywords: Medical image registration; Brain ultrasound image registration; Segmentation-based image registration; Image-guided brain-surgery; Particle swarm optimization

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