Radius-optimized efficient template matching for lesion detection from brain images

Abstract Computer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the opt...

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Autores principales:	Subhranil Koley, Pranab K. Dutta, Iman Aganj
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/df781eff67844181ae3d39949a85a45e
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spelling	oai:doaj.org-article:df781eff67844181ae3d39949a85a45e2021-12-02T15:56:57ZRadius-optimized efficient template matching for lesion detection from brain images10.1038/s41598-021-90147-02045-2322https://doaj.org/article/df781eff67844181ae3d39949a85a45e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90147-0https://doaj.org/toc/2045-2322Abstract Computer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized cross-correlation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, $${\mathbf{\mathcal{O}}}\left( {{\varvec{a}}_{{{\varvec{max}}}} {\varvec{N}}} \right)$$ O a max N , as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity $${\mathbf{\mathcal{O}}}\left( {{\varvec{a}}_{{{\varvec{max}}}} {\varvec{N}}\log {\varvec{N}}} \right)$$ O a max N log N , where $${\varvec{N}}$$ N is the number of voxels in the image and $${\varvec{a}}_{{{\varvec{max}}}}$$ a max is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to $${\mathbf{\mathcal{O}}}\left( {\varvec{N}} \right)$$ O N . We test our methods on one synthetic and two real multiple-sclerosis databases, and compare their performances in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed methods for brain lesion detection and their comparable performance with existing techniques.Subhranil KoleyPranab K. DuttaIman AganjNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-21 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Subhranil Koley Pranab K. Dutta Iman Aganj Radius-optimized efficient template matching for lesion detection from brain images
description	Abstract Computer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized cross-correlation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, $${\mathbf{\mathcal{O}}}\left( {{\varvec{a}}_{{{\varvec{max}}}} {\varvec{N}}} \right)$$ O a max N , as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity $${\mathbf{\mathcal{O}}}\left( {{\varvec{a}}_{{{\varvec{max}}}} {\varvec{N}}\log {\varvec{N}}} \right)$$ O a max N log N , where $${\varvec{N}}$$ N is the number of voxels in the image and $${\varvec{a}}_{{{\varvec{max}}}}$$ a max is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to $${\mathbf{\mathcal{O}}}\left( {\varvec{N}} \right)$$ O N . We test our methods on one synthetic and two real multiple-sclerosis databases, and compare their performances in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed methods for brain lesion detection and their comparable performance with existing techniques.
format	article
author	Subhranil Koley Pranab K. Dutta Iman Aganj
author_facet	Subhranil Koley Pranab K. Dutta Iman Aganj
author_sort	Subhranil Koley
title	Radius-optimized efficient template matching for lesion detection from brain images
title_short	Radius-optimized efficient template matching for lesion detection from brain images
title_full	Radius-optimized efficient template matching for lesion detection from brain images
title_fullStr	Radius-optimized efficient template matching for lesion detection from brain images
title_full_unstemmed	Radius-optimized efficient template matching for lesion detection from brain images
title_sort	radius-optimized efficient template matching for lesion detection from brain images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/df781eff67844181ae3d39949a85a45e
work_keys_str_mv	AT subhranilkoley radiusoptimizedefficienttemplatematchingforlesiondetectionfrombrainimages AT pranabkdutta radiusoptimizedefficienttemplatematchingforlesiondetectionfrombrainimages AT imanaganj radiusoptimizedefficienttemplatematchingforlesiondetectionfrombrainimages
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Radius-optimized efficient template matching for lesion detection from brain images

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