A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction
Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a hig...
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| Format: | Journal Article |
| Language: | English |
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2017
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| Online Access: | https://hdl.handle.net/10356/84090 http://hdl.handle.net/10220/42961 |
| _version_ | 1824454581934882816 |
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| author | Chang, Huibin Huang, Weimin Wu, Chunlin Huang, Su Guan, Cuntai Sekar, Sakthivel Bhakoo, Kishore Kumar Duan, Yuping |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Chang, Huibin Huang, Weimin Wu, Chunlin Huang, Su Guan, Cuntai Sekar, Sakthivel Bhakoo, Kishore Kumar Duan, Yuping |
| author_sort | Chang, Huibin |
| collection | NTU |
| description | Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L0 regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies. |
| first_indexed | 2025-02-19T03:24:36Z |
| format | Journal Article |
| id | ntu-10356/84090 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:24:36Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/840902020-03-07T11:50:47Z A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction Chang, Huibin Huang, Weimin Wu, Chunlin Huang, Su Guan, Cuntai Sekar, Sakthivel Bhakoo, Kishore Kumar Duan, Yuping School of Computer Science and Engineering Intensity Inhomogeneity Brain Extraction Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L0 regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies. ASTAR (Agency for Sci., Tech. and Research, S’pore) Accepted version 2017-07-21T03:57:04Z 2019-12-06T15:38:07Z 2017-07-21T03:57:04Z 2019-12-06T15:38:07Z 2017 Journal Article Chang, H., Huang, W., Wu, C., Huang, S., Guan, C., Sekar, S., et al. (2017). A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction. IEEE Transactions on Medical Imaging, 36(3), 721-733. 0278-0062 https://hdl.handle.net/10356/84090 http://hdl.handle.net/10220/42961 10.1109/TMI.2016.2636026 en IEEE Transactions on Medical Imaging © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TMI.2016.2636026]. 14 p. application/pdf |
| spellingShingle | Intensity Inhomogeneity Brain Extraction Chang, Huibin Huang, Weimin Wu, Chunlin Huang, Su Guan, Cuntai Sekar, Sakthivel Bhakoo, Kishore Kumar Duan, Yuping A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction |
| title | A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction |
| title_full | A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction |
| title_fullStr | A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction |
| title_full_unstemmed | A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction |
| title_short | A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction |
| title_sort | new variational method for bias correction and its applications to rodent brain extraction |
| topic | Intensity Inhomogeneity Brain Extraction |
| url | https://hdl.handle.net/10356/84090 http://hdl.handle.net/10220/42961 |
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