Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images
The authors propose a fast and effective solution for automatic segmentation of white matter lesions by using T1 and fluid‐attenuated inversion recovery (FLAIR) image modalities with no need for manual segmentation and atlas registration. Initially, a brain tissue segmentation method is used to segm...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-08-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2014.0121 |
| _version_ | 1827817631607947264 |
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| author | Tianming Zhan Yongzhao Zhan Zhe Liu Liang Xiao Zhihui Wei |
| author_facet | Tianming Zhan Yongzhao Zhan Zhe Liu Liang Xiao Zhihui Wei |
| author_sort | Tianming Zhan |
| collection | DOAJ |
| description | The authors propose a fast and effective solution for automatic segmentation of white matter lesions by using T1 and fluid‐attenuated inversion recovery (FLAIR) image modalities with no need for manual segmentation and atlas registration. Initially, a brain tissue segmentation method is used to segment the T1 image into cerebrospinal fluid (CSF), grey matter and white matter. Based on the obtained tissue segmentation results, the region of interest (ROI) of the FLAIR image is created by subtracting the CSF from the FLAIR image. Subsequently, the authors calculate the z‐score of the intensities in the ROI and define a threshold to perform a preliminary identification of abnormalities from normal tissues. The abnormalities obtained at this stage are used as the prior knowledge for the modified level‐set technique. The proposed level set method here is applied based on local Gaussian distribution to precisely detect the boundaries of the white matter lesions in the ROI. The level set method based on local Gaussian distribution fitting energy is robust to the intensity inhomogeneity of MR data and therefore capable of precisely extracting the boundaries of white matter lesions. Experimental analysis and quantitative comparisons with the peak‐seeking and state‐of‐the‐art white matter lesion segmentation (WMLS) techniques demonstrate that the algorithm is a stable and effective approach which significantly outperforms other trusted solutions for white matter lesion segmentation. |
| first_indexed | 2024-03-12T00:36:59Z |
| format | Article |
| id | doaj.art-3d26742262e6456fa2202096f94008b6 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:36:59Z |
| publishDate | 2015-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-3d26742262e6456fa2202096f94008b62023-09-15T09:33:33ZengWileyIET Computer Vision1751-96321751-96402015-08-019444745510.1049/iet-cvi.2014.0121Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery imagesTianming Zhan0Yongzhao Zhan1Zhe Liu2Liang Xiao3Zhihui Wei4School of Computer Science and Communications Engineering, Jiangsu UniversityPeople's Republic of ChinaSchool of Computer Science and Communications Engineering, Jiangsu UniversityPeople's Republic of ChinaSchool of Computer Science and Communications Engineering, Jiangsu UniversityPeople's Republic of ChinaSchool of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingPeople's Republic of ChinaSchool of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingPeople's Republic of ChinaThe authors propose a fast and effective solution for automatic segmentation of white matter lesions by using T1 and fluid‐attenuated inversion recovery (FLAIR) image modalities with no need for manual segmentation and atlas registration. Initially, a brain tissue segmentation method is used to segment the T1 image into cerebrospinal fluid (CSF), grey matter and white matter. Based on the obtained tissue segmentation results, the region of interest (ROI) of the FLAIR image is created by subtracting the CSF from the FLAIR image. Subsequently, the authors calculate the z‐score of the intensities in the ROI and define a threshold to perform a preliminary identification of abnormalities from normal tissues. The abnormalities obtained at this stage are used as the prior knowledge for the modified level‐set technique. The proposed level set method here is applied based on local Gaussian distribution to precisely detect the boundaries of the white matter lesions in the ROI. The level set method based on local Gaussian distribution fitting energy is robust to the intensity inhomogeneity of MR data and therefore capable of precisely extracting the boundaries of white matter lesions. Experimental analysis and quantitative comparisons with the peak‐seeking and state‐of‐the‐art white matter lesion segmentation (WMLS) techniques demonstrate that the algorithm is a stable and effective approach which significantly outperforms other trusted solutions for white matter lesion segmentation.https://doi.org/10.1049/iet-cvi.2014.0121T1-fluid-attenuated inversion recovery image modalityautomatic white matter lesion segmentationbrain tissue segmentation methodcerebrospinal fluidgrey mattermodified level-set technique |
| spellingShingle | Tianming Zhan Yongzhao Zhan Zhe Liu Liang Xiao Zhihui Wei Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images IET Computer Vision T1-fluid-attenuated inversion recovery image modality automatic white matter lesion segmentation brain tissue segmentation method cerebrospinal fluid grey matter modified level-set technique |
| title | Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images |
| title_full | Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images |
| title_fullStr | Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images |
| title_full_unstemmed | Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images |
| title_short | Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images |
| title_sort | automatic method for white matter lesion segmentation based on t1 fluid attenuated inversion recovery images |
| topic | T1-fluid-attenuated inversion recovery image modality automatic white matter lesion segmentation brain tissue segmentation method cerebrospinal fluid grey matter modified level-set technique |
| url | https://doi.org/10.1049/iet-cvi.2014.0121 |
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