Open AccessAutomatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images
Author(s) -
Zhan Tianming,
Zhan Yongzhao,
Liu Zhe,
Xiao Liang,
Wei Zhihui
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0121
Subject(s) - fluid attenuated inversion recovery , segmentation , artificial intelligence , white matter , image segmentation , pattern recognition (psychology) , level set method , computer science , level set (data structures) , region of interest , computer vision , hyperintensity , thresholding , magnetic resonance imaging , image (mathematics) , medicine , radiology
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.