Original intensity preserved inhomogeneity correction and segmentation for liver magnetic resonance imaging. (January 2019)

• Record Type: Journal Article
• Authors: Liu, Hui; Liu, Shanshan; Guo, Dongmei; Zheng, Yuanjie; Tang, Pinpin; Dan, Guo
• Is Part Of: Biomedical signal processing and control. Volume 47(2019)
• Page Start: 231
• Page End: 239
• Publication Date: 2019-01
• Subjects: Bias field -- Intensity inhomogeneity -- Fuzzy membership mask -- Spatial continuity information; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.08.005
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The MRI with bias field is corrected with the proposed method. The fuzzy membership mask is used to remove the background noise. Bias field correction can increase the accuracy of segmentation. The proposed method has been successfully applied to the clinical liver MRI. Abstract: Intensity inhomogeneity (IIH), also named as bias field, is an undesired phenomenon of liver magnetic resonance imaging (MRI) which severely affects the quantitative analysis of medical image and decreases the performance of subsequent computer aided diagnosis (CAD) of liver cirrhosis. Many algorithms have been proposed to reduce or eliminate IIH of MRI, and some notable achievements for brain MRI have been obtained. However, IIH correction of abdominal MRI receives less attention and is challenging because of the irregular structure and the wide intensity range of different tissues. In this paper, an automatic method based on the global intensity, the local intensity and the spatial continuity information is presented for reducing IIH of liver MRI. What should be noted is that the gray level should be preserved after correction since it is important for subsequent quantitative image analysis. Therefore, a constraint term is introduced based on the information of bias field intensity for appropriate IIH correction. In addition, the objective function introduces a fuzzy membership mask to remove the background noise and avoid misclassification. Our method is successfully applied to the clinical liver MRI and acquires desirable results. Compared with other approaches, our method obtains the best segmentation with Jaccard similarity (JS) = 0.88 ± 0.06, Dice index (DI) = 0.94 ± 0.03, and accuracy (ACC) = 0.99 ± 0.01.