A brain MRI bias field correction method created in the Gaussian multi-scale space

2017 ◽  
Author(s):  
Mingsheng Chen ◽  
Mingxin Qin
Keyword(s):  
Brain Mri ◽  
Bias Field ◽  
Scale Space ◽  
Correction Method ◽  
Multi Scale ◽  
Bias Field Correction

scholarly journals A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model

2019 ◽  
Vol 9 (7) ◽  
pp. 1332
Author(s):  
Zhe Zhang ◽  
Jianhua Song
Keyword(s):  
Contextual Information ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Bias Field ◽  
Correction Method ◽  
Intensity Inhomogeneity ◽  
Mri Segmentation ◽  
Robust Clustering ◽  
Clustering Model ◽  
Central Pixel

The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local contextual information (RC_LCI) model was used in this study which accurately segmented brain MRI corrupted by noise and intensity inhomogeneity. For pixels in the neighborhood of the central pixel, a weighting scheme combining local contextual information was used to generate the corresponding anisotropic weight to update the current central pixel and thus remove noisy pixels. Then, a multiplicative framework consisting of the product of a real image and a bias field could effectively segment brain MRI and estimate the bias field. Further, a linear combination of basis functions was introduced to guarantee the bias field properties. Therefore, compared with state-of-the-art models, the segmentation result obtained by RC_LCI was increased by 0.195 0.125 in terms of the Jaccard similarity coefficient. Both visual experimental results and quantitative evaluation demonstrate the superiority of RC_LCI on real and synthetic images.

scholarly journals A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI

2010 ◽  
Vol 38 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Muqing Lin ◽  
Siwa Chan ◽  
Jeon-Hor Chen ◽  
Daniel Chang ◽  
Ke Nie ◽  
...  
Keyword(s):  
Breast Density ◽  
Bias Field ◽  
Correction Method ◽  
Bias Field Correction

scholarly journals Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3000
Author(s):  
Yingping Li ◽  
Samy Ammari ◽  
Corinne Balleyguier ◽  
Nathalie Lassau ◽  
Emilie Chouzenoux
Keyword(s):  
Brain Tumors ◽  
Healthy Volunteers ◽  
Image Acquisition ◽  
Brain Mri ◽  
Bias Field ◽  
Bias Field Correction ◽  
Normalization Methods ◽  
Intensity Normalization

In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations.

Robust Finger Vein Recognition Based on Deep CNN with Spatial Attention and Bias Field Correction

2021 ◽  
Vol 30 (01) ◽  
pp. 2140005
Author(s):  
Zhe Huang ◽  
Chengan Guo
Keyword(s):  
Spatial Attention ◽  
Transfer Learning ◽  
Learning Strategy ◽  
Bias Field ◽  
The Public ◽  
Finger Vein ◽  
Bias Field Correction ◽  
Training Samples ◽  
Vein Recognition ◽  
Finger Vein Recognition

As one of the biometric information based authentication technologies, finger vein recognition has received increasing attention due to its safety and convenience. However, it is still a challenging task to design an efficient and robust finger vein recognition system because of the low quality of the finger vein images, lack of sufficient number of training samples with image-level annotated information and no pixel-level finger vein texture labels in the public available finger vein databases. In this paper, we propose a novel CNN-based finger vein recognition approach with bias field correction, spatial attention mechanism and a multistage transfer learning strategy to cope with the difficulties mentioned above. In the proposed method, the bias field correction module is to remove the unbalanced bias field of the original images by using a two-dimensional polynomial fitting algorithm, the spatial attention module is to enhance the informative vein texture regions while suppressing the other less informative regions, and the multistage transfer learning strategy is to solve the problem caused by insufficient training for CNN-based model due to lack of labeled training samples in the public finger vein databases. Moreover, several measures, including a label smoothing scheme and data augmentation, are exploited to improve the performance of the proposed method. Extensive experiments have been conducted in the work on three public databases, and the results show that the proposed approach outperforms the existing state-of-the-art methods.

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