Bias field correction in 3D-MRIs using convolutional autoencoders.

2021 ◽  
Author(s):  
Shashank Nelamangala Sridhara ◽  
Haleh Akrami ◽  
Vaishnavi Krishnamurthy ◽  
Anand Joshi
Keyword(s):  
Bias Field ◽  
Bias Field Correction

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.

Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI

2011 ◽  
Author(s):  
Satish Viswanath ◽  
Daniel Palumbo ◽  
Jonathan Chappelow ◽  
Pratik Patel ◽  
B. Nicholas Bloch ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Empirical Evaluation ◽  
Bias Field ◽  
Computer Aided Detection ◽  
Bias Field Correction ◽  
Computer Aided

Brain extraction in partial volumes T2*@7T by using a quasi-anatomic segmentation with bias field correction

2018 ◽  
Vol 295 ◽  
pp. 129-138 ◽  
Author(s):  
João Valente ◽  
Pedro M. Vieira ◽  
Carlos Couto ◽  
Carlos S. Lima
Keyword(s):  
Bias Field ◽  
Brain Extraction ◽  
Bias Field Correction ◽  
Partial Volumes
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