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

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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N3 Bias Field Correction Explained as a Bayesian Modeling Method

Bayesian and grAphical Models for Biomedical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-319-12289-2_1 ◽

2014 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Christian Thode Larsen ◽

J. Eugenio Iglesias ◽

Koen Van Leemput

Keyword(s):

Bayesian Modeling ◽

Bias Field ◽

Modeling Method ◽

Bias Field Correction

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Contraharmonic Mean Based Bias Field Correction in MR Images

Computer Analysis of Images and Patterns - Lecture Notes in Computer Science ◽

10.1007/978-3-642-40261-6_63 ◽

2013 ◽

pp. 523-530 ◽

Cited By ~ 2

Author(s):

Abhirup Banerjee ◽

Pradipta Maji

Keyword(s):

Bias Field ◽

Mr Images ◽

Bias Field Correction ◽

Contraharmonic Mean

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Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

Technology and Health Care ◽

10.3233/thc-213149 ◽

2021 ◽

pp. 1-12

Author(s):

Lin Wu ◽

Tian He ◽

Jie Yu ◽

Hang Liu ◽

Shuang Zhang ◽

...

Keyword(s):

Bias Field ◽

Correction Method ◽

Surface Coil ◽

Intensity Inhomogeneity ◽

Acquisition Time ◽

Surface Coils ◽

In Vivo Experiments ◽

Inhomogeneity Correction ◽

Volume Coil

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.

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