Liver MRI segmentation with edge-preserved intensity inhomogeneity correction

2017 ◽  
Vol 12 (4) ◽  
pp. 791-798 ◽  
Author(s):  
Hui Liu ◽  
Pinpin Tang ◽  
Dongmei Guo ◽  
HaiXia Liu ◽  
Yuanjie Zheng ◽  
...  
Keyword(s):  
Intensity Inhomogeneity ◽  
Liver Mri ◽  
Mri Segmentation ◽  
Inhomogeneity Correction

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

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.

scholarly journals A Review on MR Image Intensity Inhomogeneity Correction

2006 ◽  
Vol 2006 ◽  
pp. 1-11 ◽  
Author(s):  
Zujun Hou
Keyword(s):  
Statistical Models ◽  
Low Frequency ◽  
Intensity Inhomogeneity ◽  
Computer Memory ◽  
Mr Image ◽  
Cpu Time ◽  
Inhomogeneity Correction ◽  
Recent Developments ◽  
Frequency Components ◽  
Overall Performance

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.

scholarly journals Consistent intensity inhomogeneity correction in water-fat MRI

2014 ◽  
Vol 42 (2) ◽  
pp. 468-476 ◽  
Author(s):  
Thord Andersson ◽  
Thobias Romu ◽  
Anette Karlsson ◽  
Bengt Norén ◽  
Mikael F. Forsgren ◽  
...  
Keyword(s):  
Intensity Inhomogeneity ◽  
Inhomogeneity Correction

Segmentation of ultrasound B-mode images with intensity inhomogeneity correction

2002 ◽  
Vol 21 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Guofang Xiao ◽  
M. Brady ◽  
J.A. Noble ◽  
Yongyue Zhang
Keyword(s):  
Intensity Inhomogeneity ◽  
Inhomogeneity Correction

A parametric gradient descent MRI intensity inhomogeneity correction algorithm

2007 ◽  
Vol 28 (13) ◽  
pp. 1657-1666 ◽  
Author(s):  
Maite García-Sebastián ◽  
Elsa Fernández ◽  
Manuel Graña ◽  
Francisco J. Torrealdea
Keyword(s):  
Gradient Descent ◽  
Intensity Inhomogeneity ◽  
Correction Algorithm ◽  
Inhomogeneity Correction
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