scholarly journals A Multi-Scale Framework for Bias Field Estimation in MRI Brain Images

2018 ◽  
Vol 7 (4.10) ◽  
pp. 197
Author(s):  
Maryjo M George ◽  
Kalaivani S
Keyword(s):  
Intensity Variation ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Brain Images ◽  
Multi Scale ◽  
Field Estimation ◽  
Corrected Data ◽  
Quantitative Analyses ◽  
Mr Brain Images ◽  
Mr Brain

Intensity inhomogeneity is an artifact in MR brain images and causes intensity variation of same tissues on the basis of location of the tissue within the image. It is crucial to minimize this phenomenon to improve the accuracy of the computer-aided diagnosis. Unlike the several methods proposed in the past to minimize intensity inhomogeneity, this proposed method uses a pyramidal decomposition strategy to estimate the bias field in MR brain images. The bias field estimated from the proposed multi-scale framework can be effectively used for intensity inhomogeneity correction of the acquired MR data. The proposed methodology has been tested on simulated database and quantitative analyses in terms of coefficient of variation in grey matter and white matter tissue regions separately and combined coefficient of joint variation are assessed. The qualitative and quantitative analyses on the corrected data indicate that the method is effective for intensity inhomogeneity on brain MR images.  

scholarly journals Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

2006 ◽  
Vol 84 (2-3) ◽  
pp. 66-75 ◽  
Author(s):  
M. Bach Cuadra ◽  
M. De Craene ◽  
V. Duay ◽  
B. Macq ◽  
C. Pollo ◽  
...  
Keyword(s):  
Deformation Field ◽  
Brain Images ◽  
Field Estimation ◽  
Mr Brain Images ◽  
Mr Brain

An Efficient Computational Approach for the Detection of MR Brain Tissues in the Presence of Noise and Intensity Inhomogeneity

2017 ◽  
Vol 33 ◽  
pp. 65-79 ◽  
Author(s):  
G. Sandhya ◽  
Kande Giri Babu ◽  
T. Satya Savithri
Keyword(s):  
Energy Minimization ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Brain Images ◽  
Brain Image ◽  
Anisotropic Diffusion Filter ◽  
The Brain ◽  
Mr Brain ◽  
Brain Tissues

The automatic detection of brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) from the MR images of the brain using segmentation is of immense interest for the early detection and diagnosing various brain-related diseases. MR imaging technology is one of the best and most reliable ways of studying the brain. Segmentation of MR images is a challenging task due to various artifacts such as noise, intensity inhomogeneity, partial volume effects and elemental texture of the image. This work proposes a region based, efficient and modern energy minimization process called as Anisotropic Multiplicative Intrinsic Component Optimization (AMICO) for the brain image segmentation in the presence of noise and intensity inhomogeneity to separate different tissues. This algorithm uses an efficient Anisotropic diffusion filter to decrease the noise. The denoised image gets segmented after the correction of intensity inhomogeneity by the MICO algorithm. The algorithm decomposes the MR brain image as two multiplicative intrinsic components, called as the component of the true image which represents the physical properties of the brain tissue and the component of bias field that is related to intensity inhomogeneity. By optimizing the values of these two components using an efficient energy minimization technique, correction of intensity inhomogeneity and segmentation of the tissues can be achieved simultaneously. Performance evaluation and the comparison with some existing methods have validated the remarkable performance of AMICO in terms of efficiency of segmentation of brain images in the presence of noise and intensity inhomogeneity.

Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models

2011 ◽  
Vol 6 (1) ◽  
pp. 3-12 ◽  
Author(s):  
László Szilágyi ◽  
Sándor M. Szilágyi ◽  
Balázs Benyó ◽  
Zoltán Benyó
Keyword(s):  
Intensity Inhomogeneity ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

scholarly journals Stabilised bias field: segmentation with intensity inhomogeneity

2016 ◽  
Vol 10 (4) ◽  
pp. 302-313 ◽  
Author(s):  
Jack Spencer ◽  
Ke Chen
Keyword(s):  
Convex Relaxation ◽  
Intensity Variation ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Variational Model ◽  
Great Success ◽  
Two Phase ◽  
Relaxation Methods ◽  
Piecewise Constant ◽  
Field Estimation

Automatic segmentation in the variational framework is a challenging task within the field of imaging sciences. Achieving robustness is a major problem, particularly for images with high levels of intensity inhomogeneity. The two-phase piecewise-constant case of the Mumford-Shah formulation is most suitable for images with simple and homogeneous features where the intensity variation is limited. However, it has been applied to many different types of synthetic and real images after some adjustments to the formulation. Recent work has incorporated bias field estimation to allow for intensity inhomogeneity, with great success in terms of segmentation quality. However, the framework and assumptions involved lead to inconsistencies in the method that can adversely affect results. In this paper we address the task of generalising the piecewise-constant formulation, to approximate minimisers of the original Mumford-Shah formulation. We first review existing methods for treating inhomogeneity, and demonstrate the inconsistencies with the bias field estimation framework. We propose a modified variational model to account for these problems by introducing an additional constraint, and detail how the exact minimiser can be approximated in the context of this new formulation. We extend this concept to selective segmentation with the introduction of a distance selection term. These models are minimised with convex relaxation methods, where the global minimiser can be found for a fixed fitting term. Finally, we present numerical results that demonstrate an improvement to existing methods in terms of reliability and parameter dependence, and results for selective segmentation in the case of intensity inhomogeneity.

scholarly journals Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 2881-2887
Keyword(s):  
Fuzzy Clustering ◽  
Bias Field ◽  
Mr Images ◽  
Brain Images ◽  
Normal Tissues ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Abnormal Tissue

The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis

scholarly journals Evaluation of performance metrics for bias field correction in MR brain images

2009 ◽  
Vol 29 (6) ◽  
pp. 1271-1279 ◽  
Author(s):  
Zin Yan Chua ◽  
Weili Zheng ◽  
Michael W.L. Chee ◽  
Vitali Zagorodnov
Keyword(s):  
Performance Metrics ◽  
Bias Field ◽  
Brain Images ◽  
Evaluation Of Performance ◽  
Bias Field Correction ◽  
Mr Brain Images ◽  
Mr Brain
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