An Atlas Based Performance Evaluation of Inhomogeneity Correcting Effects

AbstractIn automated image processing the intensity inhomogeneity of MR images causes significant errors. In this work we analyze three algorithms with the purpose of intensity inhomogeneity correction. The well-known N3 algorithm is compared to two more recent approaches: a modified level set method, which is able to deal with intensity inhomogeneity and it is, as well, compared to an adaptation of the fuzzy c-means clustering with intensity inhomogeneity compensation techniques. We evaluate the outcomes of these three algorithms with quantitative performance measures. The measurements are done on the bias fields and on the segmented images. We consider normal brain images obtained from the Montreal Simulated Brain Database.

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A robust MR-based attenuation map generation in short-TE MR images of the head employing hybrid spatial fuzzy C-means clustering and intensity inhomogeneity correction

EJNMMI Physics ◽

10.1186/2197-7364-1-s1-a49 ◽

2014 ◽

Vol 1 (Suppl 1) ◽

pp. A49 ◽

Cited By ~ 3

Author(s):

Mohammad Aarabi ◽

Anahita Kazerooni ◽

Parisa Khateri ◽

Mohammad Ay ◽

Hamidreza Rad

Keyword(s):

Intensity Inhomogeneity ◽

Mr Images ◽

Fuzzy C Means ◽

Attenuation Map ◽

Inhomogeneity Correction ◽

Map Generation ◽

Fuzzy C Means Clustering

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Segmentation of MRI brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c-means clustering algorithm

2012 International Conference on Communications, Devices and Intelligent Systems (CODIS) ◽

10.1109/codis.2012.6422153 ◽

2012 ◽

Author(s):

Sudip Kumar Adhikari ◽

J. K. Sing ◽

D. K. Basu ◽

M. Nasipuri ◽

P. K. Saha

Keyword(s):

Clustering Algorithm ◽

Spatial Information ◽

Intensity Inhomogeneity ◽

Brain Images ◽

Fuzzy C Means ◽

Mri Brain ◽

Fuzzy C Means Clustering ◽

Mri Brain Images

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Enhancement and Intensity Inhomogeneity Correction of Diffusion-Weighted MR Images of Neonatal and Infantile Brain Using Dynamic Stochastic Resonance

Journal of Medical and Biological Engineering ◽

10.1007/s40846-017-0270-0 ◽

2017 ◽

Vol 37 (4) ◽

pp. 508-518 ◽

Cited By ~ 9

Author(s):

Munendra Singh ◽

Shiru Sharma ◽

Ashish Verma ◽

Neeraj Sharma

Keyword(s):

Stochastic Resonance ◽

Intensity Inhomogeneity ◽

Mr Images ◽

Dynamic Stochastic Resonance ◽

Inhomogeneity Correction ◽

Diffusion Weighted ◽

Dynamic Stochastic

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Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images

Neuroinformatics ◽

10.1007/s12021-015-9277-2 ◽

2015 ◽

Vol 14 (1) ◽

pp. 5-21 ◽

Cited By ~ 17

Author(s):

Marco Ganzetti ◽

Nicole Wenderoth ◽

Dante Mantini

Keyword(s):

Quantitative Evaluation ◽

Intensity Inhomogeneity ◽

Brain Images ◽

Inhomogeneity Correction ◽

Mr Brain Images ◽

Mr Brain

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FCM Clustering Algorithms for Segmentation of Brain MR Images

Advances in Fuzzy Systems ◽

10.1155/2016/3406406 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 17

Author(s):

Yogita K. Dubey ◽

Milind M. Mushrif

Keyword(s):

Clustering Algorithms ◽

Intensity Inhomogeneity ◽

Noise Robustness ◽

Mr Images ◽

Tissue Segmentation ◽

Brain Images ◽

Fcm Clustering ◽

Weak Boundaries ◽

Brain Mr Images ◽

Mr Brain Images

The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzyc-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

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

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.33.65 ◽

2017 ◽

Vol 33 ◽

pp. 65-79 ◽

Cited By ~ 2

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.

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