Cerebrospinal fluid image segmentation using spatial fuzzy clustering method with improved evolutionary Expectation Maximization

Abdullah, Afnizanfaizal; Hirayama, Akihiro; Yatsushiro, Satoshi; Matsumae, Mitsunori; Kuroda, Kagayaki

doi:10.1109/embc.2013.6610261

Cited by 14 publications

(12 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We applied the Bias Field Corrector (BFC) software to each of the images after skull stripping with BSE. The BFC is utilized to compensate for the intensity nonuniformity [ 26 ]. Both BSE and BFC are implemented in BrainSuite package ( http://brainsuite.usc.edu/ ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Unified Framework for Brain Segmentation in MR Images

Yazdani

Yusof

Karimian

et al. 2015

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The parametric approaches usually make the assumption that the tissues of brain follow a Gaussian distribution. The statistical model parameters usually are estimated applying a maximum a posteriori (MAP), maximum likelihood method and the expectation–maximization (EM) algorithm that is used for the optimization process [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Brain Segmentation in MR Images

Yazdani

Yusof

Karimian

et al. 2015

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…In segmenting the CSF space from the T2-weighted images with relatively large voxel size (approximately 1 mm 3 ) [22], the spatial-based fuzzy clustering method (SFCM) was applied to reduce the possible partial volume effect [23]. This method differentiated tissues with different signal intensities even in an identical voxel and determined the boundary between the tissues, resulting in a reasonably segmented image.…”

Section: Csf Motion Visualization Based On Cardiac-gated Pc Imagingmentioning

confidence: 99%

Visualization and Characterization of Cerebrospinal Fluid Motion Based on Magnetic Resonance Imaging

Yatsushiro¹,

Sunohara²,

Aoki³

et al. 2018

Hydrocephalus - Water on the Brain

Self Cite

View full text Add to dashboard Cite

Purpose: To characterize cardiac-and respiratory-driven cerebrospinal fluid (CSF) motions in intracranial space noninvasively, four-dimensional velocity mapping (4D-VM), correlation mapping, and power and frequency mapping with cardiac-gated and/or asynchronous magnetic resonance (MR) phase contrast (PC) techniques were conducted. Methods: Cardiac-gated PC in three spatial directions was applied to young, healthy, elderly, healthy, and idiopathic normal pressure hydrocephalus patient groups. 4D-VM was created from time-resolved 3D velocity distribution represented as vector and color coding. The curl and pressure gradient were calculated. Correlation mapping provides propagation delay and correlation of CSF motion at arbitrary points regarding a reference point. In addition, asynchronous PC technique was conducted for healthy volunteers with respiratory instruction as constant rhythm. Cardiac-and respiratory-driven velocities were separated by frequency analysis. Power and frequency mapping present both the energy and dominant frequency of cardiac or respiratory CSF motion. Results: 4D-VM, curl, pressure gradient images, and correlation mapping by cardiacgated PC demonstrated cardiac-driven CSF motion and its propagation properties. Power and frequency mapping, correlation mapping, and displacement analysis exhibited that the cardiac-driven CSF velocity was higher than the respiratory, although the cardiacdriven displacement was smaller. Conclusion: Visualization and characterization techniques based on PC imaging can capture the properties of CSF motion in intracranial space.

show abstract

“…One of the major proposals in the medical image segmentation is concerning the adoption of the spatial distance into the clustering based segmentation as initiated by Tolias and Panas [10], [11], [12]. Furthermore, Liew [13] proposed an automatic segmentation of 3D dimensional Magnetic Resonance Imaging (MRI) brain images.…”

Section: A Fuzzy C-means Clusteringmentioning

confidence: 99%

Segmentation of Cerebrospinal Fluid from 3D CT Brain Scans Using Modiﬁed Fuzzy C-Means Based on Super-Voxels

Bakkari¹,

Fabijańska²

2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-In this paper, the problem of segmentation of 3D Computed Tomography (CT) brain datasets is addressed using the fuzzy logic rules. In particular, a new method which combines Fuzzy C-Means clustering and the idea of super-voxels is introduced. Firstly, the method applies the extended Simple Linear Iterative Clustering (SLIC) method to divide image into supervoxels, which are next clustered by Modified Fuzzy C-Means algorithm. The method deals with 3D images and performs fully three dimensional image segmentation. Ten samples are supplied proving that our Modified Fuzzy C-Means (MFCM) together with super-voxels are apt to take into account a large diversity of special domains that appear and which are inappropriate solved adopting classical Fuzzy C-Means approach. The results of applying the introduced method to segmentation of the CerebroSpinal Fluid (CSF) from the brain ventricles are presented and discussed.

show abstract

Cerebrospinal fluid image segmentation using spatial fuzzy clustering method with improved evolutionary Expectation Maximization

Cited by 14 publications

References 33 publications

A Unified Framework for Brain Segmentation in MR Images

A Unified Framework for Brain Segmentation in MR Images

Visualization and Characterization of Cerebrospinal Fluid Motion Based on Magnetic Resonance Imaging

Segmentation of Cerebrospinal Fluid from 3D CT Brain Scans Using Modiﬁed Fuzzy C-Means Based on Super-Voxels

Contact Info

Product

Resources

About