A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal

Hu, Qingmao

doi:10.1016/j.neuroimage.2003.08.009

Cited by 98 publications

(89 citation statements)

References 17 publications

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“…A rapid algorithm for automatic extraction of the MSP of the human cerebrum from normal and pathological neuroimages based on local symmetry and histogram outlier removal techniques was developed by Hu and Nowinski [27]. In this method, the MSP is detected by a line fitting algorithm in brain MR and CT images.…”

Section: Msp Based Methods and Approachesmentioning

confidence: 99%

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

2017

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The brain is the most complex organ in the human body and it is divided into two hemispheres-left and right. The left hemisphere is responsible for control of the right side of our body, whereas the right hemisphere is responsible for control of the left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts of clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal planes, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, autism, schizophrenia, lesions and epilepsy. Therefore, in this paper, we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in different neuroimaging techniques such as the magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography (SPECT), which are utilized for detecting various brain related disorders.

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Section: Msp Based Methods and Approachesmentioning

confidence: 99%

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

2017

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“…A rapid algorithm for automatic extraction of the MSP of the human cerebrum from normal and pathological neuroimages based on local symmetry and histogram outlier removal techniques was developed by Hu and Nowinski [19]. In this method, the MSP is detected by line fitting algorithm in brain MR and CT images.…”

Section: Msp Based Methods and Approachesmentioning

confidence: 99%

“…Image modality [11] Hough transformation MRI [13] Line fitting algorithm MRI [14] Edge and cross correlation methods MRI, PET, SPECT [15] Block matching procedure MRI, CT, PET, SPECT [16] Linear stereotaxic registration and template matching MRI [17] Based on similarity measures MRI [19] Local symmetry histogram based outlier removal MRI, CT [20] Feature based approach 2D and 3D MRI [21] Kullback and Leibler's measure MRI, CT [22] Graph cuts algorithm T1-weighted MRI [23] Parallel line fitting and correlation MRI [24] Similarity measure and optimization technique MRI [25] Heuristic maximization method 3D MRI [26] Anatomy corrected asymmetry index (ACAI) FDG-PET [12] Edge based technique and multi scale correlation 3D MRI,CT [27] Intensity based cross correlation approach 3D MRI [28] Intensity based reflection approach MRI, CT, PET, SPECT [29] Edge based and Hough Transformation method CT [30] 3D rigid registration method, greedy search algorithm MRI [31] Kullback-Leibler's measure, surface deformation MRI [32] GPU-K Dimensional tree algorithm, 3D edge registration MRI [34] Random regression forest method T1 weighted MRI [35] Curve fitting method T1, T2 and PD Weighted MRI grey and white matter segmentation was conducted through non-rigid registration with the labeled template image from the MR brain image and found the difference between grey matter volumes in left and right hemispheres. But, this automatic method is not applicable to neuro-images where a large lesion is present.…”

Section: Methods Techniques Usedmentioning

confidence: 99%

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

Kalavathi

Senthamilselvi

Prasath

2016

Preprint

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Brain is the most complex organ in the human body and it is divided into two hemispheres -left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

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“…The symmetric map is with respect to the Mid Sagittal Plane (MSP) [11]. Symmetric maps are calculated respectively in FLAIR and T2, denoted as I v (symFLAIR) and I v (symT 2).…”

Section: Symmetric Mapmentioning

confidence: 99%

Multi-Feature Guided Brain Tumor Segmentation Based on Magnetic Resonance Images

Miao

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, a novel method of high-grade brain tumor segmentation from multi-sequence magnetic resonance images is presented. Firstly, a Gaussian mixture model (GMM) is introduced to derive an initial posterior probability by fitting the fluid attenuation inversion recovery histogram. Secondly, some grayscale and region properties are extracted from different sequences. Thirdly, grayscale and region characteristics with different weights are proposed to adjust the posterior probability. Finally, a cost function based on the posterior probability and neighborhood information is formulated and optimized via graph cut. Experiment results on a public dataset with 20 high-grade brain tumor patient images show the proposed method could achieve a dice coefficient of 78%, which is higher than the standard graph cut algorithm without a probability-adjusting step or some other cost function-based methods.

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A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal

Cited by 98 publications

References 17 publications

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

Multi-Feature Guided Brain Tumor Segmentation Based on Magnetic Resonance Images

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