A new symmetry-based method for mid-sagittal plane extraction in neuroimages

Ruppert, Guilherme C. S.; Teverovskiy, L.A.; Yu, Chen-Ping; Falcão, Alexandre X.; Liu, Yanxi

doi:10.1109/isbi.2011.5872407

Cited by 49 publications

(70 citation statements)

References 9 publications

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“…In the symmetry-based approach [20], the MSP is defined as the plane that maximizes the similarity between the brain and its reflection. Symmetry based approaches first define a parameter space and then need to describe the MSP based on similarity measurement, such as the cross-correlation method which is used to assess the inter-hemispheric symmetry in the selected feature space and the intensity/edge based in addition to a search method/search criteria to find the parameters that maximize the similarity measures.…”

Section: Msp Based Methods and Approachesmentioning

confidence: 99%

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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%

“…Ruppert et al [20] proposed a new symmetry based method for MSP in neuroimages which relies on image features detected using 3D Sobel edge operator and multi-scale correlation to extract the optimal MSP. This method is sensitive to image noises and deformations.…”

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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“…In the symmetry-based approach [12], MSP is defined as the one that maximizes the similarity between the brain and its reflection. Symmetry based approaches first define a parameter space and then need to describe the MSP based on similarity measurement, such as cross-correlation method which is used to assess the interhemispheric symmetry in the selected feature space and intensity/edge based and a search method/search criteria to find the parameters that maximize the similarity measures.…”

Section: Msp Based Methods and Approachesmentioning

confidence: 99%

“…Ruppert et al [12] proposed a new symmetry based method for MSP in neuroimages which is relied on image features detected using 3D Sobel edge operator and multi-scale correlation to extract the optimal MSP, This method is sensitive to image noises and deformations. A method proposed by Teverovskiy and Li [27] is different from the traditional intensity based cross correlation technique in that it performs the cross correlation on an edge image in order to capture the anatomical structures of the brain and skull while ignoring intensity fluctuation and found the MSP accurately on certain pathological 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%

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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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Fast and robust symmetry detection for brain images based on parallel scale‐invariant feature transform matching and voting

Wang

Shi

et al. 2013

Int J Imaging Syst Tech

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Symmetry analysis for brain images has been considered as a promising technique for automatically extracting the pathological brain slices in conventional scanning. In this article, we present a fast and robust symmetry detection method for automatically extracting symmetry axis (fissure line) from a brain image. Unlike the existing brain symmetry detection methods which mainly rely on the intensity or edges to determine the symmetry axis, our proposed method is based on a set of scale‐invariant feature transform (SIFT) features, where the symmetry axis is determined by parallel matching and voting of distinctive features within the brain image. By clustering and indexing the extracted SIFT features using a GPU KD‐tree, we can match multiple pairs of features in parallel based on a novel symmetric similarity metric, which combines the relative scales, orientations, and flipped descriptors to measure the magnitude of symmetry between each pair of features. Finally, the dominant symmetry axis presented in the brain image is determined using a parallel voting algorithm by accumulating the pair‐wise symmetry score in a Hough space. Our method was evaluated on both synthetic and in vivo datasets, including both normal and pathological cases. Comparisons with state‐of‐the‐art methods were also conducted to validate the proposed method. Experimental results demonstrated that our method achieves a real‐time performance and with a higher accuracy than previous methods, yielding an average polar angle error within 0.69° and an average radius error within 0.71 mm. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 314–326, 2013

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A new symmetry-based method for mid-sagittal plane extraction in neuroimages

Cited by 49 publications

References 9 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

Fast and robust symmetry detection for brain images based on parallel scale‐invariant feature transform matching and voting

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