Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

doi:10.1016/j.compmedimag.2014.06.008

Cited by 12 publications

(8 citation statements)

References 26 publications

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“…According to Eqs. (1) and (2), this change is illustrated in the element m 21 in the TM matrix because the tumor is inside the WM; for more details see [4]. This could be also seen in the basis vector of the topological feature of normal and abnormal brain MRI images.…”

Section: Topological Matrixmentioning

confidence: 92%

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Brain tumor classification and segmentation using sparse coding and dictionary learning

Al-Shaikhli¹,

Yang²,

Rosenhahn³

2016

Biomedical Engineering / Biomedizinische Technik

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This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

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Section: Topological Matrixmentioning

confidence: 92%

“…Therefore, we propose the method [11] for clustering. The topological relationship of these clusters are computed using the method proposed by Al-Shaikhli et al [4]. Let O° be the interior of the cluster, ∂O be the boundary of the cluster, and χ 0 i be the membership function of each cluster.…”

Section: Topological Matrixmentioning

confidence: 99%

Brain tumor classification and segmentation using sparse coding and dictionary learning

Al-Shaikhli¹,

Yang²,

Rosenhahn³

2016

Biomedical Engineering / Biomedizinische Technik

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“…Multi atlas based Bondiau [3] Brainstem MR T1, T2 Al Shaikhli [56] Brainstem, cerebellum, ventricles MR T1 Multiple atlas-based Zarpalas [29] Hippocampus MR T1 Artaechevarria [30] Multi-structure MR Collins [31] Hippocampus, amygdala MR T1 Khan [32] Hippocampus MR T1 Kim [33] Hippocampus MR 7T Coupé [34] Multi-structure MR T1 Wang [35] Hippocampus MR Cardoso [36] Hippocampus MR T1 Panda [40] Optic nerve, eye globe CT Heckemann [51] Multi-structure MR T1 Aljabar [52] Multi-structure MR T1 Lötjönen [53] Multi-structure MR T1 Asman [54] Multi-structure MR Active shape models Bailleul [47] Multi-structure MR Tu [48] Multi-structure MR T1 Pitiot [79] Multi-structure MR T1 Zhao [80] Multi-structure MR Rao [81] Multi-structure MR Bernard [82] Subthalamic nucleus MR T1 Olveres [83] Mid brain MR T1, SWI Active appearance models Hu [26] Hippocampus, amygdala MR T1, T2 Duchesne [45] Medial temporal lobe MR T1 Hu [46] Medial temporal lobe MR T1 Cootes [49] Multi-structure MR Brejl [78] Corpus callosum, cerebellum MR Babalola [50,86] Multi-structure MR T1…”

Section: Structures Image Modalitiesmentioning

confidence: 99%

“…Segmentation of the corpus callosum has been also investigated by using different methods such as deformable models [42,43], or machine learning [44]. Other researchers have focused on a set of different subcortical and cerebellar brain structures, proposing several approaches: active shape and appearance models [45][46][47][48][49][50], atlas-based methods [51][52][53][54][55][56], deformable models [57][58][59] or machine learning approaches [60][61][62][63][64].…”

Section: Introductionmentioning

confidence: 99%

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Dolz

Massoptier²,

Vermandel

2015

IRBM

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This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery.

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“…The tumour position and tumour growth simulating the tumour mass effect are seeded in the atlas to improve the registration accuracy. In Al‐Shaikhli et al (), the topological graph prior with atlas information is used in a modified multilevel set formulation for multiregion segmentation of brain tumour images. In Diaz and Boulanger (), mesh‐free total Lagrangian explicit dynamic (TLED) method is used to deal simulation with atlas deformation and utilized the shape of the tumour segmented from multimodal MRI to derive a new tumour growth model.…”

Section: Brain Tumour Segmentation Techniques Of Mrimentioning

confidence: 99%

Automated brain tumour segmentation techniques— A review

Angulakshmi

Priya

2017

Int J Imaging Syst Tech

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Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography (PET) image, Computer Tomography (CT) image and Magnetic Resonance Image (MRI). Multimodal imaging techniques (such as PET/CT and PET/MRI) that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article.

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Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images

Cited by 12 publications

References 26 publications

Brain tumor classification and segmentation using sparse coding and dictionary learning

Brain tumor classification and segmentation using sparse coding and dictionary learning

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Automated brain tumour segmentation techniques— A review

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