Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel M.; Jones, Timothy L.; Barrick, Thomas R.; Howe, Franklyn A.; Ye, Xujiong

doi:10.1016/j.cmpb.2018.01.003

Cited by 189 publications

(95 citation statements)

References 32 publications

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“…Finally, the challenges of the existing methods lay a platform for proposing an automatic method. A segmentation method, 3D supervoxel-based learning, was developed by Soltaninejad et al, 29 which resulted in the errors in the case of the complex structures. However, the method required to bias the ground truth labels systematically.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Additionally, DCNN for segmentation was applied by Hussain et al 28 , which rendered poor performance in the whole tumor region. A segmentation method, 3D supervoxel-based learning, was developed by Soltaninejad et al, 29 which resulted in the errors in the case of the complex structures. On the other hand, a 3D CNN-based model was used for segmentation by Chen et al, 30 which was computationally expensive.…”

Section: Review Of the Literaturementioning

confidence: 99%

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Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Mahesh

Renjit²

2019

Int J Imaging Syst Tech

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Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer‐deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Mahesh

Renjit²

2019

Int J Imaging Syst Tech

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“…Recently, several techniques have been introduced for features extraction, fusion, selection, and classification such as a combination of K‐Means and Neural Network (Sharma, Purohit, & Mukherjee, ), texture features (Soltaninejad et al, ), genetic algorithm (Szenkovits et al, ), particle swarm optimization (Lahmiri, ), LBP and histogram features (Abbasi & Tajeripour, ), and few more as in (Jamal, Hazim Alkawaz, Rehman, & Saba, ; Khan et al, , ; Sharif, Khan, Faisal, Yasmin, & Fernandes, ; Vidyarthi & Mittal, ). However, still, it is an open research problem and yet improvement is required in accuracy for early detection of brain tumor which can help early medication for its cure.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan

Lali

Rehman

et al. 2019

Microscopy Res & Technique

151

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Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker‐based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker‐based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi‐square max conditional priority features approach. In the later step, selected features are fused using a serial‐based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.

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“…Brain tumor segmentation of MR images received much attention over the last decade, especially for treatment planning and follow-up. A range of MR image sequences were used as input to segmentation procedure: single MR image sequence with [22] and without [23] contrast agent, or multi-sequence MR images with [24][25][26][27] or without contrast [24,28].…”

Section: Introductionmentioning

confidence: 99%

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

et al. 2020

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Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

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Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

Cited by 189 publications

References 32 publications

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

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