Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

Si, Tapas; De, Arunava; Bhattacharjee, Anup Kumar

doi:10.5120/21525-4481

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…Optimization techniques, such as expectation-maximization (EM) and optimization via graph cuts, improved segmentation accuracy on MRI. An optimization approach achieved a similarity index of 0.849 in segmenting infarct volumes with the fast computational time of approximately 3–4 min [ 90 , 91 ]. In [ 92 ], an entropy-based maximization method with a set threshold value and particle swarm optimization (PSO) was able to separate lesions from healthy tissue on brain MRI.…”

Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Subudhi

Dash²,

Mohapatra

et al. 2022

Diagnostics

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Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70–90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.

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Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Subudhi

Dash²,

Mohapatra

et al. 2022

Diagnostics

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“…The imperfections in image acquisition process results in intensity inhomogeneity in MR image. The noise across the MR images is removed using median filter with size 3 × 3 [9]. After denoising, the intensity inhomogeneity is corrected using Max filter based method [5,11].…”

Section: Denoising and Iih Correctionmentioning

confidence: 99%

“…In the segmented images few pixels from scalp and CSF are classified as lesions because they share similar intensity as that of the lesions. Finally, the connected component labelling algorithm [9] is used to separate the lesion from these healthy tissues in the segmented images.…”

Section: Lesion Extractionmentioning

confidence: 99%

“…A. Mayer and H. Greenspan [8] proposed an adaptive mean-shift clustering algorithm to classify the brain voxels into white matter, gray matter and Cerebro-spinal fluid. T. Si et al [9] proposed Grammatical Swarm (GS) based segmentation method for brain MRI in which GS based hardclustering technique is used to segment the lesion in brain. This method performs better than K-Means and Fuzzy c-means algorithm in lesion segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network based Lesion Segmentation of Brain MRI

Si¹,

De²,

Kumar³

2016

CAE

Self Cite

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An Artificial Neural Network based segmentation method for lesion in brain is proposed. First, Magnetic Resonance Images (MRI) are denoised and intensity inhomogeneities are corrected in the preprocessing steps. Artificial neural network is used for training using gray levels and extracted statistical features from the training data with the labelled ground truth. The test images are segmented into lesion and healthy tissues using trained neural network. The connected component labelling algorithm is used to extract only lesion from the segmented images. The proposed method is applied on two MRI data set. The performance of the proposed method is compared with K-means algorithm. The proposed method performs better than K-means algorithm both qualitatively and quantitatively.

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Lymphoma Lesions Detection from Whole Body Diffusion-Weighted Magnetic Resonance Images

Ferjaoui

Cherni

Kraiem³

et al. 2018

2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)

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Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

Cited by 4 publications

References 33 publications

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Artificial Neural Network based Lesion Segmentation of Brain MRI

Lymphoma Lesions Detection from Whole Body Diffusion-Weighted Magnetic Resonance Images

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