Brain Tumor Image Segmentation in MRI Image

Tjahyaningtijas, Hapsari Peni Agustin

doi:10.1088/1757-899x/336/1/012012

Cited by 20 publications

(8 citation statements)

References 11 publications

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“…The results in Tables- (1,2) show low values for MSE and high values for PSNR for both median and Slantlet filters in general but they are much less by using Slantlet filter.…”

Section: -2 Preprocessmentioning

confidence: 91%

“…Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the most widely used techniques to provide of differentiation between brain tissues and to diagnose the brain diseases. It is to use algorithms to analyze the digital images to establish strategies that can distinguish the types and Medical image processing [1,2]. Medical image is always affected by noise, poor image contrast, and the presence of unwanted components.…”

Section: -Introductionmentioning

confidence: 99%

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Medical Image Enhancement to Extract Brain Tumors from CT and MRI images

Al-Dabbas

Morad

2019

eijs

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Always MRI and CT Medical images are noisy so that preprocessing is necessary for enhance these images to assist clinicians and make accurate diagnosis. Firstly, in the proposed method uses two denoising filters (Median and Slantlet) are applied to images in parallel and the best enhanced image gained from both filters is voted by use PSNR and MSE as image quality measurements. Next, extraction of brain tumor from cleaned images is done by segmentation method based on k-mean. The result shows that the proposed method is giving an optimal solution due to denoising method which is based on multiple filter types to obtain best clear images and that is leads to make the extraction of tumor more precision best.

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“…The results in Tables- (1,2) show low values for MSE and high values for PSNR for both median and Slantlet filters in general but they are much less by using Slantlet filter.…”

Section: -2 Preprocessmentioning

confidence: 91%

Section: -Introductionmentioning

confidence: 99%

Medical Image Enhancement to Extract Brain Tumors from CT and MRI images

Al-Dabbas

Morad

2019

eijs

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“…Because of the structural complexity of brain tissue, brain tumour segmentation is a challenging and difficult task [17]. It can be divided mainly into three types based on human intervention [18], [19]:…”

Section: Mri Analysis Approachesmentioning

confidence: 99%

“…Semi-automatic segmentation methods: In such kind of segmentation, the user interacts with the automatic segmentation system; the user needs to enter some parameters and provides a feedback response to the system output. The semi-automatic brain tumour segmentation methods go mainly through three main processes: initialisation, feedback response, and evaluation [19].…”

Section: Mri Analysis Approachesmentioning

confidence: 99%

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

Al-Azawi¹

2021

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Due to the great development in the field of digital image processing, it has become an integral and important part of computer-aided diagnosis systems. Magnetic resonance image (MRI) processing is one of the hot fields that attracted researchers due to its role in improving and speeding up diagnosis. This work presents a new method for identifying abnormal regions in brain MR images using saliency recognition techniques. This is because tumour regions or abnormal regions share with saliency the fact that these regions differ from the rest of the image in terms of size, luminance, and texture. In this paper, we use a saliency extraction method that uses the irregularity of the regions in its definition, as this method depends on the basis that the salient areas in the image are rare and different from the rest of the image, which is what we need to determine the tumour area. The proposed algorithm is applied to a standard dataset and the obtained result showed a high level of accuracy, where the precision, recall, F-measure, and accuracy averages reached 91.8%, 96.2%, 84.5%, and 96% respectively. The results were discussed thoroughly, and the limitations were identified and discussed as well.

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“…Fortunately, with the continuous development of the magnetic resonance imaging (MRI) technology, MRI images have become a necessary tool for observation of brain activities and diagnosis of brain diseases [7][8][9][10]. However, due to the high efficiency of the MRI technology, the large number of MRI images generated has led to an increase in the workload of doctors in film reading, and what is more, manual film reading largely depends on doctors' work experience, which may lead to misdiagnoses [11][12][13][14]. Therefore, analysis of brain MRI images based on the deep learning technology has become a good choice.…”

Section: Introductionmentioning

confidence: 99%

An Alzheimer’s Disease Identification and Classification Model Based on the Convolutional Neural Network with Attention Mechanisms

Chen¹

2021

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MRI image analysis of brain regions based on deep learning can effectively reduce the workload of doctors in reading films and improve the accuracy of diagnosis. Therefore, deep learning models have great application prospects in the classification and prediction of Alzheimer’s patients and normal people. However, the existing research has ignored the correlation between small abnormalities in local brain regions and changes in brain tissues. To this end, this paper studies an Alzheimer’s disease identification and classification model based on the convolutional neural network (CNN) with attention mechanisms. In this paper, the attention mechanisms were introduced from the regional level and the feature level, and the information of brain MRI images was fused from multiple levels to find out the correlation between the slices in brain MRI images. Then, a spatio-temporal graph CNN with dual attention mechanisms was constructed, which made the network model more attentive to the salient channel features while eliminating the impact of certain noise features. The experimental results verified the effectiveness of the constructed model in identification and classification of Alzheimer’s disease.

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Brain Tumor Image Segmentation in MRI Image

Cited by 20 publications

References 11 publications

Medical Image Enhancement to Extract Brain Tumors from CT and MRI images

Medical Image Enhancement to Extract Brain Tumors from CT and MRI images

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

An Alzheimer’s Disease Identification and Classification Model Based on the Convolutional Neural Network with Attention Mechanisms

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