Identifying multiple sclerosis lesions in MR images using image processing techniques

Ali, Shawkat; Maher, Asmaa

doi:10.1109/aic-mitcsa.2016.7759913

Cited by 5 publications

(7 citation statements)

References 5 publications

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“…In addition, for increasing the detection accuracy and having the best result, the clustering parameters have been changed and tested; also, as it was mentioned before, in MS lesion detection, effectiveness is the most important part. In this research, lesion diagnosis accuracy has been improved in comparison with others works [25].…”

Section: Discussionmentioning

confidence: 87%

Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

Gheshlaghi¹,

Madani²,

Suratgar³

et al. 2018

IJAIA

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Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

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Section: Discussionmentioning

confidence: 87%

Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

Gheshlaghi¹,

Madani²,

Suratgar³

et al. 2018

IJAIA

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show abstract

“…In addition, we change clustering parameters to get different results for better lesion detection, Also as it was mentioned before, in MS lesion detection, effectiveness is the most important part. In this paper, lesion diagnosis accuracy is improved in comparison with others works [20].…”

Section: Discussionmentioning

confidence: 90%

Multiple Sclerosis Diagnosis with Fuzzy C-Means

Gheshlaghi¹,

Madani²,

Suratgar³

et al. 2018

Computer Science &Amp; Information Technology

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Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm for MS lesion segmentation. We revisit the modification of properties of fuzzy c means algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply canny contraction principle, and establish a relationship between MS lesions and edge detection. For the special case of FCM, we derive a sufficient condition for fixed lesions, allowing identification of them as (local) minima of the objective function.

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“…Finally, the feature extraction techniques are used to detect the features and classify them by using the K-NN classifier. Many image processor techniques are adopted and used to detect the MS lesions [7]. The adopted methods are to segment brain tissue from the skull bones.…”

Section: Related Workmentioning

confidence: 99%

Detection of Multiple Sclerosis using Deep Learning

Jannat

Hoque

Supti

et al. 2021

2021 Asian Conference on Innovation in Technology (ASIANCON)

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Accurate detection of white matter lesions in 3D Magnetic Resonance Images (MRIs) of patients with Multiple Sclerosis is essential for diagnosis and treatment evaluation of MS. It is strenuous for the optimal treatment of the disease to detect early MS and estimate its progression. In this study, we propose efficient Multiple Sclerosis detection techniques to improve the performance of a supervised machine learning algorithm and classify the progression of the disease. Detection of MS lesions become more intricate due to the presence of unbalanced data with a very small number of lesions pixel. Our pipeline is evaluated on MS patients data from the Laboratory of Imaging Technologies. Fluid-attenuated inversion recovery (FLAIR) series are incorporated to introduce a faster system alongside maintaining readability and accuracy. Our approach is based on convolutional neural networks (CNN). We have trained the model using transfer learning and used softmax as an activation function to classify the progression of the disease. Our results significantly show the effectiveness of the usage of MRI of MS lesions. Experiments on 30 patients and 100 healthy brain MRIs can accurately predict disease progression. Manual detection of lesions by clinical experts is complicated and time-consuming as a large amount of MRI data is required to analyze. We analyze the accuracy of the proposed model on the dataset. Our approach exhibits a significant accuracy rate of up to 98.24%.

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Identifying multiple sclerosis lesions in MR images using image processing techniques

Cited by 5 publications

References 5 publications

Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

Multiple Sclerosis Diagnosis with Fuzzy C-Means

Detection of Multiple Sclerosis using Deep Learning

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