Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification

Çetin, Özdemir; Seymen, Volkan; Sakoğlu, Ünal

doi:10.1016/j.imu.2020.100409

Cited by 17 publications

(13 citation statements)

References 17 publications

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“…With numerous neoteric deep learning models, Ozdemir et al, pointed out the conventional method of detection of MS lesions. In [14], the supervised learning of the minimum Euclidean distancebased algorithm had a step ahead in performance criteria. This accounts for a light-weight automated model for the detection of lesions in MR images.…”

Section: Summary Of Previous Work On Ms Lesion Detectionmentioning

confidence: 99%

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Automated Detection of Multiple Sclerosis Lesions in Normal Appearing White Matter from Brain MRI: A Survey

Khatokar

Kumar

Chandrahas

et al. 2021

AJCST

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Multiple Sclerosis is an inoperable disease of the Central Nervous System (CNS) that irritates the myelin sheath by forming lesions. This affects all organs of the CNS; the vital of them is the brain. This disease can be detected by diagnosis like Magnetic Resonance Imaging (MRI). It is a non-invasive diagnostic test that provides detailed images of the soft tissues of the body. Out of the different variations of MRI, MS lesions are predominantly visible in the DTI (Diffusion Tensor Imaging) variant of MRI. DTI gives enhanced visualization of normal-appearing white matter tracts of the organs, hence providing a better image of the MS lesion. In this paper, the latest methodologies regarding the identification of the MS lesions in MRI scans like T2 FLAIR or DTI, using automated techniques like deep learning, computer vision, neural network and many more are surveyed. Furthermore, this paper consists of a proposed model which would focus on correlating the lesions found in DTI scan with the basic MRI scan like T2. It would identify the MS lesion in DTI scan and eventually highlight that lesion position in the T2 image scan. This would help radiologist in a way to effectively handle multiple MRI scans.

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Section: Summary Of Previous Work On Ms Lesion Detectionmentioning

confidence: 99%

“…This accounts for a light-weight automated model for the detection of lesions in MR images. The work in [14] was relayed mainly using three 2D-MRI protocols, T1-weighted, T2-weighted, and FLAIR modalities.…”

Section: Summary Of Previous Work On Ms Lesion Detectionmentioning

confidence: 99%

Automated Detection of Multiple Sclerosis Lesions in Normal Appearing White Matter from Brain MRI: A Survey

Khatokar

Kumar

Chandrahas

et al. 2021

AJCST

View full text Add to dashboard Cite

show abstract

“…In another study done by Cetina et al, 28 aimed to improve a new, robust, and simplified images dividing technique to make MS-lesions quantitative analysis from multi-modal MRI data, stated that the advanced method categorizes several brain tissues and recolonizes MS lesion with over 90 percent specificity& accuracy, and average sensitivity ranging between 62-65 %.…”

Section: Thirty Cases With Rrms Have Been Enrolled In the Current Workmentioning

confidence: 99%

Value of magnetic resonance imaging in multiple sclerosis patients

Shaheen

2021

Al-Azhar International Medical Journal

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Background: Magnetic Resonance Imaging (MRI) has many advantages in the investigation of Multiple Sclerosis (MS). It delivers precise measurements of disorder activity, simplifies accurate diagnosing, and help in the evaluation of novel treatments. Aim of the work: to recognize the roles of MRI in in the characterizations of MS-connected brain and spinal cord involvement. Patients and methods: 30-cases relapsing-remitting MS (RRMS) cases were involved. The number, volume, and distribution of brain MRI lesions have been assessed via T2-weighted (T2W) image. Crosssectional full normalized brain volume (NBV), normalized deep gray matter volume (NDGMV), normalized white matter volume (NWMV), normalized cortical gray matter volume (NCGMV), and normalized thalamic volume (NTV) have been calculated. Results: MRI findings revealed that the mainstream of case's lesions were detected via T2W-imaging with a mean value of Number of lesions measured of 4.43±3.329, MRI accuracy to detect MS was 86.67% with sensitivity and specificity 88.9 and 66.7 correspondingly. Conclusion: MRI suggestion have a supporting function in what iseventually a clinical MS diagnosing, in the suitable medical condition, and at all times at the exclusion of substitute diagnosing. MRI has improved into the most significant tool for diagnosing and screening of MS. Its elevated sensitivity for the assessment of inflammation and neuro-degenerative procedures in the brain and spinal cord has made it the commonest employed way for the valuation of MS-cases.

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“…To advance the efficiency and accuracy of the medical diagnostic system, especially those that are distributed in complex areas (e.g., brain, skin, lung, and blood cancer classification), several live line diagnostic models (Bengio et al [ 1 ]; Tiwari et al [ 2 ]; Bhatt et al [ 3 ]) work with image processing. The effectiveness of the detection and accuracy of the multidisciplinary medical data diagnostic system depends largely on the quality of the included images captured by a few techniques (Cetin et al [ 4 ]) such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, computed tomography (CT) scan images for lungs, etc. However, due to the uncontrollable lighting conditions and lots of noise availability during capturing, the illumination distributed on the surface of the medical images remains uneven, especially when backlight or fixed lighting conditions affect the diagnostic model (Bonabeau et al [ 5 ]; Banks et al [ 6 ]; Siva Raja and Rani [ 7 ]).…”

Section: Introductionmentioning

confidence: 99%

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks

Kaushal

Islam

Althubiti

et al. 2022

Computational Intelligence and Neuroscience

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Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K-means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds.

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Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification

Cited by 17 publications

References 17 publications

Automated Detection of Multiple Sclerosis Lesions in Normal Appearing White Matter from Brain MRI: A Survey

Automated Detection of Multiple Sclerosis Lesions in Normal Appearing White Matter from Brain MRI: A Survey

Value of magnetic resonance imaging in multiple sclerosis patients

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks

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