Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region‐growing and quasi‐Monte Carlo‐expectation maximization algorithm

Hachemi, B.; Chama, Zouaouia; Alim-Ferhat, F.; Lamini, El‐Sedik; Abderrahmane, Abdelkader; Anani, M.; Choquet, Catherine

doi:10.1002/ima.22376

Cited by 5 publications

(3 citation statements)

References 18 publications

(37 reference statements)

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“…All the segmentation techniques purposed is to achieve an efficient and precise system. Fuzzy uses partial membership and is better for real problems [93] Determining membership function is not easy [114] Region Growing Selection of initial seed point and examine neighboring pixels [115] Capable of noise resistance, applicable when it is easy to identify similarity criteria [116] Manual selection of homogeneity criterion [117] Watershed Viewed on topological surfaces [118] Results are more computationally efficient, detected continuous edges [119] Can lead to oversegmentation [118]…”

Section: Resultsmentioning

confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

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<span lang="EN-US">Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.</span>

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Section: Resultsmentioning

confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

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“…Overall, the aim of all these segmentation techniques is to develop an accurate and effective system. Fuzzy logic allows for partial membership, better for real-world problems [37] Determining the membership function is difficult [41] Region Growing Region Growing is the selection of an initial seed point and examination of neighboring pixels [32] Effective at noise resistance, applicable when it is easy to identify similarity criteria [38] Manual selection of the homogeneity criterion [42], [43] Watershed Watershed Method based on topological surfaces [33] Results are computationally efficient, detects continuous edges [33] Can lead to over-segmentation [33] K-means K-means Method that divides data into k clusters to define k-centroid values of each cluster [34], [35] Simple and suitable for large datasets [39].…”

Section: Decision Tree Analysis Modelmentioning

confidence: 99%

Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review

Kandaya,

Abdullah,

Saad

et al. 2024

IJ-AI

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<p>Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis, treatment, and prognosis of complications and patient outcomes in a number of neurological diseases are currently made possible by ML through pattern recognition algorithms. However, the use of MRI is limited because of MRI plays an important role in diagnosing lumbar disc disease. However, the use of MRI is limited due to its high cost and significant operational and processing time. More importantly, MRI is contraindicated in some patients who are claustrophobic or have pacemakers due to the potential for damage. Recent studies have shown that treatment within six hours of a stroke can save a patient's life. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients.</p>

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“…Moreover, MC method was implemented to estimate the x-ray absorption in the brain tumor during different radiotherapy clinical procedures [31] and in stereotactic radiotherapy [32]. It has been also employed to improve the detection process of brain tumors in MRI images [33]. In addition, MC method played a useful role in enhancing the detection of cervical dysplasia and cancer [34,35].…”

Section: Introductionmentioning

confidence: 99%

Identifying different types of tumors in human brain, breast, and uterus according to their optical properties in the red to near-infrared region

Hamdy

2020

Preprint

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26Optical diagnosis techniques are gaining validity due to their competitive advantages 27 including safety and functionality. These methods probe the tissue using light in the red 28 to near-infrared region that shows relatively high penetration in biological tissues. The 29 reflected light which is controlled by tissue absorption and scattering parameters can 30 provide significant information about its pathology. In the present work, a method for 31 identifying different types of tumors in human brain, breast and uterus based on their 32 absorption and scattering characteristics in the red to near-infrared spectra is proposed. 33The classification method depends on studying the red to near-infrared light diffusion 34 through the examined tissues using Monte-Carlo simulation and finite element solution of 35 the light diffusion equation. The obtained tissue reflectance profiles, optical fluence rate 36 distribution and absorbed faction in each tumor type show different characteristics along 37 the selected wavelengths. The variation in these three features can be considered as an 38 alternative approach of medical diagnosis or can assist with other conventional diagnosis 39 methods. 40 41 42 Brain, breast and uterus are very vital and complex human body organs that have affected 43 by many types of tumors with different characteristics and symptoms. Diagnosing brain 44 tumors can be accomplished using different medical imaging techniques such as 45 magnetic resonance imaging (MRI) and computer tomography (CT) scan or by a 46 neurological exam [1]. While, X-ray mammography, MRI, breast ultrasonography and 48 CT can be used also to diagnose uterus tumors, transvaginal ultrasound and biopsy are 49 the most common utilized techniques [3]. However, many of these techniques have their 50 limitations. Besides, the use of ionizing radiation in the diagnosing process can even 51 develop tumors in the healthy tissues [4]. On the other hand, optical techniques are 52 considered very safe and functional diagnosing tools.53

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Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region‐growing and quasi‐Monte Carlo‐expectation maximization algorithm

Cited by 5 publications

References 18 publications

Brain stroke computed tomography images analysis using image processing: A Review

Brain stroke computed tomography images analysis using image processing: A Review

Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review

Identifying different types of tumors in human brain, breast, and uterus according to their optical properties in the red to near-infrared region

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