Advance computer analysis of magnetic resonance imaging (MRI) for early brain tumor detection

Mittal, Neetu; Tayal, Satyam

doi:10.1080/00207454.2020.1750390

Cited by 13 publications

(11 citation statements)

References 24 publications

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“…In this process, we should determine the pathology of interest in MRI images that needs some parameters to detect ROI. [ 17 18 ] The outstanding advantage of our study was no need for applying ROI. The time used in our study was 1.5 s. This value can change based on the power of the computer central processing unit.…”

Section: Discussionmentioning

confidence: 99%

“…The results demonstrated that watershed segmentation has the potential to be used as a screening method for meniscal injuries and diseases, with the aim of improving care. [ 17 18 19 20 ] We could not find an article that has used this algorithm for the detection of Baker's cyst in MRI. Moreover, due to more accuracy detection of lesions and delineation lymphomas, a nominated algorithm was performed in mammography and computed tomography.…”

Section: Discussionmentioning

confidence: 99%

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Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Ghaderi

Ghaznavi

2022

J Med Signals Sens

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Nowadays, magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee's cyst (especially Baker) segmentation is one of the novel research areas. There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of Baker's cyst in the knee's joint MRI sagittal and axial T2-weighted images. The performance of this algorithm was investigated, and the results showed that in a short time Baker's cyst can be clearly extracted from original images in axial and sagittal planes. The marker-controlled watershed segmentation was able to detect Baker's cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MRI pathologies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Ghaderi

Ghaznavi

2022

J Med Signals Sens

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“…To decrease these errors, some studies used the region of interest (ROI) that is an external marker used to evaluate a point or background signal; for example, while utilizing this algorithm for examination meniscus tearing and malignant lesion in the breast. In this process, we should determine the pathology of interest in MR images that need some parameters to detect ROI [17,19]. The outstanding advantage of our study was no need for appling ROI.…”

Section: Discussionmentioning

confidence: 99%

“…Results demonstrated that watershed segmentation has the potential to be used as a screening method for meniscal injuries and diseases, with the aim of improving care. [17][18][19][20]. We could not nd an article that has used this algorithm for the detection of baker's cyst in MRI.…”

Section: Discussionmentioning

confidence: 99%

Using marker-controlled watershed transform to detect baker’s cyst in MRI images: a pilot study

Ghaderi

Ghaznavi

2021

Preprint

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Introduction: Nowadays, Magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee’s cyst (especially baker) segmentation is one of the novel research areas.Material and Method: There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of baker’s cyst in the knee’s joint MRI sagittal and axial T2-weighted images.Results: The performance of this algorithm was investigated, and the results showed that in a short time baker’s cyst can be clearly extracted from original images in axial and sagittal planes.Conclusion: The marker-controlled watershed segmentation was able to detect baker’s cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MR images.

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“…In addition, the brain tumor is said to be one of the lethal classes of cancers. 1 The brain tumors increase the mortality between children and adults which causes a higher burden on the healthcare system.…”

Section: Introductionmentioning

confidence: 99%

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

Pandi¹,

Senthilselvi

Maragatharajan

et al. 2022

Concurrency and Computation

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Image registration is one of the image processing techniques that align more than two images of a similar scene captured under different perspectives at different intervals of time. In demographical research, the image registration process assists to study differences in the structure of brain tissue. Due to enhanced technological advancements, numerous image registration methods have been established. On the other hand, these traditional techniques face few real-time challenges while processing huge input data. In addition to this, uncertainty analysis becomes a crucial step in medical applications which is utilized to judge whether the registration result is valuable or not.The high percentage of uncertainty than the threshold makes the registration result abnormal. Therefore, to conquer such circumstances, this research work proposed a modified spine-kernelled chirplet transform (MCST) based optimal Self-Adaptive Deep Neural Network (SADNN) which focuses mainly on enhancing registration accuracy by reducing the uncertainties of registration results. The experimental analysis is conducted and from the evaluation results, the proposed MCST-based optimal SADNN technique outperforms existing techniques in terms of accuracy, specificity, sensitivity, F-measure, and DICE values. Moreover, the proposed method achieves 97.2% accuracy for accurate image registration.

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Advance computer analysis of magnetic resonance imaging (MRI) for early brain tumor detection

Cited by 13 publications

References 24 publications

Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Using marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study

Using marker-controlled watershed transform to detect baker’s cyst in MRI images: a pilot study

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

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