Multi Segmentation Method for Hemorraghic Detection

Nugroho, Andi Kurniawan; Putranto, Terawan Agus; Purnama, I Ketut Eddy; Purnomo, Mauridhi Hery

doi:10.1109/icoias.2018.8494039

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Watershed segmentation is a gradient-based segmentation technique. In this study, watershed segmentation locates watersheds and watershed ridges by assigning intensity levels as surfaces, high intensity to bright pixels and low intensity to dark pixels [24]. Based on the edge detection method, the magnitude of the gradient is calculated from the foreground and background markings of the image.…”

Section: Marker-controlled Watershedmentioning

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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Section: Marker-controlled Watershedmentioning

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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“…A multiple segmentation of brain hemorrhage using CT imaging has been established by Nugroho et al [20]. The investigation over diverse methodologies particularly contour and water segmentation, Otsu methodology and so on using the value of HU (House-field unit) has been carried out.…”

Section: Introductionmentioning

confidence: 99%

Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

Shijitha¹,

Karthigaikumar²,

Paul³

2022

Computers, Materials &Amp; Continua

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Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction. Precise segmentation of brain hemorrhage is crucial, so an enhanced segmentation is carried out in this research work. The brain image of various patients has taken using an MRI scanner by the utilization of T1, T2, and FLAIR sequence. This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet (multires UNet) through morphological operations. It is hard to precisely segment the brain lesions to extract the existing region of stroke. This crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke lesions. The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95% compared with the existing segmentation techniques such as U-net++, ResNet, Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced outcome. The performance measure of the proposed methodology acquires an improved accuracy, precision ratio, sensitivity, and specificity rate of 0.07%, 0.04%, 0.04%, and 0.05% in comparison to U-net, ResNet, Multires UNET and 3D-ResU-Net techniques respectively.

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“…Brain imaging is one of the most significant techniques for the assessment of patients with ischemic stroke through Computerized Tomography (CT) scan and Magnetic Resonance Imaging (MRI). CT scan is more common because it is faster and cheaper, whereas MR has a much higher sensitivity of ischemic lesions [2]. Expert identification is still a common approach for studying stroke lesions on MRI images, as it is used to measure the size, shape, and volume of stroke lesions.…”

Section: Introductionmentioning

confidence: 99%

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

Nugroho

Nugraheni

Putranto

et al. 2022

EMITTER Int'l J. of Engin. Technol.

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When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.

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Multi Segmentation Method for Hemorraghic Detection

Cited by 5 publications

References 6 publications

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

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

Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

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