A Survey of MRI Segmentation Techniques for Brain Tumor Studies

Udupa, Prajna; Vishwakarma, Sarvesh

doi:10.9756/bijaip.10467

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…One of the segmentation approaches is thresholding, which compares pixel intensities to one or more intensity thresholds. Threshold-based segmentation divides an image into multiple regions depending on the intensity of each pixel [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation from Magnetic Resonance Image using Optimized Thresholded Difference Algorithm and Rough Set

Toufiq¹,

Sagheer²,

Veisi³

2022

TEM Journal

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This research presents an effective method for automatically segmenting brain tumors using the proposed Optimized Thresholded Difference (OTD) and Rough Set Theory (RST). The tumor area is determined using the proposed two-level segmentation algorithm. The first level i.e., an overlay image is created, which is the intensity average of all the pixels of the brain area that were segmented in the initial stage. Then the second level, in which the process of the thresholded difference is applied between the brain area and the overlay image depending on the specified threshold. Features are extracted from the segmented images using the Gray-Level Co-occurrence Matrix (GLCM). To improve performance, an RST is employed with the extracted features. The completely automated methodology is validated using Figshare open dataset.

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Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation from Magnetic Resonance Image using Optimized Thresholded Difference Algorithm and Rough Set

Toufiq¹,

Sagheer²,

Veisi³

2022

TEM Journal

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“…Hence, research on accurate automatic segmentation methods of brain glioma has been a long-time goal of medical image processing. However, gliomas may appear in any position of the brain, with varied shape, appearance, and size, making it a challenge to segment gliomas automatically and accurately (Udupa and Vishwakarma, 2016).…”

Section: Introductionmentioning

confidence: 99%

3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks

Zhang

Chen

et al. 2021

J. Zhejiang Univ. Sci. B

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To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2D convolutional neural networks (2D-CNNs). In order to combine the lowlevel features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery (FLAIR) modal image and the T2-weighted (T2) modal image to enhance the edema section. For the loss function of network training, we improved the cross-entropy loss function to effectively avoid network over-fitting. On the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) datasets, our method achieves dice similarity coefficient values of 0.84, 0.82, and 0.83 on the BraTS2018 training; 0.82, 0.85, and 0.83 on the BraTS2018 validation; and 0.81, 0.78, and 0.83 on the BraTS2013 testing in terms of whole tumors, tumor cores, and enhancing cores, respectively. Experimental results showed that the proposed method achieved promising accuracy and fast processing, demonstrating good potential for clinical medicine.

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Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice Crop

Vishwakarma

Chilwal

2023

Mathematics and Computer Science Volume 2

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A Survey of MRI Segmentation Techniques for Brain Tumor Studies

Abstract: Abstract---

Cited by 3 publications

References 29 publications

Brain Tumor Segmentation from Magnetic Resonance Image using Optimized Thresholded Difference Algorithm and Rough Set

Brain Tumor Segmentation from Magnetic Resonance Image using Optimized Thresholded Difference Algorithm and Rough Set

3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks

Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice Crop

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