Automatic segmentation of brain tumors in magnetic resonance imaging

Mascarenhas, Layse Ribeiro; Júnior, Audenor Dos Santos Ribeiro; Ramos, Renato Moran

doi:10.31744/einstein_journal/2020ao4948

Cited by 5 publications

(2 citation statements)

References 30 publications

(32 reference statements)

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“…In 2019, Mascarenhas et al [21] introduced a histogram normalization, contrast correction and binarization strategy for decoupling nearby structures from the brain. Besides, they enhancing the region of interest of brain tumors.…”

Section: Related Workmentioning

confidence: 99%

Automated brain tumor detection of MRI image based on hybrid image processing techniques

2022

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Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper, an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.

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Section: Related Workmentioning

confidence: 99%

Automated brain tumor detection of MRI image based on hybrid image processing techniques

2022

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“…As mentioned above, segmentation is approached from conventional image processing techniques. For example, Mascarenhas et al [23] define a histogram equalization method, intensity adjustment, binarization, and segmentation through the brain region's coordinates. On the other hand, in a more robust approach, Chen et al [24] use a support vector machine and an extended Kalman filter, achieving close to 98% accuracy.…”

Section: Introductionmentioning

confidence: 99%

CrossTransUnet: A New Computationally Inexpensive Tumor Segmentation Model for Brain MRI

2023

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Brain tumors are usually fatal diseases with low life expectancies due to the organs they affect, even if the tumors are benign. Diagnosis and treatment of these tumors are challenging tasks, even for experienced physicians and experts, due to the heterogeneity of tumor cells. In recent years, advances in deep learning (DL) methods have been integrated to aid in the diagnosis, detection, and segmentation of brain neoplasms. However, segmentation is a computationally expensive process, typically based on convolutional neural networks (CNNs) in the UNet framework. While UNet has shown promising results, new models and developments can be incorporated into the conventional architecture to improve performance. In this research, we propose three new, computationally inexpensive, segmentation networks inspired by Transformers. These networks are designed in a 4-stage deep encoder-decoder structure and implement our new cross-attention model, along with separable convolution layers, to avoid the loss of dimensionality of the activation maps and reduce the computational cost of the models while maintaining high segmentation performance. The new attention model is integrated in different configurations by modifying the transition layers, encoder, and decoder blocks. The proposed networks are evaluated against the classical UNet network, showing that our networks have differences of up to an order of magnitude in the number of training parameters. Additionally, one of the models outperforms UNet, achieving training in significantly less time and with a Dice Similarity Coefficient (DSC) of up to 94%, ensuring high effectiveness in brain tumor segmentation.

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Application of Digital Image Processing Techniques for Segmentation of Meningiomas in Magnetic Resonance Images

de Oliveira,

Dantas,

Vilar

et al. 2024

IFMBE Proceedings

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Automatic segmentation of brain tumors in magnetic resonance imaging

Cited by 5 publications

References 30 publications

Automated brain tumor detection of MRI image based on hybrid image processing techniques

Automated brain tumor detection of MRI image based on hybrid image processing techniques

CrossTransUnet: A New Computationally Inexpensive Tumor Segmentation Model for Brain MRI

Application of Digital Image Processing Techniques for Segmentation of Meningiomas in Magnetic Resonance Images

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