A Novel Approach for Brain Tumor Detection Using MRI Images

Kader, Isselmou Abd El; Zhang, Shuai; Xu, Guizhi

doi:10.4236/jbise.2016.910b006

Cited by 33 publications

(17 citation statements)

References 3 publications

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“…2016;9(10):44-52. (17) Use of the high-pass median filter and histogram equalization to improve image quality (40) Development of a method for brain extraction on T1-weighted MRI images based on median filter and morphological operations…”

Section: Authors Methodology Resultsmentioning

confidence: 99%

“…Also, we tested using the median filter, a tool commonly used for filtering noise in brain MRI. (17,30) However, results were not superior to those of the proposed method, and the processing time of the algorithm was a downside. In fact, the convolution operation with masks requires great computational effort.…”

Section: ❚ Discussionmentioning

confidence: 96%

“…Histogram equalization is applied to leave the desired areas brighter than the rest of the image, thus facilitating their extraction. (2,(17)(18)(19) The histeq function (20) is used to obtain said result.…”

Section: Histogram Equalizationmentioning

confidence: 99%

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Automatic segmentation of brain tumors in magnetic resonance imaging

Mascarenhas¹,

Júnior²,

Ramos³

2020

Einstein (São Paulo)

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Objective: To develop a computational algorithm applied to magnetic resonance imaging for automatic segmentation of brain tumors. Methods: A total of 130 magnetic resonance images were used in the T1c, T2 and FSPRG T1C sequences and in the axial, sagittal and coronal planes of patients with brain cancer. The algorithms employed contrast correction, histogram normalization and binarization techniques to disconnect adjacent structures from the brain and enhance the region of interest. Automatic segmentation was performed through detection by coordinates and arithmetic mean of the area. Morphological operators were used to eliminate undesirable elements and reconstruct the shape and texture of the tumor. The results were compared with manual segmentations by two radiologists to determine the efficacy of the algorithms implemented. Results: The correlated correspondence between the segmentation obtained and the gold standard was 89.23%. Conclusion: It is possible to locate and define the tumor region automatically with no the need for user interaction, based on two innovative methods to detect brain extreme sites and exclude non-tumor tissues on magnetic resonance images. ❚ RESUMOObjetivo: Desenvolver um algoritmo computacional aplicado a imagens de ressonância magnética, para segmentação automática de tumores cerebrais. Métodos: Foram utilizadas 130 imagens de ressonância magnética nas sequências T1c, T2 e FSPRG T1c e nos planos axial, sagital e coronal de pacientes acometidos com câncer cerebral. Os algoritmos empregaram técnicas de correção de contraste, normalização de histograma e binarização, para desconectar estruturas adjacentes do cérebro e realçar a região de interesse. A segmentação automática foi realizada por meio da detecção por coordenadas e por média aritmética da área. Operadores morfológicos foram utilizados para eliminar elementos indesejáveis e reconstruir a forma e a textura do tumor. Os resultados foram comparados com as segmentações manuais de dois médicos radiologistas, para determinar a eficácia dos algoritmos implementados. Resultados: Os acertos foram de 89,23% na correspondência entre a segmentação obtida e o padrão-ouro. Conclusão: É possível localizar e delimitar a região tumoral de forma automática, sem necessidade de interação com o usuário baseado em dois métodos inovadores de detecção dos extremos do cérebro e de exclusão dos tecidos não tumorais em imagens de ressonância magnética.Descritores: Diagnóstico por imagem; Neoplasias encefálicas; Processamento de imagem assistida por computador; Imagem por ressonância magnética; Simulação por computador How to cite this article: Mascarenhas LR, Ribeiro Júnior AS, Ramos RP. Automatic segmentation of brain tumors in magnetic resonance imaging. einstein (São Paulo). 2020;18:eAO4948. http://dx.

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Section: Authors Methodology Resultsmentioning

confidence: 99%

Section: ❚ Discussionmentioning

confidence: 96%

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Automatic segmentation of brain tumors in magnetic resonance imaging

Mascarenhas¹,

Júnior²,

Ramos³

2020

Einstein (São Paulo)

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“…Isselmou et al applied image enhancement technique histogram equalization method on brain MRI image to highlight the various internal regions of the brain regions. Then, the authors applied morphological operations on the enhanced brain image to highlight the abnormal edge regions in brain MRI images.…”

Section: Literature Surveymentioning

confidence: 99%

Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier

Selvapandian

Karunakaran

2018

Int J Imaging Syst Tech

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Detection of abnormal regions in brain image is complex process due to its similarity between normal and abnormal regions. This article proposes an automated technique for the detection of meningioma tumor using Gradient Boosting Machine Learning (GBML) classification method. This proposed system consists of preprocessing, feature extraction and classification stages. In this article, Grey Level Co occurrence Matrix (GLCM) features, intensity features, and Gray Level Run Length Matrix features are derived from the test brain MRI image. These derived feature set are classified using GBML classification approach. Morphological functions are used to segment the tumor region in classified abnormal brain image. The performance of the proposed system is evaluated on brain MRI images which are obtained from open access data set. The proposed methodology stated in this article achieves 93.46% of sensitivity, 96.54% of specificity, and 97.75% of accuracy with respect to ground truth images. K E Y W O R D S brain image,

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“…The methodology proposed in this paper was not supported by low resolution brain MR images and noised brain MR images. Isselmou et al used thresholding approach to segment the abnormal tissues in brain MR images. Then, morphological operators were applied on the segmented region to improve the segmentation accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Performance analysis of computer aided brain tumor detection system using ANFIS classifier

Rufus

Selvathi²

2017

Int J Imaging Syst Tech

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The abrupt changes in brain cells due to the environmental effects or genetic disorders leads to form the abnormal lesions in brain. These abnormal lesions are combined as mass and known as tumor. The detection of these tumor cells in brain image is a complex task due to the similarities between normal cells and tumor cells. In this paper, an automated brain tumor detection and segmentation methodology is proposed. The proposed method consists of feature extraction, classification and segmentation. In this paper, Grey Level Co‐Occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Law's texture features are used as features. These features are fed to Adaptive Neuro Fuzzy Inference System (ANFIS) classifier as input pattern, which classifies the brain image. Morphological operations are now applied on the classified abnormal brain image to segment the tumor regions. The proposed system achieves 95.07% of sensitivity, 99.84% of specificity and 99.80% of accuracy for tumor segmentation.

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A Novel Approach for Brain Tumor Detection Using MRI Images

Cited by 33 publications

References 3 publications

Automatic segmentation of brain tumors in magnetic resonance imaging

Automatic segmentation of brain tumors in magnetic resonance imaging

Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier

Performance analysis of computer aided brain tumor detection system using ANFIS classifier

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