Brain Tumor Segmentation Using K-means–FCM Hybrid Technique

Patel, Vaibhavi; Kapdi, Rupal

doi:10.1007/978-981-10-7386-1_30

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…In similar studies, authors obtained a HR of 86% and MR of 0.79, which was considered satisfactory. (24)(25)(26) In this study, the HR was 89.23% and the MR was 0.70, i.e., quantitatively similar to those of previous authors, which indicates good accuracy of the proposed segmentation method. The best HR results were obtained for T2 and FSPGR-T1c sequences.…”

Section: Comparative Performance Analysis Of Automatic Segmentationsupporting

confidence: 89%

“…Quantitative results of the automatic segmentation were calculated by comparison between the gold standard and the proposed segmentation method. The metrics commonly used in the literature (24)(25)(26) to assess performance are the hit rate (HR), calculated by the number of true positive (TP) of the method compared with the gold standard; and the matching rate (MR), based on the number of false positive results (FP). In the cited studies, the HR and MR are respectively defined as:…”

Section: Evaluation Of Resultsmentioning

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: Comparative Performance Analysis Of Automatic Segmentationsupporting

confidence: 89%

Section: Evaluation Of Resultsmentioning

confidence: 99%

Automatic segmentation of brain tumors in magnetic resonance imaging

Mascarenhas¹,

Júnior²,

Ramos³

2020

Einstein (São Paulo)

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A survey of methods for brain tumor segmentation-based MRI images

Mohammed

Garouani

Jellouli

2023

Journal of Computational Design and Engineering

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Brain imaging techniques play an important role in determining the causes of brain cell injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge benefits in improving treatment possibilities and avoiding any potential complications that may occur to the patient. Recently, brain tumor segmentation has become a common task in medical image analysis due to its efficacy in diagnosing the type, size, and location of the tumor in automatic methods. Several researches have developed new methods in order to obtain the best results in brain tumor segmentation, including using deep learning techniques such as the convolutional neural network (CNN). The goal of this survey is to present a brief overview of MRI modalities and discuss common methods of brain tumor segmentation from MRI images, including brain tumor segmentation using deep learning techniques, as well as the most important contributions in this field, which have shown significant improvements in recent years. Finally, we focused in summary on the building blocks of the convolutional neural network (CNN) algorithms used for image segmentation. the entire survey methodology, it has been observed that hybrid techniques and CNN-based segmentation are more effective for brain tumor segmentation from MRI images.

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Computer Aided Diagnosis for Brain Tumor Segmentation using Fine Tuned Convolutional Neural Network

Sunsuhi¹,

S²

2022

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)

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Brain Tumor Segmentation Using K-means–FCM Hybrid Technique

Cited by 7 publications

References 4 publications

Automatic segmentation of brain tumors in magnetic resonance imaging

Automatic segmentation of brain tumors in magnetic resonance imaging

A survey of methods for brain tumor segmentation-based MRI images

Computer Aided Diagnosis for Brain Tumor Segmentation using Fine Tuned Convolutional Neural Network

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