Recognition of brain tumors in MRI images using texture analysis

Elshaikh, Buthayna G.; Garelnabi, Mem; Omer, Hiba; Sulieman, Abdelmoneim; Habeeballa, B.; Tabeidi, Rania A.

doi:10.1016/j.sjbs.2021.01.035

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…Es importante resaltar que ambos métodos clasificaron el tumor presente dentro de la clase de líquido cefalorraquídeo (CSF). Si bien existen métodos más avanzados basados en inteligencia artificial capaces de discernir entre tumores y tejido cerebral (Elshaikh et al 2021), estos métodos están fuera del alcance del tema de este artículo y merecen ser explorados con detenimiento.…”

Section: Discussionunclassified

Comparación de métodos de segmentación automática en Neurociencia

Sosa,

Harper

2023

UCOM sci.

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Cerebral segmentation is essential in the analysis of medical images to identify and differentiate anatomical structures within the brain. Accurate segmentation of cerebrospinal fluid, gray matter, and white matter is crucial for diagnosis, treatment planning, and monitoring neurological disorders. This study presents a comparison of two brain image processing techniques, FSL and DIPY, which are used for segmenting brain tissues. These techniques use intensity-based thresholds to classify image pixels into cerebrospinal fluid, gray matter, and white matter. The aim of this work is to provide a better understanding of automated approaches, thereby enhancing clinical decision-making and the understanding of brain structure and function.

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Section: Discussionunclassified

Comparación de métodos de segmentación automática en Neurociencia

Sosa,

Harper

2023

UCOM sci.

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“…In this step, the DA' initial food sources (the neighborhoods' mass centers) are located using the K-means method. In our case, four clusters are recognized here that stand in for the cerebrospinal fluid or background, grey matter, white matter, and tumor (see Figures 10 and 11) [66,67]. Then, the closest centroid is selected for each point.…”

Section: Initial Da' Food Sources Extraction Using K-mean Algorithmmentioning

confidence: 99%

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique

Darwish,

Abu Shaheen,

Elzoghabi

2023

Bioengineering

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Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.

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“…Brain neoplasms, also known as intracranial tumors, are more commonly affected in all human age classes, older adults, toddlers, and infants. Carcinogenic factors, such as ionizing radiation and the family history of the disease, are two of the most common brain cancer causes [1]. The major problem of brain cancer is the lack of control growth of normal cells, which causes an esteemed uncontrolled growth of tumors, disrupting normal tissues and causing shear stress on cells, eventually killing them.…”

Section: Introductionmentioning

confidence: 99%

“…The major problem of brain cancer is the lack of control growth of normal cells, which causes an esteemed uncontrolled growth of tumors, disrupting normal tissues and causing shear stress on cells, eventually killing them. Early diagnosis is critical for reducing long-term morbidity and mortality rates by reducing life-threatening cancer disease complications and saving appropriate treatment on time [1]. Accordingly, the need for an automatic, quick, and accurate brain tumor recognition algorithm considers essential.…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Structure for Accurately Brain Tumor Recognition

Ata¹,

Yousef²,

Karim³

et al. 2023

Computer Systems Science and Engineering

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Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.

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Recognition of brain tumors in MRI images using texture analysis

Cited by 12 publications

References 15 publications

Comparación de métodos de segmentación automática en Neurociencia

Comparación de métodos de segmentación automática en Neurociencia

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique

An Improved Deep Structure for Accurately Brain Tumor Recognition

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