Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

Rajendran, A.; Dhanasekaran, R.

doi:10.15837/ijccc.2012.3.1393

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…In [116], initially, the enhanced probabilistic fuzzy C-means clustering was applied to get the rough estimation of the tumor region. This estimation and cluster centroid is given to the gradient-vector-flow snake model to refine the tumor boundary.…”

Section: Clustering Based Methodsmentioning

confidence: 99%

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Agravat¹,

Raval²

2021

Preprint

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<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

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Section: Clustering Based Methodsmentioning

confidence: 99%

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Agravat¹,

Raval²

2021

Preprint

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“…A variation of the fuzzy clustering technique was also used with a gradient vector flow snake model in order to segment the brain image [5]. N. Noreen et al have integrated image segmented by fuzzy c-means and the image transformed by Wavelet and have later enhanced the edges using Kirch's mask [6].…”

Section: Utility Of Mri Imagesmentioning

confidence: 99%

Automated Detection and Extraction of Brain Tumor from MRI Images

Tirpude¹,

Welekar²

2013

IJCA

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Image segmentation algorithms and techniques find its applications in a wide number of domains. Segmentation of brain tumor and overall internal structure of the brain is one of the main applications in the field of medical imaging. Magnetic resonance imaging (MRI) technique is one of the many imaging modalities that are available to scan and capture the internal soft tissue structures of the body. In this paper, proposed technique has been given to extract the tumor portion, successfully demarcate the tumor boundary, locate the tumor with a bounding circle and to diagnose whether the tumor is present or absent. A fuzzy clustering-based technique is proposed which helps to study & analyze the intricate structure of the brain, hence can be used as a visual analysis and a study tool. General TermsImage Processing, Pattern Recognition Keywords MRI, Magnetic resonance imaging, image segmentation, fuzzy clustering, thresholding INTRODUCTIONMagnetic resonance imaging (MRI) technique is one of the many available imaging modalities like CT-Scan, Mammography, and X-Ray. It provides visual details about the anatomy and the overall structure of the brain. An MRI scan can be used to study the supply of blood inside the brain. Hence MRI technology becomes an important tool for detecting abnormality, tracking the progress or growth of the disease and for diagnosis too. Processing digital images by means of a digital computer comprises digital image processing domain [1].Brain tumors are caused due to abnormal, uncontrolled growth of cells. Primary tumors are those that originate in the brain. Secondary tumors are those that originate in some other part of the body, finally reaching the brain through the process of metastasis.The symptoms of brain tumors include headache, nausea, vomiting, personality and behavioral changes, memory loss, sensory disturbance, weakness, numbness [2]. MRI ScanMRI is a fairly new technique that has been used since the beginning of the 1980s. The MRI scanner uses magnetic and radio waves to create pictures of tissues, organs and other structures within the body, which can then be viewed on a computer. There is no exposure to X-rays or any other damaging forms of radiation in MRI.The pictures produced by an MRI scan are better in displaying fine details and therefore are of higher diagnostic quality when compared to more frequently used X-ray scanners for example. Utility of MRI ImagesUsing an MRI scanner, it is possible to make pictures of almost all the tissue in the body. The tissue that has the least hydrogen atoms (such as bones) turns out dark, while the tissue that has many hydrogen atoms (such as fatty tissue) looks much brighter.By changing the timing of the radio wave pulses, it is possible to gain information about the different types of tissues that are present.An MRI of the brain and spinal cord can be performed to look at different abnormalities, as it can provide clear images of these structures in spite of being surrounded by bone tissue. Changes within the tissues of brain, ...

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“…Many skull-stripping algorithms were developed, and extensive work was done in this area, but a standardized solution has not been proposed yet [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Automatic and manual segmentations are the process of partitioning the image into distinct regions.…”

Section: Introductionmentioning

confidence: 99%

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images

2015

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This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

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Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

Cited by 19 publications

References 20 publications

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Automated Detection and Extraction of Brain Tumor from MRI Images

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images

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