MRI Brain Image Enhancement Using an Improved Contrast Enhancement Method

The uncontrollable cells growth in the brain portion is the main reason for cancer deaths nowadays. So, effective detection of brain tumors is more important in the medical field to analyze the tumor portion. Detecting tumors prior and diagnosis of tumors can play a major role in preventing human death due to brain tumors. To detect the tumor portion, many segmentation and classification methods have been proposed. For the effect segmentation process, enhancing brain images is necessary. In this present paper, Magnetic resonance imaging (MRI) brain images have been taken as test images. The proposed enhancement method has two major phases. The first phase contains a regularization process in two steps to equalize the test images' intensities, and the second phase contains a mapping process of two steps to enhance the contrast of the image and remap their intensity values to the natural dynamic range.

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Possibilistic Intuitionistic Fuzzy c-Means Clustering Algorithm for MRI Brain Image Segmentation

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015500165 ◽

2015 ◽

Vol 24 (05) ◽

pp. 1550016 ◽

Cited By ~ 9

Author(s):

Hanuman Verma ◽

R. K. Agrawal

Keyword(s):

Clustering Algorithm ◽

Brain Images ◽

Brain Image ◽

Fuzzy C Means ◽

Mri Brain ◽

Intuitionistic Fuzzy ◽

Fuzzy C Means Clustering ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Mri Brain Images

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov’s intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result.

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Beyond Oncological Hyperthermia: Physically Drivable Magnetic Nanobubbles as Novel Multipurpose Theranostic Carriers in the Central Nervous System

Molecules ◽

10.3390/molecules25092104 ◽

2020 ◽

Vol 25 (9) ◽

pp. 2104 ◽

Cited By ~ 1

Author(s):

Eleonora Ficiarà ◽

Shoeb Anwar Ansari ◽

Monica Argenziano ◽

Luigi Cangemi ◽

Chiara Monge ◽

...

Keyword(s):

Central Nervous System ◽

Nervous System ◽

Brain Tumors ◽

Ad Hoc ◽

Superparamagnetic Iron Oxide ◽

Conventional Radiotherapy ◽

The Central Nervous System ◽

Magnetic Resonance Imaging Mri ◽

The Brain

Magnetic Oxygen-Loaded Nanobubbles (MOLNBs), manufactured by adding Superparamagnetic Iron Oxide Nanoparticles (SPIONs) on the surface of polymeric nanobubbles, are investigated as theranostic carriers for delivering oxygen and chemotherapy to brain tumors. Physicochemical and cyto-toxicological properties and in vitro internalization by human brain microvascular endothelial cells as well as the motion of MOLNBs in a static magnetic field were investigated. MOLNBs are safe oxygen-loaded vectors able to overcome the brain membranes and drivable through the Central Nervous System (CNS) to deliver their cargoes to specific sites of interest. In addition, MOLNBs are monitorable either via Magnetic Resonance Imaging (MRI) or Ultrasound (US) sonography. MOLNBs can find application in targeting brain tumors since they can enhance conventional radiotherapy and deliver chemotherapy being driven by ad hoc tailored magnetic fields under MRI and/or US monitoring.

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Diagnosing RCVS Without the CV

The Neurohospitalist ◽

10.1177/1941874415599576 ◽

2015 ◽

Vol 6 (3) ◽

pp. NP1-NP4 ◽

Cited By ~ 3

Author(s):

Nuri Jacoby ◽

Ulrike Kaunzner ◽

Marc Dinkin ◽

Joseph Safdieh

Keyword(s):

Magnetic Resonance ◽

Resonance Imaging ◽

Multiple Risk Factors ◽

Mri Brain ◽

Cerebral Vasoconstriction ◽

History Of ◽

Magnetic Resonance Imaging Mri ◽

Reversible Encephalopathy ◽

Occipital Lobes ◽

The Brain

This is a case of a 52-year-old man with a past medical history of 2 episodes of coital thunderclap headaches as well as recent cocaine, marijuana, and pseudoephedrine use, who presented with sudden, sharp, posterior headache associated with photophobia and phonophobia. His initial magnetic resonance imaging (MRI) of the brain, magnetic resonance angiography (MRA) of the head, and magnetic resonance venography (MRV) of the head were all normal as well as a normal lumbar puncture. Given the multiple risk factors for reversible cerebral vasoconstriction syndrome (RCVS), the patient was treated for suspected RCVS, despite the normal imaging. Repeat MRI brain 3 days after hospital admission demonstrated confluent white matter T2 hyperintensities most prominent in the occipital lobes, typical of posterior reversible encephalopathy syndrome (PRES). Repeat MRA of the head 1 day after discharge and 4 days after the abnormal MRI brain showed multisegment narrowing of multiple arteries. This case demonstrates that RCVS may present with PRES on MRI brain and also exemplifies the need to treat suspected RCVS even if imaging is normal, as abnormalities in both the MRI and the MRA may be delayed.

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Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch005 ◽

2017 ◽

pp. 115-130

Author(s):

Vijay Kumar ◽

Jitender Kumar Chhabra ◽

Dinesh Kumar

Keyword(s):

Image Segmentation ◽

Brain Images ◽

Brain Image ◽

Clustering Technique ◽

Mri Brain ◽

Gravitational Search ◽

The Brain ◽

Mri Brain Image ◽

Brain Tissues ◽

Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

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A Novel Algorithm for Detection and Classification of Brain Tumors

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026815500066 ◽

2015 ◽

Vol 14 (01) ◽

pp. 1550006

Author(s):

M. C. Jobin Christ ◽

X. Z. Gao ◽

Kai Zenger

Keyword(s):

Brain Tumors ◽

Fuzzy Inference ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Mri Brain ◽

Hierarchical Topology ◽

Inference Systems ◽

Mri Brain Images

Segmentation of an image is the partition or separation of the image into disjoint regions of related features. In clinical practice, magnetic resonance imaging (MRI) is used to differentiate pathologic tissues from normal tissues, especially for brain tumors. The main objective of this paper is to develop a system that can follow a medical technician way of work, considering his experience and knowledge. In this paper, a step by step methodology for the automatic MRI brain tumor segmentation and classification is presented. Initially acquired MRI brain images are preprocessed by the Gaussian filter. After preprocessing, initial segmentation is done by hierarchical topology preserving map (HTPM). From the resultant images, the features are extracted using gray level co-occurrence matrix (GLCM) method, and the same are given as inputs to adaptive neuro fuzzy inference systems (ANFIS) for final segmentation and the classification of brain images into normal or abnormal. In case of abnormal, the MRI brain images are classified as benign subject (tumor without cancerous tissues) or malignant subject (tumor with cancerous tissues). Based on the analysis, it has been discovered that the overall accuracy of classification of our method is above 94%, and F1-score is about 1. The simulation results also show that the proposed approach is a valuable diagnosing technique for the physicians and radiologists to detect the brain tumors.

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Features Determination from Super-Voxels Obtained with Relative Linear Interactive Clustering

Image Processing & Communications ◽

10.1515/ipc-2016-0017 ◽

2016 ◽

Vol 21 (3) ◽

pp. 69-79 ◽

Cited By ~ 1

Author(s):

Abdelkhalek Bakkari ◽

Anna Fabijańska

Keyword(s):

Computational Cost ◽

Brain Images ◽

3D Images ◽

New Approach ◽

Magnetic Resonance Imaging Mri ◽

Ct Brain ◽

Interactive Clustering ◽

The Brain ◽

Low Computational Cost ◽

3D Magnetic Resonance Imaging

Abstract In this paper, the problem of segmentation of 3D Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) brain images is considered. A supervoxel-based segmentation is regarded. In particular, a new approach called Relative Linear Interactive Clustering (RLIC) is introduced. The method, dedicated to image division into super-voxels, is an extension of the Simple Linear Interactive Clustering (SLIC) super-pixels algorithm. During RLIC execution firstly, the cluster centres and the regular grid size are initialized. These are next clustered by Fuzzy C-Means algorithm. Then, the extraction of the super-voxels statistical features is performed. The method contributes with 3D images and serves fully volumetric image segmentation. Five cases are tested demonstrating that our Relative Linear Interactive Clustering (RLIC) is apt to handle huge size of images with a significant accuracy and a low computational cost. The results of applying the suggested method to segmentation of the brain tumour are exposed and discussed.

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Overview of Multi-Modal Brain Tumor MR Image Segmentation

Healthcare ◽

10.3390/healthcare9081051 ◽

2021 ◽

Vol 9 (8) ◽

pp. 1051

Author(s):

Wenyin Zhang ◽

Yong Wu ◽

Bo Yang ◽

Shunbo Hu ◽

Liang Wu ◽

...

Keyword(s):

Image Segmentation ◽

Brain Tumor ◽

Brain Tumors ◽

Brain Diseases ◽

Learning Methods ◽

Brain Images ◽

Advantages And Disadvantages ◽

Segmentation Methods ◽

Magnetic Resonance Imaging Mri ◽

Tumor Mri

The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

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Brain Tumor Detection using Enhanced Convolution Neural Network for MR Images

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b6051.0710221 ◽

2021 ◽

Vol 10 (2) ◽

pp. 10-13

Author(s):

M.B. Bramarambika ◽

◽

M Sesha Shayee ◽

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Brain Tumors ◽

Detection System ◽

Human Life ◽

Classification Problem ◽

Tumor Detection ◽

Convolution Neural Network ◽

Brain Images ◽

The Brain

Brain tumor is a mass that grows unevenly in the brain and directly affects human life. The mass occurs spontaneously because of the tissues surrounding the brain or the skull. There are two types of Brain tumor such as Benign and Malignant. Malignant brain tumors contain cancer cells and grow quickly and spread through to other brain and spine regions as well. Accurate and prompt diagnosis of brain tumors is essential for implementing an effective treatment of this disease. Brain images produced by the Magnetic Resonance Imaging (MRI) technique are a rich source of data for brain tumor diagnosis and treatment in the medical field. Due to the existence of a large number of features compared to the other imaging types. The performance of existing methods is inadequate considering the medical significance of the classification problem. Earlier methods relied on manually delineated tumor regions, prior to classification. This prevented them from being fully automated. The automatic algorithms developed using CNN and its variants could not achieve an influential improvement in performance. In order to overcome such an issue, the proposed one is automatic brain tumor detection system, which is “ Enhanced Convolution Neural Network (CNN) Algorithm for MRI Images” for the detection of brain tumor is useful to detect and classify the Glioma part into low Glioma and high Glioma.

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The Truth, The Whole Truth, and Statistical Parametric Maps

CNS Spectrums ◽

10.1017/s1092852900001322 ◽

2001 ◽

Vol 6 (8) ◽

pp. 644-644

Author(s):

Michael Trimble

Keyword(s):

Resonance Imaging ◽

Repeated Testing ◽

Brain Images ◽

Technical Developments ◽

Positron Emission ◽

Functional Images ◽

Temporal And Spatial ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Parametric Maps

Over 20 years ago, Monte Buchsbaum first presented metabolic brain images belonging to a patient with schizophrenia at the American Psychiatric Association's annual meeting. This was a truly remarkable achievement brought about by the combined skills of the deoxyglucose technique developed by Lou Sokoloff and colleagues, and the advancement of statistical algorithms for the analysis of computerized images. Positron emission tomography (PET) had arrived, but more importantly, the imaging era of neuropsychiatry was dawning. Since then, we have been treated to a glorious array of technical developments which has seen not only coregistration of functional images (ie, PET) with corresponding structure (magnetic resonance imaging [MRI]), but a proliferation of MRI techniques that allow not only temporal and spatial resolutions undreamed of two decades ago, but permit safe, repeated testing of patients, allowing for experiments of considerable sophistication to be designed.Now, at any neuroscience meeting, many presentations are accompanied by brain images, often PETs. However, more and more often now we are seeing one form or another of MRI. Such images are displayed from various angles, adorned with multiple colors, and garnished with institutional logos, confirming (and less regularly refuting) the presenter's hypothesis about how the brain is supposed to work in relation to various cerebral functions.

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