scholarly journals Brain Tumour Temporal Monitoring of Interval Change Using Digital Image Subtraction Technique

2021 ◽  
Vol 9 ◽  
Author(s):  
Azira Khalil ◽  
Aisyah Rahimi ◽  
Aida Luthfi ◽  
Muhammad Mokhzaini Azizan ◽  
Suresh Chandra Satapathy ◽  
...  
Keyword(s):  
Brain Tumour ◽  
Treatment Plan ◽  
Brain Magnetic Resonance Imaging ◽  
Digital Subtraction ◽  
Subtraction Technique ◽  
Image Subtraction ◽  
Brain Images ◽  
Time Points ◽  
Matlab Programming ◽  
The Brain

A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.

scholarly journals Glioblastomas brain tumour segmentation based on convolutional neural networks

2020 ◽  
Vol 10 (5) ◽  
pp. 4738
Author(s):  
Moh'd Rasoul Al-Hadidi ◽  
Bayan AlSaaidah ◽  
Mohammed Al-Gawagzeh
Keyword(s):  
Brain Tumour ◽  
Breast Tumour ◽  
Classification Systems ◽  
Brain Images ◽  
Tumour Detection ◽  
Tumour Segmentation ◽  
Prediction Rate ◽  
Important Challenge ◽  
The Brain ◽  
Brain Tumour Segmentation

Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.

Severe Head Injury linked to Subsequent Development of Malignant Brain Tumour within a Short Period- A Case Report

2018 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Head Injury ◽  
Brain Tumour ◽  
Severe Head Injury ◽  
Case Reports ◽  
Subsequent Development ◽  
Malignant Brain Tumour ◽  
Tumour Development ◽  
Short Period ◽  
The Brain

There have been a few case reports of head injury leading to brain tumour development in the same region as the brain injury. Here we report a case where the patient suffered a severe head injury with contusion. He recovered clinically with conservative management. Follow up Computed Tomography scan of the brain a month later showed complete resolution of the lesion. He subsequently developed malignant brain tumour in the same region as the original contusion within a very short period of 15 months. Head injury patients need close follow up especially when severe. The link between severity of head injury and malignant brain tumour development needs further evaluation. Role of anti-inflammatory agents for prevention of post traumatic brain tumours needs further exploration.

Neurocomics and Neuroimaging

2018 ◽  
Author(s):  
Jason Tougaw
Keyword(s):  
Brain Research ◽  
Direct Access ◽  
Brain Images ◽  
High Profile ◽  
Graphic Narratives ◽  
Novel Methods ◽  
The Aesthetic ◽  
Cultural Responses ◽  
The Brain ◽  
Neuroimaging Techniques

This chapter examines a small number of recent graphic brain narratives that experiment with novel methods of visualizing the brain—including David B.’s Epileptic, Ellen Forney’s Marbles, and Matteo Farinella and Hana Ros’s Neurocomic. Tougaw argues that these narratives both draw from and challenge cultural responses to high-profile neuroimaging techniques, including PET and fMRI. Graphic narratives are a subcultural genre celebrated for their rebellious aesthetics and emphasis on narratives that challenge mainstream social and political assumptions. Brain scanning technologies are highly specialized tools that have revolutionized brain research and gained considerable mainstream attention. The mainstreaming of these technologies oversimplifies the images they produce, creating a widely held sense that they offer direct access to the brains they visualize. By contrast, graphic narratives put heavy emphasis on the aesthetic process involved in their making of brain images. While careful not to minimize these differences, the chapter argues that key similarities between neurocomics and neuroimaging techniques can be a means for clarifying the roles played by the sciences and the humanities in the cultural laboratory of contemporary neuromania.

scholarly journals Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 2051-2054
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Important Task ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Medical Issues ◽  
Current Scenario ◽  
Exact Position ◽  
The Brain

Medical image processing is an important task in current scenario as more and more humans are diagnosed with various medical issues. Brain tumor (BT) is one of the problems that is increasing at a rapid rate and its early detection is important in increasing the survival rate of humans. Detection of tumor from Magnetic Resonance Image (MRI) of brain is very difficult when done manually and also time consuming. Further the tumors assume different shapes and may be present in any portion of the brain. Hence identification of the tumor poses an important task in the lives of human and it is necessary to identify its exact position in the brain and the affected regions. The proposed algorithm makes use of deep learning concepts for automatic segmentation of the tumor from the MRI brain images. The algorithm is implemented using MATLAB and an accuracy of 99.1% is achieved.

scholarly journals Segmentation of Brain Images by Optimizing Clustering of Convolution Based Features

2021 ◽  
Vol 229 ◽  
pp. 01034
Author(s):  
Vikas Kumar
Keyword(s):  
Brain Tumour ◽  
Brain Tumours ◽  
Normal Brain ◽  
Resonance Imaging ◽  
Comprehensive Overview ◽  
Brain Images ◽  
Non Invasive ◽  
Tumour Segmentation ◽  
Tissue Contrast ◽  
Brain Tumour Segmentation

Brain tumour segmentation aims to separate the various types of tumour tissues like active cells, necrotic core, and edema from normal brain tissues of substantia alba (WM), grey matter (GM), and spinal fluid (CSF). Magnetic Resonance Imaging based brain tumour segmentation studies are attracting more and more attention in recent years thanks to non-invasive imaging and good soft tissue contrast of resonance Imaging (MRI) images. With the event of just about two decades, the ingenious approaches applying computer-aided techniques for segmenting brain tumour are getting more and more mature and coming closer to routine clinical applications. the aim of this paper is to supply a comprehensive overview for MRIbased brain tumour segmentation methods. Firstly, a quick introduction to brain tumours and imaging modalities of brain tumours is given in this proposed research, convolution based optimization. These stepwise step refine the segmentation and improve the classification parameter with the assistance of particle swarmoptimization.

An Evaluation of the Antihistamine Activity of Acrivastine and Its Onset in Human Skin

1992 ◽  
Vol 20 (2) ◽  
pp. 106-111 ◽  
Author(s):  
V K Manna ◽  
P Marks ◽  
J R Gibson
Keyword(s):  
Crossover Study ◽  
Healthy Volunteers ◽  
Human Skin ◽  
Treatment Plan ◽  
Onset Of Action ◽  
Double Blind ◽  
Time Points ◽  
Antihistamine Activity ◽  
Flare Response ◽  
Histamine Challenge

In a double-blind, two-period crossover study, 24 healthy volunteers were evaluated to establish the time of onset of action of activity of acrivastine in suppressing the weal and flare response to intradermally injected histamine. Volunteers received single doses of 8 mg acrivastine and placebo according to a fully randomized, balanced treatment plan. Acrivastine significantly ( P < 0.002) reduced the flare response induced by 0.4 μg histamine challenge 15 min after oral acrivastine dosing when compared with placebo. A significant ( P < 0.001) reduction of the weal response was noted at 25 min, although trends in this direction were already present at earlier time points. Dans d'une étude croisée à deux phase, réalisée en double aveugle et ayant porté sur 24 volontaires sains, on a tenté d'établir le moment du début de l'action de l'acrivastine dans la suppression de la réponse inflammatoire consécutive à l'injection intradermique d'histamine. Les volontaires ont reçu des doses uniques de 8 mg d'acrivastine et de placebo, selon un plan de traitement entièrement randomisé et équilibré. L'acrivastine a réduit significativement ( P < 0,002) la réponse de rubéfaction induite par 0,4 μg d'histamine 15 minutes après l'administration orale d'acrivastine, par rapport au placebo. Une réduction significative ( P < 0,001) de la réponse d'enflure a été notée à 25 minutes, bien qu'une tendance en ce sens ait déjà été observée à un stade plus précoce.

Visualization of immature brain lesions: MR patterns of long-term outcomes. Case reports

2021 ◽  
Vol 2 (2) ◽  
pp. 94-99
Author(s):  
Anatoly V. Anikin ◽  
Milana A. Basargina ◽  
Eugeniya V. Uvakina
Keyword(s):  
White Matter ◽  
Premature Infants ◽  
Periventricular Leukomalacia ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Lesions ◽  
Gestation Period ◽  
Long Term Outcomes ◽  
Immature Brain ◽  
The Brain

The periventricular and deep white matter of the immature brain of premature infants has an increased vulnerability to various, primarily ischemic injuries. The leading mechanism of selective vulnerability of the white matter of the large hemispheres in children with a low gestation period is the lack of formation of adjacent blood circulation zones between the main arteries of the developing brain. Magnetic resonance imaging has a high sensitivity to detect damage to the brain substance, both in the acute period and in the period of long-term outcomes. Periventricular leukomalacia (PVL) is one of the variants of brain damage in premature infants and the most common term in the conclusions of diagnostic doctors (ultrasound, CT, MRI). Considering the pathomorphological criteria, not always detected changes in the white matter of the large hemispheres are PVL. Diffuse (telencephalic) gliosis and diffuse leukomalacia are ordinary and typical variants of damage to the white matter of the large hemispheres in extremely premature infants, with a gestation period of up to 30-32 weeks. In the first variant, atrophic changes predominate with a pronounced decrease in the volume of white matter and a secondary expansion of the lateral ventricles. Diffuse leukomalacia is most often mistaken for PVL, but the localization of the white matter lesion of the large hemispheres is extensive and extends beyond the peri- and paraventricular region. Clinical examples show various variants of primary non-hemorrhagic brain lesions in prematurely born children in the long-term period. The analysis of the revealed changes is carried out, taking into account current data on developing the brain and pathomorphological criteria.

Prediction of Neurological Disorders Using Visual Saliency

2021 ◽  
pp. 1-11
Author(s):  
Sreelakshmi S. ◽  
Anoop V. S.
Keyword(s):  
Neurological Disorders ◽  
Early Stage ◽  
Brain Magnetic Resonance Imaging ◽  
Visual Saliency ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri ◽  
Processing Techniques ◽  
The Brain ◽  
Prompt Diagnosis

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

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

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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