Medical imaging is expensive and very much sophisticated because of proprietary software and expert personalities. This paper introduces an inexpensive, user friendly general-purpose image processing tool and visualization program specifically designed in MATLAB to detect much of the brain disorders as early as possible. The application provides clinical and quantitative analysis of medical images. Minute structural difference of brain gradually results in major disorders such as schizophrenia, Epilepsy, inherited speech and language disorder, Alzheimer's dementia etc. Here the main focusing is given to diagnose the disease related to the brain and its psychic nature (Alzheimer’s disease).
Brain imaging techniques allow researchers and doctors to view the brain without invasive surgery. There are several accepted imaging techniques used in research and hospitals worldwide, including brain lesioning, brain staining, and various brain imaging methods. Brain imaging techniques like MRI, PET, CAT, EEG, DOI, and fMRI non-invasively measure brain structure and function by detecting changes in blood flow, oxygen use, electric fields or other signals during mental activities.
PET CT combines functional imaging using positron emission tomography (PET) with anatomical imaging using computed tomography (CT). PET detects gamma rays emitted by radiotracers administered to the patient to construct 3D images showing metabolic or biochemical processes. CT provides detailed anatomical images for context. The document discusses the principles and components of PET CT scanning, including radiotracer production and synthesis, scanner design using detector rings, coincidence detection, data acquisition and reconstruction to produce diagnostic images.
IRJET - Detection for Alzheimer’s Disease using Image ProcessingIRJET Journal
This document discusses a study on detecting Alzheimer's disease from MRI scans using image processing techniques in MATLAB. It begins with an introduction to Alzheimer's and digital medical imaging. The proposed system takes MRI scans as input, performs preprocessing like interpolation and segmentation. The region of interest (hippocampus and ventricles) is extracted using watershed algorithm. Pixels are classified as black or white based on intensity and the ratio is used to classify the patient as healthy, mild cognitive impairment, or Alzheimer's. The goal is to detect Alzheimer's at early stages from subtle differences in brain structure visible in MRI scans.
This document presents a method for classifying MRI brain images using a neuro-fuzzy model. It discusses extracting textural features from MRI images using principal component analysis for dimensionality reduction. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the neuro-fuzzy classifier to classify images as normal or abnormal based on the extracted features. The neuro-fuzzy model combines the learning ability of neural networks with the advantages of fuzzy logic rule-based systems to accurately classify MRI brain images.
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
IRJET- Brain Tumor Detection and Classification with Feed Forward Back Propag...IRJET Journal
This document presents a method for detecting and classifying brain tumors in MRI images using a feed forward back propagation neural network. It first preprocesses MRI images by dividing them into blocks and applying Haar transforms for noise removal and edge preservation. Statistical, GLCM, morphological and edge features are then extracted from each block. These features are used to identify abnormal areas. The blocks are then classified as normal or tumor using a feed forward back propagation neural network, which can model nonlinear relationships and is trained to reduce error rates. The method achieves 98% classification accuracy on a benchmark MRI dataset. It results in high accuracy tumor detection with less iterations, reducing computation time compared to previous methods.
This document proposes a method for early detection of Alzheimer's disease using image processing techniques on brain MRI scans. It involves preprocessing MRI images, identifying regions of interest like the hippocampus and ventricles, segmenting images using techniques like thresholding and watershed segmentation to classify pixels as healthy or damaged tissue, and analyzing features like brain atrophy and enlarged ventricles to classify subjects as healthy, mild cognitive impairment, or Alzheimer's disease. The method was tested on 12 MRI samples and achieved 90% accuracy in detection. Future work could involve applying machine learning methods like neural networks to the image analysis for more accurate detection of the disease.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Brain imaging techniques allow researchers and doctors to view the brain without invasive surgery. There are several accepted imaging techniques used in research and hospitals worldwide, including brain lesioning, brain staining, and various brain imaging methods. Brain imaging techniques like MRI, PET, CAT, EEG, DOI, and fMRI non-invasively measure brain structure and function by detecting changes in blood flow, oxygen use, electric fields or other signals during mental activities.
PET CT combines functional imaging using positron emission tomography (PET) with anatomical imaging using computed tomography (CT). PET detects gamma rays emitted by radiotracers administered to the patient to construct 3D images showing metabolic or biochemical processes. CT provides detailed anatomical images for context. The document discusses the principles and components of PET CT scanning, including radiotracer production and synthesis, scanner design using detector rings, coincidence detection, data acquisition and reconstruction to produce diagnostic images.
IRJET - Detection for Alzheimer’s Disease using Image ProcessingIRJET Journal
This document discusses a study on detecting Alzheimer's disease from MRI scans using image processing techniques in MATLAB. It begins with an introduction to Alzheimer's and digital medical imaging. The proposed system takes MRI scans as input, performs preprocessing like interpolation and segmentation. The region of interest (hippocampus and ventricles) is extracted using watershed algorithm. Pixels are classified as black or white based on intensity and the ratio is used to classify the patient as healthy, mild cognitive impairment, or Alzheimer's. The goal is to detect Alzheimer's at early stages from subtle differences in brain structure visible in MRI scans.
This document presents a method for classifying MRI brain images using a neuro-fuzzy model. It discusses extracting textural features from MRI images using principal component analysis for dimensionality reduction. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the neuro-fuzzy classifier to classify images as normal or abnormal based on the extracted features. The neuro-fuzzy model combines the learning ability of neural networks with the advantages of fuzzy logic rule-based systems to accurately classify MRI brain images.
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
IRJET- Brain Tumor Detection and Classification with Feed Forward Back Propag...IRJET Journal
This document presents a method for detecting and classifying brain tumors in MRI images using a feed forward back propagation neural network. It first preprocesses MRI images by dividing them into blocks and applying Haar transforms for noise removal and edge preservation. Statistical, GLCM, morphological and edge features are then extracted from each block. These features are used to identify abnormal areas. The blocks are then classified as normal or tumor using a feed forward back propagation neural network, which can model nonlinear relationships and is trained to reduce error rates. The method achieves 98% classification accuracy on a benchmark MRI dataset. It results in high accuracy tumor detection with less iterations, reducing computation time compared to previous methods.
This document proposes a method for early detection of Alzheimer's disease using image processing techniques on brain MRI scans. It involves preprocessing MRI images, identifying regions of interest like the hippocampus and ventricles, segmenting images using techniques like thresholding and watershed segmentation to classify pixels as healthy or damaged tissue, and analyzing features like brain atrophy and enlarged ventricles to classify subjects as healthy, mild cognitive impairment, or Alzheimer's disease. The method was tested on 12 MRI samples and achieved 90% accuracy in detection. Future work could involve applying machine learning methods like neural networks to the image analysis for more accurate detection of the disease.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
This document summarizes a research paper that introduces a user-friendly program developed in MATLAB for early detection of Alzheimer's disease using brain MRI scans. The program provides quantitative and clinical analysis of digital medical images by identifying the cerebral cortex region of the brain and measuring the cavity area in brain folds, known as sulci. This is done using bi-cubic interpolation for more accurate results than other interpolation techniques. The paper also provides background on Alzheimer's disease, describing it as a neurodegenerative disorder causing memory loss and cognitive decline due to brain cell death. Common imaging techniques used to study the brain like MRI, CT, PET, EEG and MEG are also summarized.
Combining Optical Brain Imaging and Physiological Signals to Study Cognitive ...InsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and fNIR Devices, Dr. Hasan Ayaz, Dr. Kurtulus Izzetoglu and Frazer Findlay present new research capabilities enabled through the integration of optical brain imaging technology and physiological recording systems.
Key topics covered during this webinar include physiological and physical principles of optical brain imaging, theory of operation, hardware and software integration, essential fNIR signal processing (demonstrated using fnirSoft analysis software), common field applications of fNIR imaging, why and how researchers can measure physiological data such as EDA, HR and ECG and acquistion procedures for co-registration of fNIR data and physiological monitoring signals using AcqKnowledge data acquisition and analysis software.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
This document compares the performance of three widely-used brain segmentation software packages (SPM, FSL, and Brainsuite) at segmenting brain MRI images into gray matter, white matter, and cerebrospinal fluid. It evaluates the packages using both simulated Brainweb MRI images and real MRI images from the IBSR dataset. For each package, it describes the segmentation methods and preprocessing steps. The accuracy of the segmented tissues from each package is assessed by calculating the similarity between the automated segmentations and corresponding ground truth segmentations. The results show variations in segmentation performance between the different packages and methods.
Presented at International Workshop on
Frontiers of Neuroengineering,
Brain-machine Interfaces
& Neural Prostheses
Zhejiang University, Hangzhou, China
March 29, 2011
IRJET-A Review on Brain Tumor Detection using BFCFCM AlgorithmIRJET Journal
The document presents a review of brain tumor detection using the BFCFCM clustering algorithm. It begins with an introduction to brain tumors and MRI imaging. It then reviews several existing techniques for brain tumor detection using artificial neural networks, linear discriminant analysis, neuro-fuzzy systems, and region growing segmentation with watershed algorithms. The document proposes a method using pre-processing, skull masking, segmentation with an advanced fuzzy c-means algorithm, feature extraction through thresholding, and an SVM classifier. Segmentation partitions the MRI image into regions/objects of interest like the tumor. Feature extraction analyzes the segmented regions to characterize the tumor for classification.
This document discusses using functional near-infrared spectroscopy (fNIR) neuroimaging to study learning in natural environments. It proposes establishing a neuroimaging facility at Drexel University that would use portable fNIR to non-invasively monitor brain activity during learning, problem-solving, training and other cognitive tasks. This could provide insights into the learning process and help optimize educational approaches. The facility would bring together experts from different fields and allow collaborative research on applications of fNIR including training, cognitive workload assessment, brain-computer interfaces and clinical areas.
Neurorobotics and Advances in rehabilitation engineeringBhaskarBorgohain4
Advances in robotics,mechatronics,cyborgs and disruptive technologies for heptics, brain machine interfaces and neurorobotics are bringing a sea change to the field of rehabilitation engineering. Carbon fibre cheetah blades, Bionic arms, c legs are helping the amputees to the extent that amputees can now run in competitive sports at the level of summer Olympics.
Feature Extraction and Classification of NIRS DataPritam Mondal
A thesis paper submitted to the department of Electronics and Communication
Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh, in
partial fulfillment of the requirement for the degree of “Bachelor of Science” in Electronics
and Communication Engineering
Magnetoencephalography an emerging biological marker for neurodegenerative an...Adonis Sfera, MD
Magnetoencephalography (MEG) is a technique that measures magnetic fields produced by electrical currents in the brain to map functional areas with great temporal resolution. MEG detects alterations in brain structure correlated with changes in function as seen with MRI. A new combined MEG-MRI device can simultaneously record ultra-low-field MRI and MEG to unprecedentedly locate brain activity. MEG has accurately diagnosed PTSD at 90% by identifying patterns unique to sufferers, and combining with diffusion tensor imaging may further improve diagnostic accuracy.
This document provides information about an upcoming "Multimodal Imaging in Neurosciences" course, including:
1) Dates and topics for upcoming lectures, as well as details about a final test on basic imaging techniques.
2) An overview of various neuroimaging modalities like CT, MRI, PET, and their applications.
3) A brief history of the development of high-intensity focused ultrasound (HIFU) technology from the 1880s to present.
There are over 600 neurological disorders that can cause dysfunction in the brain, spine or nerves. Neuroprosthetics are implantable devices that can replace or support lost neurological function. There are three main types: sensory neuroprosthetics like cochlear implants that restore hearing; motor neuroprosthetics that help control limb movement; and cognitive prosthetics that treat conditions like Alzheimer's. While neuroprosthetics show promise, they also carry risks like infections from brain surgery. Regulations vary depending on the device, but most require clinical trials to demonstrate safety and effectiveness before approval. Further research and international guidelines could help advance this emerging field.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
The document discusses brain imaging technologies such as MRI, fMRI, and emerging techniques. It describes how these methods can be used to image individual neurons, neuronal networks, and the whole brain. Examples are given of how fMRI has been used to study basic brain functions and diagnose neurological disorders. The document also suggests ways for non-experts to access and analyze brain imaging data through open access repositories and analysis tools.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
Let’s master the digital toolkit to harness lifelong neuroplasticitySharpBrains
Four leading pioneers of applied neuroplasticity helped us navigate best practices to harness most promising non-invasive neurotechnologies, such as cognitive training, mindfulness apps, EEG and virtual/ augmented reality.
--Chair: Linda Raines, CEO of the Mental Health Association of Maryland
--Dr. Michael Merzenich, winner of the 2016 Kavli Prize in Neuroscience
--Dr. Judson Brewer, Founder & Research Lead of Claritas Mindsciences
--Tan Le, CEO of Emotiv
--Dr. Andrea Serino, Head of Neuroscience at MindMaze
Learn more at sharpbrains.com
Kohut Literature Review fMRI correctionsJulie Kohut
Functional magnetic resonance imaging (fMRI) is a noninvasive brain mapping technique that measures blood flow and oxygen levels to detect brain activity. fMRI is used clinically to study brain recovery after stroke, pre-surgical planning by locating functional areas near tumors, and identifying seizure onset zones. It has advantages over other imaging methods as it provides detailed brain images without radiation exposure. While offering insights, fMRI also has limitations such as sensitivity to patient movement and drugs affecting blood flow measurements. Continued research aims to improve fMRI for applications in neurology, psychology, marketing and developing lie detection techniques.
Neurobionics and robotic neurorehabilitationsNeurologyKota
This document discusses neurobionics, robotic neurorehabilitation, and applications of neurobionics. It summarizes key areas including: (1) neurobionics aims to integrate electronics with the nervous system to repair or substitute impaired functions, (2) robotic neurorehabilitation uses robots to assist in rehabilitation processes, and (3) applications of neurobionics include motor interfaces like robotic arms, sensory interfaces like cochlear implants, and treating conditions like epilepsy and Parkinson's disease.
IRJET- Precision of Lead-Point with Support Vector Machine based Microelectro...IRJET Journal
This document discusses using microelectrode recordings (MER) with support vector machine learning to study the function of subthalamic nucleus (STN) neurons in the human brain during deep brain stimulation for Parkinson's disease. The study aims to improve the signal-to-noise ratio of MER signals and precisely identify the location of the STN to ensure safety and efficacy of deep brain stimulation chip implantation. MER is found to confirm the presence of abnormal STN neurons and clear positioning of the microchip electrode in the target area. Access to functional data from neurons deep in the brain using MER may help further elucidate cryptic aspects of brain function.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
This document reviews various techniques for detecting brain tumors in MRI images. It begins with an introduction to MRI and brain tumors. It then discusses several common methods for feature extraction (such as texture-based features using gray-level co-occurrence matrix) and classification (including neural networks, fuzzy c-means, k-nearest neighbors, support vector machines) that have been used for automated brain tumor detection. The document reviews 10 previous studies that detected brain tumors using techniques like segmentation, principal component analysis, probabilistic neural networks, and self-organizing maps. It then provides more detail on feature extraction methods, focusing on texture-based features.
This document summarizes a research paper that introduces a user-friendly program developed in MATLAB for early detection of Alzheimer's disease using brain MRI scans. The program provides quantitative and clinical analysis of digital medical images by identifying the cerebral cortex region of the brain and measuring the cavity area in brain folds, known as sulci. This is done using bi-cubic interpolation for more accurate results than other interpolation techniques. The paper also provides background on Alzheimer's disease, describing it as a neurodegenerative disorder causing memory loss and cognitive decline due to brain cell death. Common imaging techniques used to study the brain like MRI, CT, PET, EEG and MEG are also summarized.
Combining Optical Brain Imaging and Physiological Signals to Study Cognitive ...InsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and fNIR Devices, Dr. Hasan Ayaz, Dr. Kurtulus Izzetoglu and Frazer Findlay present new research capabilities enabled through the integration of optical brain imaging technology and physiological recording systems.
Key topics covered during this webinar include physiological and physical principles of optical brain imaging, theory of operation, hardware and software integration, essential fNIR signal processing (demonstrated using fnirSoft analysis software), common field applications of fNIR imaging, why and how researchers can measure physiological data such as EDA, HR and ECG and acquistion procedures for co-registration of fNIR data and physiological monitoring signals using AcqKnowledge data acquisition and analysis software.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
This document compares the performance of three widely-used brain segmentation software packages (SPM, FSL, and Brainsuite) at segmenting brain MRI images into gray matter, white matter, and cerebrospinal fluid. It evaluates the packages using both simulated Brainweb MRI images and real MRI images from the IBSR dataset. For each package, it describes the segmentation methods and preprocessing steps. The accuracy of the segmented tissues from each package is assessed by calculating the similarity between the automated segmentations and corresponding ground truth segmentations. The results show variations in segmentation performance between the different packages and methods.
Presented at International Workshop on
Frontiers of Neuroengineering,
Brain-machine Interfaces
& Neural Prostheses
Zhejiang University, Hangzhou, China
March 29, 2011
IRJET-A Review on Brain Tumor Detection using BFCFCM AlgorithmIRJET Journal
The document presents a review of brain tumor detection using the BFCFCM clustering algorithm. It begins with an introduction to brain tumors and MRI imaging. It then reviews several existing techniques for brain tumor detection using artificial neural networks, linear discriminant analysis, neuro-fuzzy systems, and region growing segmentation with watershed algorithms. The document proposes a method using pre-processing, skull masking, segmentation with an advanced fuzzy c-means algorithm, feature extraction through thresholding, and an SVM classifier. Segmentation partitions the MRI image into regions/objects of interest like the tumor. Feature extraction analyzes the segmented regions to characterize the tumor for classification.
This document discusses using functional near-infrared spectroscopy (fNIR) neuroimaging to study learning in natural environments. It proposes establishing a neuroimaging facility at Drexel University that would use portable fNIR to non-invasively monitor brain activity during learning, problem-solving, training and other cognitive tasks. This could provide insights into the learning process and help optimize educational approaches. The facility would bring together experts from different fields and allow collaborative research on applications of fNIR including training, cognitive workload assessment, brain-computer interfaces and clinical areas.
Neurorobotics and Advances in rehabilitation engineeringBhaskarBorgohain4
Advances in robotics,mechatronics,cyborgs and disruptive technologies for heptics, brain machine interfaces and neurorobotics are bringing a sea change to the field of rehabilitation engineering. Carbon fibre cheetah blades, Bionic arms, c legs are helping the amputees to the extent that amputees can now run in competitive sports at the level of summer Olympics.
Feature Extraction and Classification of NIRS DataPritam Mondal
A thesis paper submitted to the department of Electronics and Communication
Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh, in
partial fulfillment of the requirement for the degree of “Bachelor of Science” in Electronics
and Communication Engineering
Magnetoencephalography an emerging biological marker for neurodegenerative an...Adonis Sfera, MD
Magnetoencephalography (MEG) is a technique that measures magnetic fields produced by electrical currents in the brain to map functional areas with great temporal resolution. MEG detects alterations in brain structure correlated with changes in function as seen with MRI. A new combined MEG-MRI device can simultaneously record ultra-low-field MRI and MEG to unprecedentedly locate brain activity. MEG has accurately diagnosed PTSD at 90% by identifying patterns unique to sufferers, and combining with diffusion tensor imaging may further improve diagnostic accuracy.
This document provides information about an upcoming "Multimodal Imaging in Neurosciences" course, including:
1) Dates and topics for upcoming lectures, as well as details about a final test on basic imaging techniques.
2) An overview of various neuroimaging modalities like CT, MRI, PET, and their applications.
3) A brief history of the development of high-intensity focused ultrasound (HIFU) technology from the 1880s to present.
There are over 600 neurological disorders that can cause dysfunction in the brain, spine or nerves. Neuroprosthetics are implantable devices that can replace or support lost neurological function. There are three main types: sensory neuroprosthetics like cochlear implants that restore hearing; motor neuroprosthetics that help control limb movement; and cognitive prosthetics that treat conditions like Alzheimer's. While neuroprosthetics show promise, they also carry risks like infections from brain surgery. Regulations vary depending on the device, but most require clinical trials to demonstrate safety and effectiveness before approval. Further research and international guidelines could help advance this emerging field.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
The document discusses brain imaging technologies such as MRI, fMRI, and emerging techniques. It describes how these methods can be used to image individual neurons, neuronal networks, and the whole brain. Examples are given of how fMRI has been used to study basic brain functions and diagnose neurological disorders. The document also suggests ways for non-experts to access and analyze brain imaging data through open access repositories and analysis tools.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
Let’s master the digital toolkit to harness lifelong neuroplasticitySharpBrains
Four leading pioneers of applied neuroplasticity helped us navigate best practices to harness most promising non-invasive neurotechnologies, such as cognitive training, mindfulness apps, EEG and virtual/ augmented reality.
--Chair: Linda Raines, CEO of the Mental Health Association of Maryland
--Dr. Michael Merzenich, winner of the 2016 Kavli Prize in Neuroscience
--Dr. Judson Brewer, Founder & Research Lead of Claritas Mindsciences
--Tan Le, CEO of Emotiv
--Dr. Andrea Serino, Head of Neuroscience at MindMaze
Learn more at sharpbrains.com
Kohut Literature Review fMRI correctionsJulie Kohut
Functional magnetic resonance imaging (fMRI) is a noninvasive brain mapping technique that measures blood flow and oxygen levels to detect brain activity. fMRI is used clinically to study brain recovery after stroke, pre-surgical planning by locating functional areas near tumors, and identifying seizure onset zones. It has advantages over other imaging methods as it provides detailed brain images without radiation exposure. While offering insights, fMRI also has limitations such as sensitivity to patient movement and drugs affecting blood flow measurements. Continued research aims to improve fMRI for applications in neurology, psychology, marketing and developing lie detection techniques.
Neurobionics and robotic neurorehabilitationsNeurologyKota
This document discusses neurobionics, robotic neurorehabilitation, and applications of neurobionics. It summarizes key areas including: (1) neurobionics aims to integrate electronics with the nervous system to repair or substitute impaired functions, (2) robotic neurorehabilitation uses robots to assist in rehabilitation processes, and (3) applications of neurobionics include motor interfaces like robotic arms, sensory interfaces like cochlear implants, and treating conditions like epilepsy and Parkinson's disease.
IRJET- Precision of Lead-Point with Support Vector Machine based Microelectro...IRJET Journal
This document discusses using microelectrode recordings (MER) with support vector machine learning to study the function of subthalamic nucleus (STN) neurons in the human brain during deep brain stimulation for Parkinson's disease. The study aims to improve the signal-to-noise ratio of MER signals and precisely identify the location of the STN to ensure safety and efficacy of deep brain stimulation chip implantation. MER is found to confirm the presence of abnormal STN neurons and clear positioning of the microchip electrode in the target area. Access to functional data from neurons deep in the brain using MER may help further elucidate cryptic aspects of brain function.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
This document reviews various techniques for detecting brain tumors in MRI images. It begins with an introduction to MRI and brain tumors. It then discusses several common methods for feature extraction (such as texture-based features using gray-level co-occurrence matrix) and classification (including neural networks, fuzzy c-means, k-nearest neighbors, support vector machines) that have been used for automated brain tumor detection. The document reviews 10 previous studies that detected brain tumors using techniques like segmentation, principal component analysis, probabilistic neural networks, and self-organizing maps. It then provides more detail on feature extraction methods, focusing on texture-based features.
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
Neuroimaging involves using various imaging techniques to assess brain structure and function in order to diagnose diseases. The main techniques discussed are computed tomography (CT), which combines multiple X-rays to create 3D brain images; positron emission tomography (PET), which images brain activity by detecting radioactive tracers; and magnetic resonance imaging (MRI), which produces highly detailed structural images using magnetic fields and radio waves. Functional MRI (fMRI) is a specialized MRI technique that maps brain activity by detecting changes in oxygenated blood flow.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
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Image Processing Technique for Brain Abnormality Detection
1. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 51
Image Processing Technique for Brain Abnormality Detection
Ashraf Anwar prof1aa@yahoo.com
Associate Professor of Computer Science,
Chair of Online Technologies & National Advisory Council Member
University of Atlanta
Atlanta, GA, USA
Arsalan Iqbal arsalan.Iqbal@email.uofa.edu
School of Computer and Information Science
University of Atlanta
Atlanta, Georgia, USA
Abstract
Medical imaging is expensive and very much sophisticated because of proprietary software and
expert personalities. This paper introduces an inexpensive, user friendly general-purpose image
processing tool and visualization program specifically designed in MATLAB to detect much of the
brain disorders as early as possible. The application provides clinical and quantitative analysis of
medical images. Minute structural difference of brain gradually results in major disorders such as
schizophrenia, Epilepsy, inherited speech and language disorder, Alzheimer's dementia etc. Here
the main focusing is given to diagnose the disease related to the brain and its psychic nature
(Alzheimer’s disease).
Keywords: Alzheimer, Bilinear Interpolation, Brain Imaging, Cerebral Cortex, Image
Registration, Neuroinformatics.
1. INTRODUCTION
The human brain [1] is the center of the human nervous system and is the most complex organ in
any creature on earth. Any abnormality in brain leads to the total collapse of entire vital functions
of the body [2]. This paper introduces a concept of simple user friendly GUI application to process
an image of brain and analyze its morphological abnormalities.
2. PROBLEM DEFINITION
Typical abnormality of brain is the shrinking of its cerebral cortex (as shown in Figure 1). The
spaces in the folds of the brain (the sulci) are grossly enlarged. This type of abnormality leads to
Alzheimer’s disease. Here the major objective is to identify the cerebral cortex region of brain and
measure the cavity area in the sulci (Figure 2) using software.
Currently, Brain MRI images could be checked for abnormality using more recent techniques [3],
[4], [5].
Goyal et al. [3] showed a technique for automatic detection of some types of brain abnormalities,
along with techniques for tumor segmentation in MRI sequences. They presented an automated
and clinically-tested method for detection of brain abnormalities and tumor-edema segmentation
using MRI sequences. Their method follows a Radiologist’s approach to the brain diagnosis using
multiple MRI sequences instead of any prior models or training phases. Their procedure consists
of the following steps:
2. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 52
i. Pre-processing of the MRI sequences, T2, T1, and T1 post-contrast (enhanced) for
size standardization, contrast equalization and division into active cells.
ii. Identification of the T2 MRI sequence as normal or abnormal by exploiting the vertical
symmetry of the brain.
iii. Determination of the region of abnormality using its hyper-intense nature.
iv. Separation of tumor from edema using the T1 and its post-contrast (enhanced)
sequences.
v. Estimation of the volume of tumor found and generation of an anatomical differential of
the possible disorders.
Lashkari [4] introduced an automatic brain tumor detection method to increase the accuracy and
yield, and decrease the diagnosis time. The goal in his work was to classify the tissues into two
classes: normal and abnormal. MR images that were used in his work were images from normal
and abnormal brain tissues. He tried to give clear description from brain tissues using Zernike
Moments, Geometric Moment Invariants, energy, entropy, contrast and some other statistic
features such as mean, median, variance, correlation between corresponding points, and values
of maximum and minimum intensity. He used a feature selection method to reduce the feature
space as well. His method used neural networks to do that classification. The purpose was to
classify the brain tissues into normal and abnormal classes automatically; which saves the
radiologist time, and increases accuracy and yield of diagnosis.
Reddy et al. [5] showed an improvement to fuzzy clustering means (FCM). They introduced an
earlier spatial constraint into FCM algorithm, in which the spatial information is encoded through
mutual influences of neighboring positions. To detect the abnormalities of Brain MRI images, they
used a new spatial FCM, and compared the results with k-means and FCM techniques.
3. PROJECT GOALS
A. Alzheimer- The Brain Disorder: Just like the rest of our bodies, our brains change as we age.
Most of us notice some slowed thinking and occasional problems remembering certain things.
However, serious memory loss, confusion and other major changes in the way our minds work
are not a normal part of aging [6]. These may be the signs of brain cells failure [7].
The brain has 100 billion nerve cells (neurons). Each nerve cell communicates with many others
to form network. Nerve cell networks have special jobs. Some are involved in thinking, learning
and remembering. Others help us see, hear and smell. Still others tell our muscles when to move.
In Alzheimer’s disease, as in other types of dementia, increasing numbers of brain cells
deteriorate and die [6]. Figure 3 shows the gradual increase of Alzheimer’s diseased people.
FIGURE 1:
Cerebral Cortex.
FIGURE2:
Sulci.
3. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 53
FIGURE 3: Alzheimer’s Patients’ Census.
B. Brain imaging methods: Functional imaging of electric brain activity requires specific models to
transform the signals recorded at the surface of the human head into an image [8]. Two
categories of model are available: 1) single-time-point and 2) spatio-temporal methods. The
instantaneous methods rely only on few voltage differences measured at one sampling point. To
create a spatial image from this limited information, they require strict assumptions that rarely
conform to the underlying physiology. Spatio-temporal models create two kinds of images: first, a
spatial image of discrete equivalent multiple dipoles or regional sources, and second, an image of
source current waveforms that reflect the temporal dynamics of the brain activity in circumscribed
areas. The accuracy of the spatial image is model dependent and limited, but it can be validated
from the spatio-temporal data by the "regional source imaging" technique, introduced here. The
source waveforms are linear combinations of the scalp waveforms, and thus, specific derivations
which image local brain activities at a macroscopic level.
Brain source imaging of somatosensory evoked potentials revealed temporally overlapping
activities from the brainstem, thalamus, and from multiple sources in the region of the
contralateral somatosensory projection areas.
MRI (Magnetic Resonance Imaging): Magnetic Resonance Imaging (MRI), or nuclear magnetic
resonance imaging (NMRI), is primarily a medical imaging technique most commonly used in
radiology to visualize the internal structure and function of the body. MRI provides much greater
contrast between the different soft tissues of the body than computed tomography (CT) does,
making it especially useful in neurological (brain), musculoskeletal, cardiovascular, and
oncological (cancer) imaging. Unlike CT, it uses no ionizing radiation, but uses a powerful
magnetic field to align the nuclear magnetization of (usually) hydrogen atoms in water in the body.
Radio frequency (RF) fields are used to systematically alter the alignment of this magnetization,
causing the hydrogen nuclei to produce a rotating magnetic field detectable by the scanner. This
signal can be manipulated by additional magnetic fields to build up enough information to
construct an image of the body.
Magnetic Resonance Imaging is a relatively new technology. The first MR image was published in
1973 and the first study performed on a human took place on July 3, 1977. By comparison, the
first human X-ray image was taken in 1895.
Magnetic Resonance Imaging was developed from knowledge gained in the study of nuclear
magnetic resonance [8]. In its early years the technique was referred to as nuclear magnetic
resonance imaging (NMRI). However, as the word nuclear was associated in the public mind with
ionizing radiation exposure it is generally now referred to simply as MRI. Scientists still use the
term NMRI when discussing non-medical devices operating on the same principles. The term
Magnetic Resonance Tomography (MRT) is also sometimes used.
4. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 54
Functional MRI: Functional MRI or functional Magnetic Resonance Imaging (fMRI) is a type of
specialized MRI scan. It measures the haemodynamic response related to neural activity in the
brain or spinal cord of humans or other animals. It is one of the most recently developed forms of
neuroimaging. Since the early 1990s, fMRI has come to dominate the brain mapping field due to
its low invasiveness, lack of radiation exposure, and relatively wide availability.
Positron Emission Tomography (PET): Positron emission tomography (PET) is a nuclear
medicine imaging technique which produces a three-dimensional image or picture of functional
processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-
emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule.
Images of tracer concentration in 3-dimensional space within the body are then reconstructed by
computer analysis. In modern scanners, this reconstruction is often accomplished with the aid of
a CT X-ray scan performed on the patient during the same session, in the same machine.
If the biologically active molecule chosen for PET is FDG, an analogue of glucose, the
concentrations of tracer imaged then give tissue metabolic activity, in terms of regional glucose
uptake. Although use of this tracer results in the most common type of PET scan, other tracer
molecules are used in PET to image the tissue concentration of many other types of molecules of
interest.
C. Image Registration: In this project, sets of data acquired by sampling the same scene or object
at different times, or from different perspectives, will be in different coordinate systems. Image
registration is the process of transforming the different sets of data into one coordinate system. [9]
Registration is necessary in order to be able to compare or integrate the data obtained from
different measurements.
Medical image registration (for data of the same patient taken at different points in time) often
additionally involves elastic (also known as nonrigid) registration to cope with deformation of the
subject (due to breathing, anatomical changes, and so forth). Nonrigid registration of medical
images can also be used to register a patient's data to an anatomical atlas, such as the Talairach
atlas for neuroimaging.
Image registration is the process of overlaying two or more images of the same scene taken at
different times, from different viewpoints, and/or by different sensors. It geometrically aligns two
images—the reference and sensed images. The present differences between images are
introduced due to different imaging conditions. Image registration [10] is a crucial step in all image
analysis tasks in which the final information is gained from the combination of various data
sources like in image fusion, change detection, and multichannel image restoration. Typically,
registration is required in remote sensing (multispectral classification, environmental monitoring,
change detection, image mosaicing, weather forecasting, creating super-resolution images,
integrating information into geographic information systems (GIS)), in medicine (combining
computer tomography (CT) and NMR data to obtain more complete information about the patient,
monitoring tumor growth, treatment verification, comparison of the patient’s data with anatomical
atlases), in cartography (map updating), and in computer vision (target localization, automatic
quality control), to name a few. During the last decades, image acquisition devices have
undergone rapid development and growing amount and diversity of obtained images invoked the
research on automatic image registration.
D. Image registration methodology: Image registration, as it was mentioned above, is widely used
in remote sensing, medical imaging, computer vision etc. In general, its applications can be
divided into four main groups according to the manner of the image acquisition [9]:
1. Different viewpoints (multi-view analysis). Images of the same scene are acquired from
different viewpoints. The aim is to gain larger a 2D view or a 3D representation of the scanned
5. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 55
scene. Examples of applications: Remote sensing—mosaicing of images of the surveyed area.
Computer vision—shape recovery (shape from stereo).
2. Different times (multi-temporal analysis). Images of the same scene are acquired at different
times, often on regular basis, and possibly under different conditions. The aim is to find and
evaluate changes in the scene which appeared between the consecutive image acquisitions.
Examples of applications: Remote sensing—monitoring of global land usage, landscape planning.
Computer vision— automatic change detection for security monitoring, motion tracking. Medical
imaging— monitoring of the healing therapy, monitoring of the tumor evolution.
3. Images of the same scene are acquired by different sensors. The aim is to integrate the
information obtained from different source streams to gain more complex and detailed scene
representation. Examples of applications: Remote sensing—fusion of information from sensors
with different characteristics like panchromatic images, offering better spatial resolution,
color/multispectral images with better spectral resolution, or radar images independent of cloud
cover and solar illumination. Medical imaging—combination of sensors recording the anatomical
body structure like magnetic resonance image (MRI), ultrasound or CT with sensors monitoring
functional and metabolic body activities like positron emission tomography (PET), single photon
emission computed tomography (SPECT) or magnetic resonance spectroscopy (MRS). Results
can be applied, for instance, in radiotherapy and nuclear medicine.
4. Scene to model registration. Images of a scene and a model of the scene are registered. The
model can be a computer representation of the scene, for instance maps or digital elevation
models (DEM) in GIS, another scene with similar content (another patient), ‘average’ specimen,
etc. The aim is to localize the acquired image in the scene/model and/or to compare them.
Examples of applications: Remote sensing—registration of aerial or satellite data into maps or
other GIS layers. Computer vision—target template matching with real-time images, automatic
quality inspection. Medical imaging— comparison of the patient’s image with digital anatomical
atlases specimen classification.
Due to the diversity of images to be registered and due to various types of degradations it is
impossible to design a universal method applicable to all registration tasks. Every method should
take into account not only the assumed type of geometric deformation between the images but
also radiometric deformations and noise corruption, required registration accuracy and
application-dependent data characteristics. Nevertheless, the majority of the registration methods
consist of the following four steps (closed-boundary regions, edges, contours, line intersections,
corners, etc.) are manually or, preferably, automatically detected. For further processing, these
features can be represented by their point representatives (centers of gravity, line endings,
distinctive points), which are called control points (CPs) in the literature. Feature matching. In this
step, the correspondence between the features detected in the sensed image and those detected
in the reference image is established. Various feature descriptors and similarity measures along
with spatial relationships among the features are used for that purpose. Transform model
estimation. The type and parameters of the so-called mapping functions, aligning the sensed
image with the reference image, are estimated. The parameters of the mapping functions are
computed by means of the established feature correspondence. Image resampling and
transformation. The sensed image is transformed by means of the mapping functions. Image
values in non-integer coordinates are computed by the appropriate interpolation technique. The
implementation of each registration step has its typical problems. First, we have to decide what
kind of features is appropriate for the given task. The features should be distinctive objects, which
are frequently spread over the images and which are easily detectable. Usually, the physical
interpretability of the features is demanded. The detected feature sets in the reference and
sensed images must have enough common elements, even in situations when the images do not
cover exactly the same scene or when there are object occlusions or other unexpected changes.
The detection methods should have good localization accuracy and should not be sensitive to the
assumed image degradation. In an ideal case, the algorithm should be able to detect the same
7. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 57
3. Get the pixmap (Identified as 3D array of image in MATLAB [12]).
4. Extract cavity area of sulci (Apply imcrop function [12]).
5. Find the sum of each pixel value of three arrays; (RED+GREEN+BLUE) from cropped area
6. If value= =0 identify as black.
7. Put total identified pixels (black) into an array called as cavity array.
8. Else put those pixels (not black) into an array called as cortex array.
9. Compare the length of both cavity and cortex arrays (Apply length function).
10. If (length of cavity array is more), then trace as abnormality in the figure.
11. Else normal nature.
12. Set the graph of both cavity and cortex arrays (Apply plot function).
5. TESTED RESULTS USING MATLAB
FIGURE 5: Simulated Image First Load.
8. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 58
FIGURE 6: Image in Zoom.
FIGURE 7: Normal Area.
FIGURE 8: Abnormal Area.
9. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 59
FIGURE 9: Measure of Abnormality.
6. SCOPE OF STUDY
The scope of this study can open an emerging trend in the field of computer and medical science;
a new branch of science called Neuroinformatics, it may deal all the issues of neuroscience and
computer science.
Neuroinformatics is a research field that encompasses the organization of neuroscience data and
application of computational models and analytical tools. These areas of research are important
for the integration and analysis of increasingly fine grain experimental data and for improving
existing theories about nervous system and brain function. Neuroinformatics provides tools,
creates databases and the possibilities for interoperability between and among databases,
models, networks technologies and models for the clinical and research purposes in the
neuroscience community and other fields.
Neuroinformatics stands at the intersection of neuroscience and information science. Other
sciences, like genomics, have proved the effectiveness of data sharing through databases and by
applying theoretical and computational models for solving complex problems in the field. Through
Neuroinformatics facilities, researchers can share their data and contribute to other disciplines
using available tools for the analysis and integration of data. Researchers can also more easily
quantitatively confirm their working theories by means of computational modeling. Additionally,
Neuroinformatics fosters more collaborative research of which one aim is to provide better
possibilities to study the brain at multiple levels of brain structure. There are three main directions
where Neuroinformatics has to be applied:
a. The development of tools and databases for management and sharing of neuroscience data
at all levels of analysis,
b. The development of tools for analyzing and modeling,
c. The development of computational models of the nervous system and neural processes.
In the recent decade; as vast amounts of diverse data about the brain were gathered by many
research groups, the problem of how to integrate the data from thousands of publications in order
to enable efficient tools for further research; was raised. The biological and neuroscience data are
highly interconnected and complex, and by itself represents a great challenge for scientists.
10. Ashraf Anwar & Arsalan Iqbal
International Journal of Image Processing (IJIP), Volume (7) : Issue (1) : 2013 60
Combining informatics research and brain research provides benefits for both fields of science.
On one hand, informatics facilitates brain data processing and data handling, by providing new
electronic and software technologies for arranging databases, modeling and communication in
brain research. On the other hand, enhanced discoveries in the field of neuroscience will invoke
the development of new methods in information technologies.
7. CONCLUSION
Due to high cost and requirement of professionalism much of medical imaging softwares are far
from common man. Here the attempt was develop simple software in MATLAB to detect the
structural abnormality of brain. The task almost fulfilled but it requires much perfection. The
highlight of this software is simplicity, user friendliness and keen observation of the image at
minute level.
8. FUTURE RESEARCH
In this research the measurement of cavity area is totally based on the color coding system. This
measurement should be converted into some metric form. After that the abnormality can be
categorized as mild, moderate and severe.
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