Clinical Diagnosis of Parkinson’s disease [PD] leads to errors, excessive medical costs, and provide
insufficient services to the patients. There is no particular method or a test to detect the PD. The diagnosis
of the Parkinson’s disease needs an accurate detection. Computer Aided Diagnosis (CAD) gives accurate
results to detect the PD. These CAD can be embedded into a real time application for the early diagnosis of
PD. Dopamine nerve terminals can be reduced in the brain parts such as Substantia nigra, Striatum, and
other brain structures. This reduction which will lead to Parkinson’s disease. Dopamine Reduction gets
automatically diagnosed by CAD and PD/normal patients can be found. For this, machine learning system
(MLS)/CAD can be trained with the help of Artificial Neural Networks (ANN). Image processing techniques
that are available to detect PD using MLS/CAD gets discussed in this paper.
High Precision And Fast Functional Mapping Of Cortical Circuitry Through A No...Taruna Ikrar
Taruna Ikrar, MD., PhD. Author at (High Precision and Fast Functional Mapping of Cortical Circuitry Through a Novel Combination of Voltage Sensitive Dye Imaging and Laser Scanning Photostimulation)
High Precision And Fast Functional Mapping Of Cortical Circuitry Through A No...Taruna Ikrar
Taruna Ikrar, MD., PhD. Author at (High Precision and Fast Functional Mapping of Cortical Circuitry Through a Novel Combination of Voltage Sensitive Dye Imaging and Laser Scanning Photostimulation)
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
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.
Image Processing Technique for Brain Abnormality DetectionCSCJournals
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).
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.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
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.
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
Intro to Transcranial Direct Curent Stimulation (tDCS)Daniel Stevenson
A comprehensive introduction to tDCS with a main focus on research utilizing motor-evoked potentials (MEPs) to uncover the physiological mechanism of therapeutic and enhancing effects of tDCS application. Regulation and FDA guidelines are also thoroughly covered. Provides a good source of relevant academic citations (on each slide).
Adoption of internal web technologies by oecd turkish government officialsijmpict
Use of communication and information channels for the OECD have been increasingly encouraged by
new channels such as the OECD’s Committee Information Service (OLIS) and Clearspace (CS) web
portals. A logit regression model was used to estimate the influence of the government’s supply side policy
tools and organisational factors on the decision to open OLIS and Clearspace accounts. Additionally,
probability analysis conducted to give insights on the usage frequency of information channels. Study used
a dataset that includes 126 Turkish top-level country and municipal government officials working on
different OECD study topics in 2010. Findings imply that the influence of the explanatory variables tested
differ between the two web-portal models. Satisfaction with the timing of information provided by the
OECD Permenant Delegation (timing issues in reaching reports) among officials is the only variable that
consistently has a positive influence on the adoption of both web-portal applications. The probability
analysis show that while duration of employment and degree of expertise increase the probability of use of
online information channels, work duration on OECD topics and meeting participation are the variables
that decrease the probability of use of face to face communication channels.
Crime and violence are inherent in our political and social system. With the moving pace of technology, the
popularity of internet grows continuously, with not only changing our views of life, but also changing the
way crime takes place all over the world. We need a technology that can be used to bring justice to those
who are responsible for conducting attacks on computer systems across the globe. In this paper, we present
various measures being taken in order to control and deal with the crime related to digital devices. This
paper gives an insight of Digital Forensics and current situation of India in handling such type of crimes.
Emotions are an unstoppable and uncontrollable aspect of mental state of human. Some bad situations give
stress and leads to different sufferings. One can’t avoid situation but can have awareness when body feel
stress or any other emotion. It becomes easy for doctors whose patient is not in condition to speak. In that
case person’s physiological parameters are measured to decide emotional status. While experiencing
different emotion, there are also physiological changes taking place in the human body, like variations in
the heart rate (ECG/HRV), skin conductance (GSR), breathing rate(BR), blood volume pulse(BVP),brain
waves (EEG), temperature and muscle tension. These were some of the metrics to sense emotive coefficient.
This research paper objective is to design and develop a portable, cost effective and low power
embedded system that can predict different emotions by using Naïve Bayes classifiers which are based on
probability models that incorporate class conditional independence assumptions. Inputs to this system are
various physiological signals and are extracted by using different sensors. Portable microcontroller used
in this embedded system is MSP430F2013 to automatically monitor the level of stress in computer. This
paper reports on the hardware and software instrumentation development and signal processing approach
used to detect the stress level of a subject.To check the device's performance, few experiments were done in
which 20 adults (ten women and ten men) who completed different tests requiring a certain degree of effort,
such as showing facing intense interviews in office.
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
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.
Image Processing Technique for Brain Abnormality DetectionCSCJournals
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).
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.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
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.
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
Intro to Transcranial Direct Curent Stimulation (tDCS)Daniel Stevenson
A comprehensive introduction to tDCS with a main focus on research utilizing motor-evoked potentials (MEPs) to uncover the physiological mechanism of therapeutic and enhancing effects of tDCS application. Regulation and FDA guidelines are also thoroughly covered. Provides a good source of relevant academic citations (on each slide).
Adoption of internal web technologies by oecd turkish government officialsijmpict
Use of communication and information channels for the OECD have been increasingly encouraged by
new channels such as the OECD’s Committee Information Service (OLIS) and Clearspace (CS) web
portals. A logit regression model was used to estimate the influence of the government’s supply side policy
tools and organisational factors on the decision to open OLIS and Clearspace accounts. Additionally,
probability analysis conducted to give insights on the usage frequency of information channels. Study used
a dataset that includes 126 Turkish top-level country and municipal government officials working on
different OECD study topics in 2010. Findings imply that the influence of the explanatory variables tested
differ between the two web-portal models. Satisfaction with the timing of information provided by the
OECD Permenant Delegation (timing issues in reaching reports) among officials is the only variable that
consistently has a positive influence on the adoption of both web-portal applications. The probability
analysis show that while duration of employment and degree of expertise increase the probability of use of
online information channels, work duration on OECD topics and meeting participation are the variables
that decrease the probability of use of face to face communication channels.
Crime and violence are inherent in our political and social system. With the moving pace of technology, the
popularity of internet grows continuously, with not only changing our views of life, but also changing the
way crime takes place all over the world. We need a technology that can be used to bring justice to those
who are responsible for conducting attacks on computer systems across the globe. In this paper, we present
various measures being taken in order to control and deal with the crime related to digital devices. This
paper gives an insight of Digital Forensics and current situation of India in handling such type of crimes.
Emotions are an unstoppable and uncontrollable aspect of mental state of human. Some bad situations give
stress and leads to different sufferings. One can’t avoid situation but can have awareness when body feel
stress or any other emotion. It becomes easy for doctors whose patient is not in condition to speak. In that
case person’s physiological parameters are measured to decide emotional status. While experiencing
different emotion, there are also physiological changes taking place in the human body, like variations in
the heart rate (ECG/HRV), skin conductance (GSR), breathing rate(BR), blood volume pulse(BVP),brain
waves (EEG), temperature and muscle tension. These were some of the metrics to sense emotive coefficient.
This research paper objective is to design and develop a portable, cost effective and low power
embedded system that can predict different emotions by using Naïve Bayes classifiers which are based on
probability models that incorporate class conditional independence assumptions. Inputs to this system are
various physiological signals and are extracted by using different sensors. Portable microcontroller used
in this embedded system is MSP430F2013 to automatically monitor the level of stress in computer. This
paper reports on the hardware and software instrumentation development and signal processing approach
used to detect the stress level of a subject.To check the device's performance, few experiments were done in
which 20 adults (ten women and ten men) who completed different tests requiring a certain degree of effort,
such as showing facing intense interviews in office.
Checkpoint and recovery protocols are commonly used in distributed applications for providing fault
tolerance. A distributed system may require taking checkpoints from time to time to keep it free of arbitrary
failures. In case of failure, the system will rollback to checkpoints where global consistency is preserved.
Checkpointing is one of the fault-tolerant techniques to restore faults and to restart job fast. The algorithms
for checkpointing on distributed systems have been under study for years.
It is known that checkpointing and rollback recovery are widely used techniques that allow a distributed
computing to progress inspite of a failure.There are two fundamental approaches for checkpointing and
recovery.One is asynchronus approach, process take their checkpoints independenty.So,taking checkpoints
is very simple but due to absence of a recent consistent global checkpoint which may cause a rollback of
computation.Synchronus checkpointing approach assumes that a single process other than the application
process invokes the checkpointing algorithm periodically to determine a consistent global checkpoint.
Automatic analysis of smoothing techniques by simulation model based real tim...ijesajournal
The pivotal research work that has been carried out and described in this literature acknowledges the
importance of various smoothing techniques for processing 3D human faces from 2.5D range face images.
The smoothing techniques have been developed and implemented using MATLAB-Simulink for real time
processing in embedded system. In addition, the significance of smoothed 2.5D range image over original
face range image has been discovered as well as its time complexity has also been reported with array of
experiments. The variations in time complexities are also accomplished using different optimization levels
and execution modes. A set of filtering techniques such as, Max filter, Min filter, Median filter, Mean filter,
Mid-point filter and Gaussian filter, have been designed and illustrated using Simulink model. The model
takes depth face image (i.e. the range face image) as input in real time and presents the improvement over
original face images. In the design flow, the performance of every block has also been characterized by
range face images from Frav3D, GavabDB, and Bosphorus databases. In the experimental section of this
research article, an array of performance analysis for these smoothing techniques with variation of
frameworks is explained.
Quite often in experimental work, many situations arise where some observations are lost or become
unavailable due to some accidents or cost constraints. When there are missing observations, some
desirable design properties like orthogonality,rotatability and optimality can be adversely affected. Some
attention has been given, in literature, to investigating the prediction capability of response surface
designs; however, little or no effort has been devoted to investigating same for such designs when some
observations are missing. This work therefore investigates the impact of a single missing observation of the
various design points: factorial, axial and center points, on the estimation and predictive capability of
Central Composite Designs (CCDs). It was observed that for each of the designs considered, precision of
model parameter estimates and the design prediction properties were adversely affected by the missing
observations and that the largest loss in precision of parameters corresponds to a missing factorial point.
Cryptography technology is a security technique used to change plain text to another shape of data or to
symbols, which is known as the cipher text. Cryptography aims to keep the data secure during its journey
through public networks. Currently, there are many proposed algorithms that provide this service
especially for sensitive data or very important conversations either through mobile or video conferences. In
this paper, an inventive security symmetric algorithm is implemented and evaluated, and its performance is
compared to the AES. The algorithm has four different rounds for each quarter of the key container table,
and each of them serves to shift the table. The algorithm uses the XOR operation, which, being lightweight
and cheap, is very appropriate for use with Real Time Applications. The result shows that the suggested
algorithm spends less time than AES although it has 16 rounds and the numbers used to mix up the table
are big.
Watch step by Step Info, How you make system for Autopilot business. You also buy this system (if you can't afford or make step by step business formula),No problem!!
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The foremost by-product of this paper is the automation of geological undertakings, for instance, dealing
with exceptionally thin sections of rocks that were subjected to deformation alongside finite steps of time
which can be recorded in video for later analysis using image processing and numerical analysis
procedures. Markers are used in order to trace gradients of deformation over a sample and study other
mechanical properties. Image processing and video sequence analysis can be a very powerful investigation
tool and this paper shows preliminary results from its use on microtectonics. The proposed algorithm is a
combination of two well-known approaches: feature extraction and block matching.
Retina is a layer which is found at the back side of the eye ball which plays main role for visualization. Any
disease in the retina leads to severe problems. Blood vessels segmentation and classification of retinal
vessels into arteries and veins is an essential thing for detection of various diseases like Diabetic
Retinography etc. This paper discusses about various existing methodologies for classification of retinal
image into artery and vein which are helpful for the detection of various diseases in retinal fundus image.
This process is basis for the AVR calculation i.e. for the calculation of average diameter of arteries to
veins. One of the symptoms of Diabetic Retinography causes abnormally wide veins and this leads to low
ratio of AVR. Diseases like high blood pressure and pancreas also have abnormal AVR. Thus classification
of blood vessels into arteries and veins is more important. Retinal fundus images are available on the
publically available Database like DRIVE [5], INSPIREAVR [6], VICAVR [7].
The main objective of this paper is to design and develop an automatic vehicle, fully controlled by a
computer system. The vehicle designed in the present work can move in a pre-determined path and work
automatically without the need of any human operator and it also controlled by human operator. Such a
vehicle is capable of performing wide variety of difficult tasks in space research, domestic, scientific and
industrial fields. For this purpose, an IBM compatible PC with Pentium microprocessor has been used
which performed the function of the system controller. Its parallel printer port has been used as data
communication port to interface the vehicle. A suitable software program has been developed for the
system controller to send commands to the vehicle.
The influence of individual factors on the entrepreneurial intentionijmvsc
Today, no one is safe from forces and pressures, which are exerted on it, because of a significant number
of the requirements in particular as regards competitiveness, the need for change, or the crises and the
deregulations. In front of the economic and social turbulences which we know, the creation of new
company appears as a cause of general interest. This research papers focuses on the problematic of the
entrepreneurship, and more particularly on the stake which this domain represents in our society, by
treating the determinants of the entrepreneurial intention. To face this news gives, students must
reconsider their behaviors and their practices to renew themselves, to open out and reinforce their
position in the market. Some of these practices form what one calls the entrepreneurial orientation. For
this reason, we will devote this paper for better encircling and apprehending the concept of individual
factors, and we tried to know how the individual factors (motivations, need for accomplishment, need for
autonomy, passion to develop its own idea, individual characteristics, work experience, teaching) can
influence the intention of the entrepreneur to create his own project. We focused on review literature
through a survey of a sample of students from the Higher Institute of Business Administration of Sfax
(Tunisia).
Statistical, Energy Values And Peak Analysis (SEP) Approach For Detection of ...IJMERJOURNAL
ABSTRACT: In this paper, a technique of statistical, Energy values and peak analysis (SEP) approach is used for detection of neurodegenerative diseases from the signal of force sensitive resistors. In this work within the time series Left Stride Interval, Right Stride Interval, Left Swing Interval, Right Swing Interval, Left Stance Interval, Right Stance Interval and Double support interval are obtained and apply the SEP method. In statistical analysis, energy, standard deviation, mean, variance, co-variance are calculated. Two approximations and two details of energy values are extracted from wavelet decomposition. Average peak interval and peak histogram are calculated using peak analysis. Support Vector Machine (SVM) and Random Forest are used as a classifier. Data sets which include a healthy control (HC), various types of Neuro degenerative Diseases: Parkinson’s Disease (PD), Huntington Disease (HD), Amyotrophic Lateral Sclerosis. For disease diagnostic Force Sensitive resistor signals are used for evaluation. The results show that the proposed technique can successfully detect the NDD pathologies. For NDD detection, the accuracy, the Sensitivity, the Specificity values are 97%, 97% and 97% using Random forest Classifier.
DUSHYANT VERMA - BRAIN TECHNIQUES FOR MIND MAPPING.pptxdushyantverma25
In this blog, we will explore various brain imaging techniques and shed light on their applications in neuroscience, all with the guidance of renowned neurologist, Dushyant Verma. From peering inside the intricate networks of the brain to deciphering neurological disorders, these imaging techniques have revolutionized our understanding of the mind.
Deep brain stimulation (DBS)/Brain pacemaker has evolved as an important and established treatment modality for variety of advanced movement disorders and also for some psychiatry disorders.1Chronic DBS stimulation provides a non destructive and reversible means of disturbing the abnormal function of basal ganglia circuit. It can be adjusted as disease progresses or adverse event occur. Bilateral stimulation can be performed without a significant increase inadverse effects.Adverse events related to unintended stimulation of adjacent structures are readily reversible by altering the stimulus parameters.
In Feb 2022, Dr. Cassano presented this NYU-MGH study at LifeStance Health. The presentation covers in-vitro and pre-clinical data in support of the use of transcranial PBM for Alzheimer’s disease (AD). The most recent clinical data on PBM for AD were also reviewed. The NYU-MGH study was described: a large randomized, double-blind clinical trial (n=125) sponsored by NIH/NIA and by the Alzheimer’s Association.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
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It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
1. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
DOI:10.5121/ijcsa.2014.4605 57
PARKINSONS DISEASE DIAGNOSIS USING
IMAGE PROCESSING TECHNIQUES
A SURVEY
A.Valli1
and Dr.G.Wiselin Jiji2
1
Final Year PG, Dr.Sivanthi Aditanar College of Engineering, Tiruchendur, India
2
Principal, Dr.Sivanthi Aditanar College of Engineering, Tiruchendur, India
ABSTRACT
Clinical Diagnosis of Parkinson’s disease [PD] leads to errors, excessive medical costs, and provide
insufficient services to the patients. There is no particular method or a test to detect the PD. The diagnosis
of the Parkinson’s disease needs an accurate detection. Computer Aided Diagnosis (CAD) gives accurate
results to detect the PD. These CAD can be embedded into a real time application for the early diagnosis of
PD. Dopamine nerve terminals can be reduced in the brain parts such as Substantia nigra, Striatum, and
other brain structures. This reduction which will lead to Parkinson’s disease. Dopamine Reduction gets
automatically diagnosed by CAD and PD/normal patients can be found. For this, machine learning system
(MLS)/CAD can be trained with the help of Artificial Neural Networks (ANN). Image processing techniques
that are available to detect PD using MLS/CAD gets discussed in this paper.
Keywords
Parkinson’s disease (PD), Statistical Parametric Mapping (SPM), Computer Aided Diagnosis (CAD)
1.INTRODUCTION
Parkinson’s disease (PD), Statistical Parametric Mapping (SPM), Computer Aided Diagnosis (CAD)
Parkinson's disease is a second neurodegenerative disease which causes major threat to aged
people and in the society as a whole and this is next to Alzheimer’s disease [2]. Neurochemically,
Parkinson’s disease gets occurred due to the loss of dopamine nerve terminals in the region of
striatum which are connects to the Substantia Nigra. Dopamine deficit may cause due to the loss
of neurons in the midbrain of Substantia Nigra which will lead to the result that there occur
changes within nigrostriatum neural conduction. Apart from that, PD can also characterize by the
presence of intracytoplasmic inclusions called lewy bodies. Clinical diagnosis for each and every
human may vary largely.
Problems with imbalance, tremor, postural instability, rigidity are all the major symptoms for PD.
Motor symptoms usually start at one side of the body and gradually it will progress to the
opposite side. Other parkinsonian syndromes are mainly affected by these symptoms. Idiopathic
PD diagnosis indicators are symptoms, move to advanced stage, and treatment response based on
levadopa. Tremor symptoms lead to loss of voluntary movement. Tremor may happen at thumb
and wrist, which is one of the most typical initial symptoms. The amount of resistance can be
measured by limb rigidity when it is moved passively. PD disease patients have higher resistance
in limb than normal person. Changes in muscle and joints properties can also contribute to the
presence of parkinsonian rigidity. Bradykinesia symptoms lead to some of the familiar problems
such as difficult to sit and stand in a floor, get in and out of a car, chair. Bradykinesia symptoms
2. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
58
which are similar to akinesia and hypokinesia which also leads to loss of immobility. Postural
instability may cause problems such as person suffer to sit independently, more disturbance in
outside, doesn’t stand individually, and inability to regain balance. An akinesia symptom leads to
immovability of body movement, whereas range and size of movement gets decreased refers to
hypokinesia. Problems with imbalance symptoms has not analyzed yet. It is very difficult to
predict PD with the help of applicable environmental information.
Still there is no medical treatment to diagnose Parkinson’s disease although medication is
available while the symptoms are gets identified at early stages of this disease [3]. Early stage
diagnosis can result in significant life saving. Parkinson’s disease specialists make correct
decisions by evaluating their test results of their patients. Diagnosing Parkinson’s disease may
need experience and highly skilled specialists [4]. Improvement of diagnosis and assessment in
early disease can gets resolved by functional neuro imaging. Statistical Parametric Mapping
(SPM) is a popular Matlab based software package for performing neuro imaging studies that can
be used to locate significant effects of statistical parametric models in images. Biological Neural
systems can be simulated by artificial neural Networks. There are number of nerve cells in human
brain which are called as neurons where each neuron is connected to other neurons via stand of
fiber called axons. Nerve impulses get transmitted from one neuron to other by axons, when the
neurons are affected. Dendrites are the one which connects a neuron and axons of other neurons.
The contact point between a dendrite and axon is called a synapse. The output of the network may
be continuous or discrete. If it is discrete it performs prediction otherwise it performs
classification. Detection of Normal/PD classes can be accepted even there is a small change in the
arrangement of neurons and network. Most of the medical diagnosis uses classifier that will get
increasing gradually. Advancements in artificial Intelligence led to the emergence of expert
systems for medical applications. Classification systems can be used to improve accuracy,
reliability of diagnosis and minimizing possible errors, as well as making the diagnoses of the
disease more time efficient [4].
2.OVERVIEW OF ALGORITHM
Diagnosis and the detection of PD using automated image processing techniques at early stages
are reviewed here is the main intension of this survey. In this paper, different methods based on
machine learning techniques were mentioned for the diagnosis of PD. These processing
algorithms provide more accuracy, sensitivity, specificity over the detection of the patients
affected or not affected by PD.
2.1. DATASET
Multiple system atrophy, progressive supranuclear palsy is neurodegenerative disorder which is
associated with PD. Visualization and assessment of changes in cerebral blood flow, glucose
metabolism, and neurotransmitter can be done by Positron emission tomography (PET) and
single-photon emission tomography (SPECT). Structural changes are very small and it can be
diagnosed successfully at an early stage of this disease. Anatomical imaging modalities diagnostic
accuracy (e.g., magnetic resonance imaging [MRI]) is not good in neurodegenerative disorder [5].
A change in neurotransmitter function gets noted particularly in the dopaminergic system which
becomes evident long, before structural, metabolic or blood flow. Positron Emission tomography
(PET) and single photon emission tomography (SPECT) images are preferred than MRI for the
diagnosis of Parkinson’s disease. PET and SPECT imaging uses the below two main paths:
1. To detect abnormal tissue functioning in neurons make a studies about blood flow and cerebral
metabolism.
3. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
59
2. To study the loss of dopamine neurons keep monitor and track the imaging of dopaminergic
neurotransmitter system.
2.2. PRE-PROCESSING
Image pre-processing is the technique which can be used to enhance data images prior to
computational processing. Preprocessing an image which involves removing noise removal,
normalization of intensity images, removing reflections, and masking portions of images.
Aprajita Sharma [6] presented the preprocessing techniques for MRI brain images. Dynamic
speckle noise may arise from different tissues so that MRI images are very sensitive. These noises
can be avoided by morphological operations. The main problem of reduction is that speckle is not
a static noise but dynamic in image. Influence is not very considerable when a small image gets
considered and their speckle noise can be reduced.
Jiri Blahuta, Tomas souk up [8] presents the techniques for preprocessing ultrasound images. Use
diagnostic ultrasound for soft tissues. The main processing used here is Input images to be resized
to 50x50 mm ROI with Substantia Nigra. All the input images are in gray scale. If input image is
in 24 bit depth that is RGB, convert that RGB image into gray image using rgb2gray function.
Therefore all the intensity value of that given image is in range (0; 255).
I.A. Illan, J.m Gorriz presented the normalization technique for preprocessing in SPECT images.
Spatial normalization is one of the techniques similar to image registration. Human brain may
differ in size and shape and they can be view in different view as sagittal, coronal and
Hemispheric. The location of one subject’s brain scan can correlated to the same location of
another subject’s brain scan is the main goal of spatial normalization. And also they used Intensity
normalization. An intensity value in the brain gets normalized and it acts as a mean image. This
value can be used as a feature for classifying the normal and Parkinson’s disease affected person.
Dan Long, jinwei wang [23] performed data preprocessing by using Statistical Parametric
Mapping (SPM8) probability maps. All the images were then resembled to an isotropic resolution
of 3 mm at the end of the normalization and segmentation process can be applied across all of the
subjects. Each of the every image gets modulated to the original image that is target image. Target
image gets selected as a good quality image. The modulated image gets smoothed by a 10-mm
full width at half maximum (FWHM) Gaussian kernel.
2.3. FEATURE EXTRACTION
The feature extraction [10] process can be used to reduce the input data dimension and minimize
the training time taken by the classifier. From the Region of Interest (ROI) we can extract
Geometrical, statistical, and texture moments [4]. These statistical features are extracted directly
from the images. Statistical moments which include features such as mean value, Median Value,
Standard Deviation, Skewness, Kurtosis. Gabor filters are employed to extract texture features.
Gabor filters are very effective to extract these texture features.
Prashanth, Sumantra Dutta Roy [7], developed a machine learning algorithm for diagnosing
Parkinson’s disease. DaTSCAN SPECT images that are available from the PPMI database
(http://www.ppmi-info.org/about-ppmi/who-we-are/study-cores/) were used to compute the
striatal binding ratio (SBR) values of the four striatal regions (left and right caudate, left and right
putamen). SBR Values contained in the database is calculated from 179 normal and 369 early PD
subjects. Longitudinal subjects are evaluated in PPMI. Occital lobe region considered as the
reference and using this, SBR values are calculated for the right caudate, left caudate, right
putamen and left putamen.
4. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
60
Dan Long, jinwei wang [23] proposed that to test the hypothesis, first extract multi-level
characteristics (ALFF, ReHo and RFCS) from the fMRI data and extract grey matter, white
matter, and CSF volume from structural data. Intrinsic or spontaneous neural activity in the
human brain gets indicated by ALFF. This is one of the effective indicators. In this paper ALFF
can be calculated by voxel time series gets converted into the frequency domain using Fast
Fourier Transform and from that power spectrum was obtained and then ALFF was calculated as
the mean of this square root [24].The ALFF of each voxel was divided by the global mean value
to reduce the global effects of variability in all subjects. ReHo features can be used to evaluate
brain activities in the resting state by measuring the functional synchronization of a given voxel to
its nearest neighbors. Average correlation of one region gets compared with all other regions
which gets measured by RFCS.
2.4. FEATURE SELECTION
Principle Component Analysis is one of the feature selection techniques. The transformation of
data space into a feature space is known as feature selection. This selection technique reduced
number of effective features and retains only important intrinsic information data. A set of
correlated variables gets converted into a new set of uncorrelated variables by using PCA.
Aprajita Sharma [6] uses the PCA as a feature selection technique to reduce the dimensionality of
the data.
Dan Long, jinwei wang [23] proposed that the feature selection method was used to compare the
feature values of the various brain regions between the two subject groups. Two groups which
have similar differences in features were selected.
XEL BASED MORPHOMETRY (VBM)
Thomas jubalt, Simons presented a voxel based morphometry technique. This is a neuroimaging
analysis technique that allows investigation of focal differences in brain anatomy using statistical
approach of SPM. This technique identifies local atrophies without the selection of region of
interest. MRI images are used as an input image in this paper which includes two steps that is
spatial preprocessing (normalization, segmentation, jacobian modulation and smoothing) and
statistical analysis. These steps are implemented in SPM software package.
Figure1.Voxel Based Morphometry Steps
MRI images get preprocessed by one of the standard optimized procedure. [9]. Normalization
process done by averaging all anatomical scans which produces template and priori images. First
T1-weighted images were segmented and then processed. Normalized all grey/white matter
5. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
61
images to grey/white matter template. Original T1 images are then processed by white matter
normalization parameters. At last Grey matter, White matter, and cerebrospinal fluid gets
segmented from normalized images. To preserve initial volumes, jacobian determinants whish
gets derived from spatial normalization step were multiplied with grey matter, white matter voxel
values to get an outcome of modulated white matter images.
12 mm FWHM isotropic Gaussian kernel can be used to spatially smooth modulated white matter
images. False positive finding risk gets minimized by kernel size. Statistical testing may be
performed on smoothed data using general linear model using covariance analysis, grey and white
matter volume which is treated as nuisance covariates based on Gaussian theory. Loss of GM and
WM volume reduction gets identified by statistical testing. False discovery rate correction at
voxel level which are used to set statistical threshold at p < 0.05.
2.6.CLASSIFICATION
Classification step can be used to classify whether the patient affected by Parkinson’s disease or
not. The training and classification is done using Neural Network. Neural network technology
offers a number of tools such as learning and adaption, generalization, robustness, feature
extraction and distributed representation. Classification and recognition problem are gets easily
solved by Artificial Neural Network.
Neuro fuzzy technology can be used for classification [6]. Neuro fuzzy technology can be
constructed by using fuzzy operations at single neuron level. It includes both the advantages of
both ANN and fuzzy logic. ANN requires large training set to achieve high accuracy. Fuzzy
system more accurate but depends on expert knowledge.
Support Vector Machine(SVM) are supervised techniques for classification which finds a linear
hyper plane by placing the largest margin by mapping input features to a higher dimensional
space through either linear or nonlinear kernel functions. Prediction/Prognostic models gets
developed that can be used to identify the purpose of risk prediction in PD with the help of
multivariate logistic regression techniques. The probability occurrence of one out of two classes
gets modeled by binomial logistic regression. Supervised learning algorithm which requires
training data and one of the most powerful supervised classification algorithms is Support Vector
Machine (SVM). The main intent of SVM classifier is to directly focus on finding classification
boundary without probability estimation values. This classifier is also known as hard classifier.
Moreover classification may be performed with the help of probability estimation and evaluate
class-conditional probabilities are known as soft classifier [7].
Artificial Neural Network was used for classification. In this paper they create MLP topology
with patternnet function with inputs, number of hidden layer and training function. Input is
vectors with optimal position of ellipses for different images. Expected outcome may be a correct
coordinates for ellipse. Learning position from different input images based on Substantia Nigra
position is the main purpose of ANN [8]. With the help of that position new coordinates for the
new image gets easily identified.
2.6.STATISTICAL PARAMETRIC MAPPING (SPM)
This is a method for processing and analysis of neuroimages. A MATLAB is a program for
processing and analysis of functional neuroimages and molecular neuroimages. To locate valid
changes in different subject scans neuro images with the help of SPM tool. It gets run in an
MATLAB environment and freely downloadable. Spatial processing can be done by using affine
transformation. Smoothing process can be done with the help of Gaussian kernel. With the help of
masking, t test classifier can be used to diagnose PD from normal and PD patients. Statistical
analysis can be carried out with the help of General Linear Model (GLM).
6. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
Figure2.
4. RELATED WORK
Aprajita Sharma [6] presented that
applied to MRI brain slices. Self organizing map (SOM) algorithm can be used for clustering
which classify all the pixels to identified classes that identified classes are Normal or PD.
Artificial neural network implements SOM that can be used to determine the main classes, where
the number of classes can be unknown priori.
area with the help of Clustering technique.
Figure 3.Flow chart of Aparajita Sharma [6]
Thomas Jubalt [9] contributes
first identifiable stage along with this damage some of the other factors such as test report and
other environmental information gets considered to improve earlier diagnosis of PD.
symptoms get identified from this damage such as autonomic dysfunction and sleep disorder
proceed for further diagnosis. The
white matter which are associated with PD.
Jiri Blahuta [8] shows that medical ultrasound images get classifi
intelligence with experimental software with MATLAB. Main
classification of ROI substantia nigra in midbrain. Parkinson’s disease detection gets easily
diagnosed from classified images. To measure the area of
dimension of window with ROI was selected by neurologist.
better accuracy for automatic positioning
Motion
Correction
Smoothing
Spatial
Normalization
Slices of MRI
images
Ultrasound images in DICOM format
Learning SN position
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
Figure 2: SPM steps
Figure2.Statistical Parametric Mapping (SPM) steps
[6] presented that feature extraction and unsupervised clustering techniques are
Self organizing map (SOM) algorithm can be used for clustering
which classify all the pixels to identified classes that identified classes are Normal or PD.
neural network implements SOM that can be used to determine the main classes, where
the number of classes can be unknown priori. Parkinson’s disease gets diagnosed in brain stem
help of Clustering technique.
Flow chart of Aparajita Sharma [6] Reference paper
contributes that Parkinson’s disease gets affected brain stem and this is the
along with this damage some of the other factors such as test report and
other environmental information gets considered to improve earlier diagnosis of PD.
symptoms get identified from this damage such as autonomic dysfunction and sleep disorder
proceed for further diagnosis. The GOAL of this paper is to characterize the volume reduction in
white matter which are associated with PD. Steps of this paper are shown in Figure 2
shows that medical ultrasound images get classified by using artificial
intelligence with experimental software with MATLAB. Main GOAL of this paper
classification of ROI substantia nigra in midbrain. Parkinson’s disease detection gets easily
diagnosed from classified images. To measure the area of substantia nigra is one of the
dimension of window with ROI was selected by neurologist. This paper future work will base on
better accuracy for automatic positioning
Smoothing General Linear
Model
Parameter
Estimates
Re-Processing Extracting
Features
Separation Of
classes
Ultrasound images in DICOM format
Learning SN position Measuring area of SN
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
62
ture extraction and unsupervised clustering techniques are
Self organizing map (SOM) algorithm can be used for clustering
which classify all the pixels to identified classes that identified classes are Normal or PD.
neural network implements SOM that can be used to determine the main classes, where
Parkinson’s disease gets diagnosed in brain stem
Parkinson’s disease gets affected brain stem and this is the
along with this damage some of the other factors such as test report and
other environmental information gets considered to improve earlier diagnosis of PD. Non motor
symptoms get identified from this damage such as autonomic dysfunction and sleep disorders to
this paper is to characterize the volume reduction in
in Figure 2.
ed by using artificial
of this paper is a
classification of ROI substantia nigra in midbrain. Parkinson’s disease detection gets easily
substantia nigra is one of the goals. The
This paper future work will base on
Separation Of
classes
Measuring area of SN
7. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
63
Figure 4.Flow chart of whole processing of Jiri Blahuta [8]
R. Prashanth [7] proposed that recent neuroimaging techniques such as dopaminergic imaging
use Single photon emission computed tomography (SPECT) are used to detect even early stages
of this disease. Here use the striatal binding values (SBR) that are calculated from the PPMI
database. This can be act as features which are passed to the classification step. SVM
classification can be used to classify whether patient affected by Parkinson’s disease gets easily
identified at an early stage.
The contributions of the present study are
(1) High classification performance can be obtained using only four SBR feature without any
feature selection techniques making the system simple.
(2) Sample size used for this paper is very high.
(3) High performance gets obtained.
Figure 5 Flow chart of whole processing of Prashanth [7]
Dan Long [23] proposed that diagnosis of Parkinson’s disease done by the integration of
information from a variety of imaging modalities. Here they used the imaging modalities as
resting state functional magnetic resonance imaging (rsfMRI). This paper deals with that which is
the combination of both functional and structural imaging technology. They construct a non
invasive multimodal magnetic resonance imaging algorithm framework for diagnosis of early PD.
These systems which also can be used to distinguish between PD and Normal Group. Here they
used template based approach to extract features and did not use region of interest (ROI) because
ROI method is not suitable for the proper qualification of these changes.
Figure 6.Flow chart of whole processing of Dan Long [23]
I.A.Illan [28] proposed that computer aided diagnosis (CAD) system was designed to diagnose
Parkinson’s disease at an early stage. Main aim of this work is to evaluate the SVM classification
performance in DaTSCAN images, which gets compared to other possible classifiers. Processing
involves in this tool are SPECT images are spatially normalized using the SPM8 software to
performs that particular voxel in one position of one subject scan may corresponds to same voxel
Features extracted from PPMI database as SBR values
Feature Analysis
Classification using SVM Logistic regression classification
RsfMRI Input
Image
Feature
Extraction
Feature
Selection
Feature
fusion
SVM
classifier
8. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
64
in another subject scan. This spatial Normalization technique based on affine transformation.
After spatial normalization the second step is intensity normalization this can be used to establish
comparisons between the areas of specific activity and the non specific activity. At last with the
help of that normalization values that can be classified by three classification algorithms. 1.
Support vector machines (SVM), 2. K-nearest neighbor, 3. Nearest mean. SVM classification
provides best classification rates to near 90%. Some of the challenging task may be tackling by
SVM.
Figure7.Flow chart of whole processing of Illan [23]
A Ram Yu [30] proposed that early diagnosis of Parkinson’s disease for rat brain. In this paper
they used PET images. This will leads to good quality of the brain and it is one of the most
important to visualize all of the brain structures in good quality. All of the processing is involved
in SPM8 software. PET image having good quality was selected as a target image. Then all
individual images were spatially normalized to the target image by using affine transformation.
Extract the particular striatum position .To increase the signal to noise ratios Gaussian Kernel
smoothing was used for smoothing purpose. Binding potential can be calculated in the second
frame of PET data set by mean count of striatum minus mean count of cerebellum divided by
mean count of cerebellum. To differentiate normal and PD classes two sample t-tests were
performed by Binding potential calculation. Without the help of the SPM tool these techniques
can be applied in MATLAB. They also extend to extract 2D shape and Volume Features.
Figure 8.Flow chart of whole processing of Ram Yu [30]
Input PET image
Pre-Processing
Affine
Transformation
Gaussian Kernel
DaTSCAN Input
Image
Pre-Processing
Spatial
Normalization
Integral
Normalization
Classification
9. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
65
The Comparison of Performance, Advantages, Disadvantages of the above discussed papers has
been organized and the detailed comparison table is shown below.
Paper Referred Performance Measure Advantages Disadvantages
Ref Paper [6] Accuracy of Diagnosis
is Moderate.
NeuroFuzzy
technique overcomes
the drawback of
ANN.
1. ANN require
large training set to
achieve high
accuracy
2. Difficult to
identify
Abnormalities in
MRI brain images.
Ref Paper [8] Accuracy of Diagnosis
is Approximately 70%.
ANN will be better
trained for more
input samples which
lead to high
accuracy.
Small number of
training samples
gets used.
Ref Paper [9] Accuracy of diagnosis
is moderate.
Registration and
segmentation errors
get avoided.
Brain stem nature
gets vary due to
registration.
Ref Paper [7] SVM classifier
produces Accuracy of
more than 96%
1. Used four features
without need of any
feature selection
technique.
2. High Performance.
Classification
accuracy with
logistic regression
can be lower
compared to SVM.
Ref Paper [23] Diagnosis Yields good
results with
Accuracy=86.96%,
sensitivity=78.95%
Specificity=92.59%
1. Multiple
Modalities can be
used to extract more
features.
2. Use template
Based Approach to
extract features not
Small dataset
samples can be used
and this dataset may
not applicable to
other dataset.
10. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.6,December 2014
66
use ROI.
Ref Paper [28] Diagnosis Yields good
results with
Sensitivity=89.02%
Specificity=93.21%
1. Remove
Expensive manual
operation.
2. Reduce time and
cost.
Unavoidable errors
gets occurred during
Normalization
Ref Paper [30] High Localization
accuracy in diagnosis.
PET images shows a
good quality of
visualization of brain
Structures.
Hard to perform the
spatial
Normalization using
full frames data set.
Easy to process in
separate data set
5.CONCLUSION
Parkinson’s disease caused by reduction of dopamine in nerve terminals and reduction cannot be
diagnosed easily. Up to date there is no treatment, blood test to finding this disease. Over the past
few years, neurology communities’ researchers have received substantial attention for the
recognition of substantia nigra, Striatum in brain stem. This attention may lead the researchers to
working in diverse fields to detect PD. This survey paper proposed for automatic recognition of
Striatum, substantia nigra in brain stem. PET imaging test can be used to detect reduction of
dopamine in brain. Classification algorithms are used to distinguish between normal and PD.
Therefore accurate diagnosis of PD and number of wrong decisions gets reduced with the help of
CAD and diagnosis algorithms. Extracting features and selecting appropriate features will yields
good classification results. To improve the overall performance of CAD and diagnosis algorithms,
further developments in each algorithm step may require.
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Authors
A. Valli received B.E. (CSE) degree in 2013 and presently doing post-graduation in Dr.Sivanthi Aditanar
College of Engineering, Tiruchendur. Area of interest is Medical image processing. She is affiliated to
student member in Computer Society of India (CSI) and active associate member of Institution of Engineers
(AMIE).
Dr.G.Wiselin Jiji received B.E (CSE) degree in 1994 and M.E (CSE) degree in 1998 respectively. She got
her doctorate degree from Anna University in 2008. Classification, segmentation, medical image
processing are her area of research interests. She is a life member in ISTE, Institution of Engineers (India)
and Biomedical Engineering Society of India and a member of Computer Society of India.