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.
Mri brain image segmentatin and classification by modified fcm &svm akorithmeSAT Journals
Abstract Brain Tumor detection is challenging task in biomedical field. Image segmentation is a key step from the image processing to image analysis, it occupy an important place. The manual segmentation of brain image is challenging and time consuming task. An automated system overcomes the drawbacks as well as it segments the white matter, grey matter, cerebrospinal fluid and edema. This clustering approach is particularly used for brain tumor detection in abnormal MR images. In this paper the application of Modified FCM algorithm for Brain tumor detection and its classification by SVM algorithm is focused. The Magnetic Resonance image is converted in to vector format and that is given as input to the modified fuzzy c-means algorithm. In modified fuzzy c-means the steps are: initial fuzzy partitioning and fuzzy membership generation Cluster updation based on objective function, Assigning labels to pixels of each category and display segmented image that will give more meaningful regions to analyze. This clustered images served as inputs to SVM. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes. Keywords: Clustering, Classification, Fuzz C-Means, Support Vector Machine, MRI, Brain Tumor.
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.
Comparitive study of brain tumor detection using morphological operatorseSAT Journals
Abstract
Segmentation divides an image into foreground object and the background object. In our case foreground object is brain tumor and background is CSF, white matter, and grey matter. Aim of our study is to detect the tumor and remove the background completely and compare the morphological operations that can be used for this purpose. Segmentation remains a challenging area for researchers since many segmentation methods results in over segmentation or under segmentation and hence, leads to the false interpretation of the results. The proposed work is the comparative study of the morphological segmentation methods for segmenting brain tumor from MRI images. Before segmentation, filtration process is carried out using two method, Non Local mean filter and median filter and their results are compared using MSE and PSNR. NL mean filter preserves sharp edges and fine details in an image hence, preferred over median filter. Also tumor location is identified, to get an approximate idea about the position of the tumor in the brain i.e. in which part the brain tumor is located. The tumor is identified by using different algorithms which are based on morphology such as watershed segmentation, morphological erosion, and hole filling algorithm and comparison between them is carried out based on parameters like accuracy, sensitivity and elapsed time. Each of the segmentation results are compared with the tumor obtained using interactive tool present in MATLAB R2013b.
Keywords: Brain tumor, MRI images, Image segmentation, Morphology, Erosion, Thresholding, Hole filling, Watershed segmentation
Mri brain image segmentatin and classification by modified fcm &svm akorithmeSAT Journals
Abstract Brain Tumor detection is challenging task in biomedical field. Image segmentation is a key step from the image processing to image analysis, it occupy an important place. The manual segmentation of brain image is challenging and time consuming task. An automated system overcomes the drawbacks as well as it segments the white matter, grey matter, cerebrospinal fluid and edema. This clustering approach is particularly used for brain tumor detection in abnormal MR images. In this paper the application of Modified FCM algorithm for Brain tumor detection and its classification by SVM algorithm is focused. The Magnetic Resonance image is converted in to vector format and that is given as input to the modified fuzzy c-means algorithm. In modified fuzzy c-means the steps are: initial fuzzy partitioning and fuzzy membership generation Cluster updation based on objective function, Assigning labels to pixels of each category and display segmented image that will give more meaningful regions to analyze. This clustered images served as inputs to SVM. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes. Keywords: Clustering, Classification, Fuzz C-Means, Support Vector Machine, MRI, Brain Tumor.
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.
Comparitive study of brain tumor detection using morphological operatorseSAT Journals
Abstract
Segmentation divides an image into foreground object and the background object. In our case foreground object is brain tumor and background is CSF, white matter, and grey matter. Aim of our study is to detect the tumor and remove the background completely and compare the morphological operations that can be used for this purpose. Segmentation remains a challenging area for researchers since many segmentation methods results in over segmentation or under segmentation and hence, leads to the false interpretation of the results. The proposed work is the comparative study of the morphological segmentation methods for segmenting brain tumor from MRI images. Before segmentation, filtration process is carried out using two method, Non Local mean filter and median filter and their results are compared using MSE and PSNR. NL mean filter preserves sharp edges and fine details in an image hence, preferred over median filter. Also tumor location is identified, to get an approximate idea about the position of the tumor in the brain i.e. in which part the brain tumor is located. The tumor is identified by using different algorithms which are based on morphology such as watershed segmentation, morphological erosion, and hole filling algorithm and comparison between them is carried out based on parameters like accuracy, sensitivity and elapsed time. Each of the segmentation results are compared with the tumor obtained using interactive tool present in MATLAB R2013b.
Keywords: Brain tumor, MRI images, Image segmentation, Morphology, Erosion, Thresholding, Hole filling, Watershed segmentation
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Non negative matrix factorization ofr tuor classificationSahil Prajapati
The PPT aware about you the concept of Non Negative Matrix Factorization and how theses techniques can be used to treat cancer by the use of the coding such as a MATLAB,LABVIEW software to locate the tumor or the cancer part with the different approaches and tachniques.
Go through the PPT to know and how one can improvise my work for better results??
Please help me if one come up with other techniques.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An automated approach for the recognition of bengali license plates presentationMD Abdullah Al Nasim
Automatic Number Plate Recognition (ALPR) is a system for automatically identifying the license plates of any vehicle. This process is important for tracking, ticketing, and any billing system, among other things. With the use of information and communication technology (ICT), all systems are being automated, including the vehicle tracking system. This study proposes a hybrid method for detecting license plates using characters from them. Our captured image information was used for the recognition procedure in Bangladeshi vehicles, which is the topic of this study. Here, for license plate detection, the YOLO model was used where 81\% was correctly predicted. And then, for license plate segmentation, Otsu's Thresholding was used and eventually, for character recognition, the CNN model was applied. This model will allow the vehicle's automated license plate detection system to avoid any misuse.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Brain tumour segmentation based on local independent projection based classif...eSAT Journals
Abstract
Brain tumour detection and segmentation is most important and challenging task in early tumour diagnosis. There are various
segmentation methods available but they are still challenging methods because of its complex characteristics such as ambiguous
boundaries and high diversity. To overcome this problem we are going to implement automatic brain tumour detection and
segmentation method by using local independent projection based classification. In this method we are going to consider tumour
segmentation as a classification problem. In this paper locality is important in calculations of projections. Also local anchor
embedding is used to solve linear projection weights. The softmax regression model is used to improve classification performance.
In this study we used MRI images as training and testing data. Finally the brain tumour is classified into tumour and edema
region. The area of tumour region is calculated in pixels.
Key Words: Brain tumour detection & segmentation, local independent projection based classification, local anchor
embedding and softmax regression.
Brain Tumor Detection Using Artificial Neural Network Fuzzy Inference System ...Editor IJCATR
Manual classification of brain tumor is time devastating and bestows ambiguous results. Automatic image classification is
emergent thriving research area in medical field. In the proposed methodology, features are extracted from raw images which are then
fed to ANFIS (Artificial neural fuzzy inference system).ANFIS being neuro-fuzzy system harness power of both hence it proves to be
a sophisticated framework for multiobject classification. A comprehensive feature set and fuzzy rules are selected to classify an
abnormal image to the corresponding tumor type. This proposed technique is fast in execution, efficient in classification and easy in
implementation.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Non negative matrix factorization ofr tuor classificationSahil Prajapati
The PPT aware about you the concept of Non Negative Matrix Factorization and how theses techniques can be used to treat cancer by the use of the coding such as a MATLAB,LABVIEW software to locate the tumor or the cancer part with the different approaches and tachniques.
Go through the PPT to know and how one can improvise my work for better results??
Please help me if one come up with other techniques.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An automated approach for the recognition of bengali license plates presentationMD Abdullah Al Nasim
Automatic Number Plate Recognition (ALPR) is a system for automatically identifying the license plates of any vehicle. This process is important for tracking, ticketing, and any billing system, among other things. With the use of information and communication technology (ICT), all systems are being automated, including the vehicle tracking system. This study proposes a hybrid method for detecting license plates using characters from them. Our captured image information was used for the recognition procedure in Bangladeshi vehicles, which is the topic of this study. Here, for license plate detection, the YOLO model was used where 81\% was correctly predicted. And then, for license plate segmentation, Otsu's Thresholding was used and eventually, for character recognition, the CNN model was applied. This model will allow the vehicle's automated license plate detection system to avoid any misuse.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Brain tumour segmentation based on local independent projection based classif...eSAT Journals
Abstract
Brain tumour detection and segmentation is most important and challenging task in early tumour diagnosis. There are various
segmentation methods available but they are still challenging methods because of its complex characteristics such as ambiguous
boundaries and high diversity. To overcome this problem we are going to implement automatic brain tumour detection and
segmentation method by using local independent projection based classification. In this method we are going to consider tumour
segmentation as a classification problem. In this paper locality is important in calculations of projections. Also local anchor
embedding is used to solve linear projection weights. The softmax regression model is used to improve classification performance.
In this study we used MRI images as training and testing data. Finally the brain tumour is classified into tumour and edema
region. The area of tumour region is calculated in pixels.
Key Words: Brain tumour detection & segmentation, local independent projection based classification, local anchor
embedding and softmax regression.
Brain Tumor Detection Using Artificial Neural Network Fuzzy Inference System ...Editor IJCATR
Manual classification of brain tumor is time devastating and bestows ambiguous results. Automatic image classification is
emergent thriving research area in medical field. In the proposed methodology, features are extracted from raw images which are then
fed to ANFIS (Artificial neural fuzzy inference system).ANFIS being neuro-fuzzy system harness power of both hence it proves to be
a sophisticated framework for multiobject classification. A comprehensive feature set and fuzzy rules are selected to classify an
abnormal image to the corresponding tumor type. This proposed technique is fast in execution, efficient in classification and easy in
implementation.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
An evaluation of automated tumor detection techniques of brain magnetic reson...Salam Shah
Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.
A Review on Brain Disorder Segmentation in MR ImagesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Today, computer aided system is widely used in various fields. Among them, the brain tumor detection is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of brain tumors for cancer diagnosis, from large amount of Magnetic Resonance Imaging MRI images generated in clinical routine, is a difficult and time consuming task or even generates errors. So, the automatic brain tumor segmentation is needed to segment tumor. The purpose of the thesis is to detect the brain tumor quickly and accurately from the MRI brain image. In the system, the average filter is used to remove noise and make smooth an input MRI image and threshold segmentation is applied to segment tumor region from MRI brain images. Region properties method is used to detect the tumor region exactly. And then, the equation of the tumor region in the system is effectively applied in any shape of the tumor region. Moe Moe Aye | Kyaw Kyaw Lin "Brain Tumor Detection System for MRI Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27864.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/27864/brain-tumor-detection-system-for-mri-image/moe-moe-aye
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Securing your Kubernetes cluster_ a step-by-step guide to success !
B04530612
1. IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org
ISSN (e): 2250-3021, ISSN (p): 2278-8719
Vol. 04, Issue 05 (May. 2014), ||V3|| PP 06-12
International organization of Scientific Research 6 | P a g e
A review on various brain tumor detection techniques in brain
MRI images
Komal Sharma1
, Akwinder Kaur2
, Shruti Gujral3
Research Scholar1
, Assistant Professor2
, Assistant Professor3
Department of Computer Science, Chandigarh University, Gharuan, Mohali, India
Abstract: - Magnetic resonance imaging is important imaging technique used in the detection of brain tumor.
Brain tumor is one of the most dangerous diseases occurring among the human beings. Brain MRI plays a very
important role for radiologists to diagnose and treat brain tumor patients. Study of the medical image by the
radiologist is a time consuming process and also the accuracy depends upon their experience. Thus, the
computer aided systems becomes very necessary as they overcome these limitations. Several automated
methods are available, but automating this process is very difficult because of different appearance of the
tumor among the different patients. There are various feature extraction and classification methods which are
used for detection of brain tumor from MRI images. In this paper, various approaches are reviewed
enlightening the advantages and disadvantages of these methods.
Keywords: - Feature extraction, Image segmentation, Clustering, Neural Network, MRI image, Tumor detection
I. INTRODUCTION
MRI is a fairly new technique that has been used by radiologist to envision the structure and function
of the human body. The MRI scanner uses magnetic fields and the radio waves to create images of various
tissues, organs and some other structures within the body. MRI instrument uses three electromagnetic fields - i)
Static field which is very strong static magnetic field which is used to polarize the hydrogen nuclei of water
molecules, ii) Gradient field which is weaker time varying field used for spatial encoding, iii) Weak radio
frequency field which is used for manipulation of hydrogen nuclei which produce measurable signals collected
through Radio Frequency antenna.MRI of the brain can be obtained to obtain the changes concerned with
bleeding and also when the brain has been starved of oxygen after a stroke. It can be used to characterize tumors
(benign or malignant) and check their progression.
MRI imaging technique is very useful in early detection of abnormalities in the brain. It is based on
nuclear magnetic resonance phenomenon. MRI uses an effective magnetic field to line up the nuclear
magnetization of hydrogen atoms in water in the body of the human beings.
The human body is consisting of various types of cells and every cell has its own specific function. On
losing the ability to grow properly, cells start growing in an improper fashion and in an unordered manner. Extra
cells are grouped together to form a mass of tissue which is called tumor. These cells can be categorized in two
ways: Benign and malignant. Benign tumors do not contain cancer cells and malignant tumors contain tumor
cells.
Various researches and studies have been carried out on both type of tumors till date. These studies
show that rate of tumors in human body has been increasing and there is no clue of their such a fast rate of
increasing. Medical imaging techniques and analysis tools which play a vital role in the field of clinical study
also enable the doctors and radiologists to arrive at a specific diagnosis.
The examination involving MRI brain classification on the MRI images is also required. The aim of
classification is to group items which have similar feature values into categories. A number of studies has been
done on brain MRI classification. The classification is done using techniques such as neural networks , fuzzy c-
means, k-nearest neighbors and Support Vector Machine (SVM) as shown in figure 1.
Fig 1. Classification Techniques
2. A review on various brain tumor detection techniques in brain MRI images
International organization of Scientific Research 7 | P a g e
The MR images data is by nature are very complex . Accurate diagnosis of MR images data is not an
easy task and is always time consuming. In some extreme scenario, diagnosis with wrong result and delay in
delivery of a correct diagnosis decision could occur due to the complexity and cognitive process of which it is
involve.
The diagnosis of magnetic resonance human brain images is possible via supervised techniques such as
artificial neural networks and support vector machine (SVM), and unsupervised classification techniques
unsupervised such as self-organization map (SOM) and fuzzy c-means combined with feature extraction
techniques like texture feature extraction, intensity feature extraction. Some other supervised classification
techniques such as k-nearest neighbors (k-NN) also group pixels based on their similarities in each feature
image can be used to classify the normal/pathological T2-wieghted MRI images.
Brain tumour can occur in different parts of the brain. The most common brain tumours are Gliomas,
Oligodendroglioma ,Meningiomas, Glioblastoma, Pituitary adenomas and Nerve sheath tumours. Detection and
segmentation of brain tumour- in MR images is very important in medical diagnosis. Brain tumor is the most
dangerous diseases occurring commonly among the human beings, so study of brain tumor is very important.
II. OVERVIEW
Natarajan et al. [1] proposed brain tumor detection method for MRI brain images. The MRI brain
images are first pre-processed using median filter, then segmentation of image is done using threshold
segmentation and morphological operations are applied and then finally, the tumor region is obtained using
image subtraction technique. This approach gives the exact shape of tumor in MRI brain image. Joshi et al. [2]
proposed brain tumor detection and classification system in MR images by first extracting the tumor portion
from brain image, then extracting the texture features of the detected tumor using Gray Level Co-occurrence
Matrix (GLCM) and then classified using neuro-fuzzy classifier. Amin and Mageed [3] proposed neural network
and segmentation base system to automatically detect the tumor in brain MRI images. The Principal Component
Analysis (PCA) is used for feature extraction and then Multi-Layer Perceptron (MLP) is used classify the
extracted features of MRI brain image. The average recognition rate is 88.2% and peak recognition rate is
96.7%. Sapra et al. [4] proposed image segmentation technique to detect brain tumor from MRI images and then
Probabilistic Neural Network (PNN) is used for automated brain tumor classification in MRI scans. PNN
system proposed handle the process of brain tumor classification more accurately. Suchita and Lalit [5]
proposed unsupervised neural network learning technique for classification of brain MRI images. The MRI
brain images are first pre-processed which include noise filtering, edge detection, then the tumor is extracted
using segmentation. The texture features are extracted using Gray-Level Co-occurrence Matrix(GLCM) and
then Self-Organizing Maps (SOM) are used to classify the brain as normal or abnormal brain, that is, whether it
contain tumor or not. Rajeshwari and Sharmila [6] proposed pre-processing techniques which are used to
improve the quality of MRI image before using it into an application. The average, median and wiener filters are
used for noise removal and interpolation based Discrete Wavelet Transform (DWT) technique is used for
resolution enhancement. The Peak Signal to Noise Ratio (PSNR) is used for evaluation of these techniques.
George and Karnan [7] proposed MRI image enhancement technique based on Histogram Equalization
and Center Weighted Median (CWM) filter as they are used to enhance the MRI image more effectively. Daljit
Singh et al. in [8] proposed a hybrid technique for automatic classification of MRI images by first extracting the
features using Principal Component Analysis (PCA) and Gray-Level Co-occurrence Matrix(GLCM) and then
extracted features are fed as an input to Support Vector Machine(SVM) classifier which classifies the brain
image as normal or abnormal. Gadpayleand and Mahajani [9] proposed brain tumour detection and classification
system. The tumor is extracted using segmentation and then texture features are extracted using GLCM and
finally the BPNN and KNN classifiers are used to classify the MRI brain image into normal or abnormal brain.
The accuracy is 70% using KNN classifier and 72.5% by using BPNN classifier. Shasidhar et al. in [10]
proposed modified Fuzzy C-Means (FCM) algorithm for MR brain tumor detection. The texture features are
extracted from brain MR image and then modified FCM algorithm is used for brain tumor detection. The
average speed-ups of as much as 80 times a traditional FCM algorithm is obtained using the modified FCM
algorithm. The modified FCM algorithm is a fast alternative to the traditional FCM technique.
III. FEATURE EXTRACTION
Large amount of information is needed to represent an image and this information occupies large
amount of memory. The features are extracted from the image. The extracted features contains the relevant
information about the image. The extracted features are used as input to the classifier for classification. The
features extracted methods for an image are described below.
3. A review on various brain tumor detection techniques in brain MRI images
International organization of Scientific Research 8 | P a g e
3.1 Texture Based Features:-
The texture extraction means extracting the homogeneity of an image and similarity between regions of
an image. Texture feature classifies MRI of brain helps in detection of tumor in brain MRI image. The various
texture features are described below:-
3.1.1 Energy:-
It gives the measure of homogeneity. It is also known as angular second moment or uniformity.
E= 𝑝(𝑖. 𝑗)2𝑁𝑔−1
𝑗=0
𝑁𝑔−1
𝑖=𝑜 .............(1)
3.1.2 Contrast:-
It gives the measure of the intensity variation between the reference pixel and the neighbouring pixel.
Contrast= 𝑛2
𝑝(𝑖. 𝑗)2𝑁𝑔−1
𝑗=0
𝑁𝑔−1
𝑖=0
𝑁𝑔−1
𝑛=0 ...........(2)
3.1.3 Correlation :-
It gives the measure of the intensity variation between the reference pixel and neighborhood pixel.
Correlation=
1
𝜎 𝑥 𝜎 𝑦 (𝑖, 𝑗) 𝑝(𝑖, 𝑗)2
− 𝜇 𝑥
𝑁𝑔−1
𝑗=0
𝑁𝑔−1
𝑖−0 𝜇 𝑦 .....(3)
3.1.4 Sum of Squares (Variance) :-
It gives the measure of the gray tone variance of an image.
Variance, 𝜎2
= (1 − 𝜇)2
. 𝑝(𝑖, 𝑗)
𝑁𝑔−1
𝑗=0
𝑁𝑔−1
𝑖=0 ...........(4)
3.1.5 Inverse Difference Moment (IDM):-
It gives the measure of the local homogeneity of an image.
IDM=
1
1+(𝑖−𝑗 )2 . 𝑝 𝑖. 𝑗
𝑁𝑔−1
𝑗=0
𝑁𝑔−1
𝑖=0 ...........(5)
3.1.6 Entropy :-
It gives the amount of information of an image which is used for image compression.
Entropy, EN= 𝑝 𝑖. 𝑗 𝑙𝑜𝑔 𝑝 𝑖, 𝑗
𝑁𝑔−1
𝑗=0
𝑁𝑔−1
𝑖=0 ..........(6)
Advantages of Texture based features:-
It shows relatively good discrimination between the tumor and its surrounding edema.
Disadvantages of Texture based features:-
These methods cannot segment all components of the tumor.
These features are very sensitive to noise and inhomogeneity.
3.2 Intensity Based Features:-
Intensity based features are most commonly used features used in the classification. The intensity based
features include mean, median, mode, skewness, kurtosis, energy, entropy. Let f(x,y) - a two dimensional image,
h(i) - the intensity level of an image, N
g
- the total number of gray levels in the entire image and p(i) - the
probability density.
3.2.1 Mean:-
The mean gives the average level intensity of an image.
Ng-1
Mean, µ= ∑ i. p(i) ............(7)
i=0
4. A review on various brain tumor detection techniques in brain MRI images
International organization of Scientific Research 9 | P a g e
where p(i)=
ℎ 𝑖
𝑁 𝑥 𝑁 𝑦
,i=0,1,2,..............,N-1 .........(8)
Ng-1 Ng-1
h(i)= ∑ ∑ α (f(x,y),i),i=0,1,2,,.............,N-1 ...........(9)
x=0 y=0
α(i.j)= 1;i = j
0;i ≠ j ..................(10)
3.2.2 Variance:-
The variance gives variation of the intensity around the mean.
Variance, 𝜎2
= (1 −
𝑁𝑔−1
𝑖=0 𝜇)2
. 𝑝 𝑖 .................(11)
3.2.3 Standard Deviation:-
SD, 𝜎 = (1 − 𝜇)2. 𝑝(𝑖)
𝑁𝑔−1
𝑖=0 ............(12)
3.2.4 Skewness:-
The skewness gives the symmetry of an image.
𝜇3
= 𝜎−3
(1 − 𝜇)3
. 𝑝(𝑖)
𝑁𝑔−1
𝑖=0 ...............(13)
3.2.5 Energy:
Energy gives the measure of sum of the squared elements.
Energy, E= (𝑝(𝑖))2𝑁𝑔−1
𝑖=0 ..........(14)
3.2.6 Entropy:
Entropy gives the measure of uncertainty in a random variable.
Entropy, En= − 𝑝 𝑖 𝑙𝑜𝑔2
𝑁𝑔−1
𝑖=0 𝑝 𝑖 .........(15)
Advantages of Intensity based features:-
These features directly indicate the physical characteristics of the tissues in brain MRI.
Identification of tumor is fast by the using physical characteristics.
Disadvantages of Intensity based features:-
Different tissues in MRI may have overlapping intensity values.
Other information like anatomic knowledge should also be considered for achieving good results.
IV. CLASSIFICATION TECHNIQUES
There are various classification techniques used for classifying brain as normal or abnormal . These
classification techniques are described below :-
4.1 Artificial Neural Network :-
In this technique, the image is mapped into a Neural Network. The neural network works in two
phases- the training phase and the testing phase. Firstly the neural network was trained with training examples
in the training phase. After training, the neural network is tested on the unknown instances. Neural network
technique includes important step that is feature extraction. Feature extraction is very important as the features
that are extracted forms the input part of the neural network.
5. A review on various brain tumor detection techniques in brain MRI images
International organization of Scientific Research 10 | P a g e
Artificial Neural Network is divided into 2 categories:-
1. Feed-Forward Neural Network.
2. Recurrent Network or Feed-Backward Network.
In feed-forward neural network, the neurons are arranged in layers and they have unidirectional
connections between them. They produce only one set of output values. They are called as static network
because in this the output values are produced only based on current input. The output values does not depend
on previous input values. They are also called as memory less network. In feedback network, the neurons have
bidirectional connections between them. Feedback or Recurrent networks produce a set of values which
depends on the previous input values. Feedback network is also known as dynamic network because the output
values always depend on the previous input values.
Back Propagation algorithm is used in feed-forward neural network. In this network, the neurons are
arranged in layers and send the output in the forward direction. The errors generated are back propagated in the
backward direction to the input layer. The network receives the input by neurons in the input layer of the neural
network and the output of the network is given by the neurons on an output layer of the neural network. The
neural network consists of one or more intermediate hidden layers. In back propagation algorithm, the
supervised learning is used. The error between the input and the computed output is calculated and back
propagated. The network is trained with random weights and then later the weights are adjusted by back
propagation to get the minimal error. The network is perfect if the error is minimal. In back propagation, the
weights are changed each time such that the error reduces gradually. This is repeated until there is no change in
the error.
Advantages of artificial neural network :-
The neural networks have high parallel ability and fast computing.
Expert intervention is reduced during the whole process.
Disadvantages of artificial neural network :-
Some of the information should be known beforehand.
They should be first trained using learning process beforehand.
Period of training neural networks may be very long.
4.2 Fuzzy C-Means :-
It is a method of clustering. In this method, one pixel may belong to two or more clusters which
represents group. In this algorithm, the finite collection of pixels are partitioned into a group of "c" fuzzy
clusters according to some given criterion. The objective function of this algorithm is defined as the sum of
distances between cluster centers and patterns. Different types of similarity measures are used to identify classes
depending on the data and the application in which it is to be used. Some examples which can be used as
similarity measures are intensity distance and connectivity.
The algorithm contain following steps:-
Initialize the matrix M.
Centers vectors are calculated.
Perform K steps until the termination value is reached.
Advantages of fuzzy c-means :-
It is very simple and fast algorithm.
This algorithm is more robust to noise and provides better segmentation quality.
Disadvantages of fuzzy c-means :-
It considers only image intensity values.
4.3 Support Vector Machines(SVM) :-
SVM is a supervised classifier with associated learning algorithm. The SVM based on the training
samples. It attempts to minimize the bound on the generalization error . The generalisation error is the error
made by the learning machine on the test data not used during training phase. Thus, the SVM always performs
well when applied to data which is outside the training set. SVM uses this advantage and focus on the training
examples which are difficult to classify. These “borderline” training examples which are difficult to classify
are called as support vectors. SVM formulation is somewhat modified by adding least squares term in its cost
function. It helps to circumvent the need to solve a more difficult quadratic programming problem and only
6. A review on various brain tumor detection techniques in brain MRI images
International organization of Scientific Research 11 | P a g e
requires the solution of a set of linear equations. This approach significantly reduces the complexity and
computation in solving the problem of classification . It is based on the hyperplane and the hyperplane
maximizes the separating margin among the two classes.
Support Vector Machines (SVM) works in the two stages- the training stage and the testing stage. SVM
trains itself by learning features which are given as input to its learning algorithm. During the training phase,
SVM selects the suitable margins between its two classes. Artificial neural network has a number of issues like
having local minima and selection of number of neurons for each problem. Thus, the SVM classifier has no
local minima.
SVM is a systematic and effective method for two class problems. The MRI brain images are classified
into two separate classes such as normal class and abnormal class using SVM classifier. The SVM classifier
method is better than rule based systems.
Advantages of support vector machines :-
This algorithm has high generalization performance.
It works well in case of high dimensional feature space.
This algorithm works independent of the dimensionality of the feature space.
The results given by support vector machines are very accurate.
Disadvantages of support vector machines :-
The training time is very long.
This algorithm is highly dependent on the size of data.
4.4 K-Nearest Neighbour (KNN) :-
The KNN algorithm is based on a distance function (Euclidean Distance) and a voting function is used
for the k-Nearest Neighbours. The distance metric used is the Euclidean distance, It shows the higher accuracy
and stability for MRI images than other classifiers. The KNN algorithm has a slow running time. The
segmentation steps of KNN algorithm are as following :-
Determine k value where k gives the number of nearest neighbors.
Distance between query instance and all the training samples is calculated.
On the basis of kth minimum distance , the distance is sorted.
The majority class is assigned.
The class is determined.
The brain abnormalities are segmented.
Advantages of KNN algorithm :-
KNN algorithm is fairly simple to implement.
Real time image segmentation is done using KNN algorithm as it runs more quickly.
Disadvantages of KNN algorithm :-
There is some possibility of yielding an erroneous decision if the obtained single neighbour is an outlier of
some other class.
V. CONCLUSION
In this paper, various methods and techniques that are being used to detect the brain tumor from MRI
images of brain are reviewed. A method which performs well for one MRI brain image may not perform for the
other MRI brain image. So, it is very hard to achieve a general method that can be used for all MRI brain images.
The merits and demerits of various techniques for brain tumour detection are analyzed in this paper. Several
hybrid approaches may be developed through the combination of these techniques.
REFERENCES
[1] Natarajan P, Krishnan.N, Natasha Sandeep Kenkre, Shraiya Nancy, Bhuvanesh Pratap Singh, "Tumor
Detection using threshold operation in MRI Brain Images" , IEEE International Conference on
Computational Intelligence and Computing Research, 2012.
[2] Dipali M. Joshi, N. K. Rana, V. M. Misra, " Classification of Brain Cancer Using Artificial Neural
Network" , IEEE International Conference on Electronic Computer Technology ,ICECT ,2010.
[3] Safaa E.Amin, M.A. Mageed," Brain Tumor Diagnosis Systems Based on Artificial Neural Networks and
Segmentation Using MRI" , IEEE International Conference on Informatics and Systems, INFOS 2012.
7. A review on various brain tumor detection techniques in brain MRI images
International organization of Scientific Research 12 | P a g e
[4] Pankaj Sapra, Rupinderpal Singh, Shivani Khurana, "Brain Tumor Detection Using Neural Network" ,
International Journal of Science and Modern Engineering, IJISME ,ISSN: 2319-6386, Volume-1, Issue-9,
August 2013.
[5] Suchita Goswami, Lalit Kumar P. Bhaiya, " Brain Tumor Detection Using Unsupervised Learning based
Neural Network" , IEEE International Conference on Communication Systems and Network
Technologies,2013.
[6] S. Rajeshwari, T. Sree Sharmila, "Efficient Quality Analysis of MRI Image Using Preprocessing
Techniques" ,IEEE Conference on Information and Communication Technologies, ICT 2013.
[7] E. Ben George, M.Karnan, "MRI Brain Image Enhancement Using Filtering Techniques", International
Journal of Computer Science & Engineering Technology ,IJCSET, 2012.
[8] Daljit Singh, Kamaljeet Kaur, "Classification of Abnormalities in Brain MRI Images Using GLCM, PCA
and SVM" , International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958,
Volume-1, Issue-6, August 2012.
[9] Prachi Gadpayleand, P.S. Mahajani, "Detection and Classification of Brain Tumor in MRI Images ",
International Journal of Emerging Trends in Electrical and Electronics, IJETEE – ISSN: 2320-9569, Vol.
5, Issue. 1, July-2013.
[10] M. Shasidhar , V.Sudheer Raja, B. Vijay Kumar, "MRI Brain Image Segmentation Using Modified
Fuzzy C-Means Clustering Algorithm" ,IEEE International Conference on Communication Systems and
Network Technologies, 2011.
[11] T. Rajesh, R. Suja Mani Malar,” Rough Set Theory and Feed Forward Neural Network Based Brain
Tumor Detection in Magnetic Resonance Images" ,IEEE International on Advanced Nanomaterials &
Emerging Engineering Technologies, 20 13.
[12] Komal Sharma, Navneet Kaur, " Comparative Analysis of Various Edge Detection Techniques" ,
International Journal of Advanced Research in Computer Science and Software Engineering, IJARCSSE,
ISSN: 2277 128X, Volume 3, Issue 12, December 2013.
[13] J. Selvakumar, A. Lakshmi, T. Arivoli, " Brain Tumor Segmentation and Its Area Calculation in Brain
MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm" , IEEE-International Conference
On Advances In Engineering, Science And Management, ICAESM, 2012.
[14] R. J. Ramteke1, Khachane Monali Y., " Automatic Medical Image Classification and Abnormality
Detection Using K-Nearest Neighbour" , International Journal of Advanced Computer Research,Volume-
2 Number-4 Issue-6 December-2012.
[15] Xiao Xuan, Qingmin Liao, Statistical Structure Analysis in MRI Brain Tumor Segmentation" ,IEEE
International Conference on Image and Graphics, 2007.
[16] Mohd Fauzi Othman, Mohd Ariffanan, Mohd Basri, " Probabilistic Neural Network for Brain Tumor
Classification" ,IEEE International Conference on Intelligent Systems, Modelling and Simulation,2011.
[17] Shweta Jain, "Brain Cancer Classification Using GLCM Based Feature Extraction in Artificial Neural
Network" , International Journal of Computer Science & Engineering Technology ,IJCSET, ISSN : 2229-
3345 Vol. 4 No. 07 Jul 2013.
[18] Dina Aboul Dahab, Samy S. A. Ghoniemy, Gamal M. Selim, "Automated Brain Tumor Detection and
Identification Using Image Processing and Probabilistic Neural Network Techniques" ,International
Journal of Image Processing and Visual Communication, ISSN 2319-1724 : Volume (Online) 1 , Issue 2 ,
October 2012.
[19] Walaa Hussein Ibrahim, Ahmed Abdel Rhman Ahmed Osman, Yusra Ibrahim Mohamed, "MRI Brain
Image Classification Using Neural Networks" ,IEEE International Conference On Computing,
Electrical and Electronics Engineering, ICCEEE,2013.
[20] Noramalina Abdullah, Lee Wee Chuen, Umi Kalthum Ngah Khairul Azman Ahmad, "Improvement of
MRI Brain Classification using Principal Component Analysis" , IEEE International Conference on
Control System, Computing and Engineering, 2011.
[21] Mehdi Jafari, Reza Shafaghi, "A Hybrid Approach for Automatic Tumor Detection of Brain MRI using
Support Vector Machine and Genetic Algorithm", Global Journal of Science Engineering and
Technology, Issue-3, 2012.
[22] V. Salai Selvam and S. Shenbagadevi, "Brain Tumor Detection using Scalp EEG with Modified Wavelet-
ICA and Multi Layer Feed Forward Neural Network" , Annual International Conference of the IEEE
EMBS Boston, Massachusetts USA, August 30 - September 3, 2011.
[23] Amir Shahzad, Muhammad Sharif, Mudassar Raza, Khalid Hussai, " Enhanced Watershed Image
Processing Segmentation " , Journal of Information & Communication Technology, Vol. 2, No. 1, 2009.