"The image shows no signs of diabetic macular edema."
If exudates are found between 1DD to 2DD from the fovea, it is classified as less
significant stage. The
Small overview of the startups involved in healthcare artificial intelligence, the OCT market, investments, patent and IP issues and FDA regulation.
Alternative download link: https://dl.dropboxusercontent.com/u/6757026/slideShare/retinalAI_landscape.pdf
How AI Enhances & Accelerates Diabetic Retinopathy DetectionCognizant
To enable earlier and quicker diagnosis of diabetic retinopathy (DR), Cognizant has built a system based on AI and deep learning - a convolutional neural network - that analyzes many thousands of fundus images and delivers accurate assessments of eye-disease damage.
Small overview of the startups involved in healthcare artificial intelligence, the OCT market, investments, patent and IP issues and FDA regulation.
Alternative download link: https://dl.dropboxusercontent.com/u/6757026/slideShare/retinalAI_landscape.pdf
How AI Enhances & Accelerates Diabetic Retinopathy DetectionCognizant
To enable earlier and quicker diagnosis of diabetic retinopathy (DR), Cognizant has built a system based on AI and deep learning - a convolutional neural network - that analyzes many thousands of fundus images and delivers accurate assessments of eye-disease damage.
From unimodal image classification to integrative multimodal deep learning pipelines in disease classification, disease management and predictive personalised healthcare.
Possible future avenues for ophthalmic imaging combining advanced techniques and deep learning. "Bubbling under the surface, and inspiration from ‘bioimaging’ in general"
Tele-ophthalmology: the new normal in current timesObaidur Rehman
Covers telehealth and telemedicine in general. Tele-ophthalmology development in India. Practice and patterns as defined by concerned authorities. Guidelines as set up Govt of India. Current tele-ophthalmology projects in India
Slide for study session given by Ryosuke Sasaki at Arithmer inc.
It is a summary of recent methods for object pose estimation in robotics using deep learning.
He entered Ph.D course at Univ. of Tokyo in April 2020.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
A case of intermittent exotropia by Krishna BanjadeKrishna Banjade
Intermittent exotropia is the most common type of strabismus in India, also known as "Intermittent divergent squint."
This PPT gives us the clear idea about different types of intermittent exotropia and the importance of patch test to differentiate basic X(T) from pseudodivergence X(T)
Novel Development in treatment of Diabetic Macular Edema, by Dr. Fritz Allen, presented at VO, Lecture Series 11, Feb 20, 2011
COPE Course ID: 30657-PS
From unimodal image classification to integrative multimodal deep learning pipelines in disease classification, disease management and predictive personalised healthcare.
Possible future avenues for ophthalmic imaging combining advanced techniques and deep learning. "Bubbling under the surface, and inspiration from ‘bioimaging’ in general"
Tele-ophthalmology: the new normal in current timesObaidur Rehman
Covers telehealth and telemedicine in general. Tele-ophthalmology development in India. Practice and patterns as defined by concerned authorities. Guidelines as set up Govt of India. Current tele-ophthalmology projects in India
Slide for study session given by Ryosuke Sasaki at Arithmer inc.
It is a summary of recent methods for object pose estimation in robotics using deep learning.
He entered Ph.D course at Univ. of Tokyo in April 2020.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
A case of intermittent exotropia by Krishna BanjadeKrishna Banjade
Intermittent exotropia is the most common type of strabismus in India, also known as "Intermittent divergent squint."
This PPT gives us the clear idea about different types of intermittent exotropia and the importance of patch test to differentiate basic X(T) from pseudodivergence X(T)
Novel Development in treatment of Diabetic Macular Edema, by Dr. Fritz Allen, presented at VO, Lecture Series 11, Feb 20, 2011
COPE Course ID: 30657-PS
AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THR...IJCSES Journal
One common cause of visual impairment among people of working age in the industrialized countries is
Diabetic Retinopathy (DR). Automatic recognition of hard exudates (EXs) which is one of DR lesions in
fundus images can contribute to the diagnosis and screening of DR.The aim of this paper was to
automatically detect those lesions from fundus images. At first,green channel of each original fundus image
was segmented by improved Otsu thresholding based on minimum inner-cluster variance, and candidate
regions of EXs were obtained. Then, we extracted features of candidate regions and selected a subset which
best discriminates EXs from the retinal background by means of logistic regression (LR). The selected
features were subsequently used as inputs to a SVM to get a final segmentation result of EXs in the image.
Our database was composed of 120 images with variable color, brightness, and quality. 70 of them were
used to train the SVM and the remaining 50 to assess the performance of the method. Using a lesion based
criterion, we achieved a mean sensitivity of 95.05% and a mean positive predictive value of 95.37%. With
an image-based criterion, our approach reached a 100% mean sensitivity, 90.9% mean specificity and
96.0% mean accuracy. Furthermore, the average time cost in processing an image is 8.31 seconds. These
results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening
for DR.
Detection and Grading of Diabetic Maculopathy Automatically in Digital Retina...paperpublications3
Abstract: Diabetic Retinopathy (DR) is a critical eye disease which can be regarded as manifestation of diabetes on the retina the symptoms can blur or distort the patient’s vision and are a main cause of blindness. Exudates are one of the signs of Diabetic Retinopathy. If the disease is detected early and treated promptly many of the visual loss can be prevented. This paper explains the development of an automatic fundus image processing and analytic system to facilitate diagnosis of the ophthalmologists. The algorithms to detect the optic disc; blood vessels and exudates are investigated. The proposed system extracts macula from digital retinal image using the optic disc location. Many common features such as intensity, geometric and correlations are used to distinguish between them. The system uses GLCM for feature extraction. The system uses a SVM based classifier to differentiate the retinal images in different stages of maculopathy by using the macula co-ordinates and exudates feature set.
Haemorrhage Detection and Classification: A ReviewIJERA Editor
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
Classification of OCT Images for Detecting Diabetic Retinopathy Disease using...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
COMPUTING THE GROWTH RATE OF STEM CELLS USING DIGITAL IMAGE PROCESSING Pratyusha Mahavadi
The aim is to compute the growth rate of stem cells by using segmentation, feature extraction and pattern recognition which are the fundamental methods of digital image processing. DRLSE algorithm is applied for segmenting images. The DRLSE algorithm is an amalgamation of Canny Edge Detector algorithm and DRLSE method, which uses the four well potential function. Features are extracted from segmented images using GLCM method and finally Support Vector Machine (SVM) is used for pattern recognition and classification of stem cells.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
An Efficient Integrated Approach for the Detection of Exudates and Diabetic M...acijjournal
Diabetic Retinopathy (DR) is a major cause of blindness. Exudates are one of the primary signs of diabetic retinopathy which is a main cause of blindness that could be prevented with an early screening process In this approach, the process and knowledge of digital image processing to diagnose exudates
from images of retina is applied. An automated method to detect and localize the presence of exudates and Maculopathy from low-contrast digital images of Retinopathy patient’s with non-dilated pupils is proposed. First, the image is segmented using colour K-means Clustering algorithm. The segmented image along with Optic Disc (OD) is chosen. To Classify these segmented region, features based on colour and texture are extracted. The selected feature vector are then classified into exudates and nonexudates using a Support Vector Machine (SVM) Classifier. Also the detection of Diabetic Maculopathy,
which is the severe stage of Diabetic Retinopathy is performed using Morphological Operation. Using a clinical reference standard, images with exudates were detected with 96% success rate. This method appears promising as it can detect the very small areas of exudates.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found
to be 96%.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
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/
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
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Thesis presentation
1.
2. CLASSIFICATION OF DIABETIC MACULAR
EDEMA USING COLORED FUNDUS IMAGE
PRESENTED BY:
MUHAMMAD ZUBAIR
SUPERVISED BY:
Dr. SHOAB AHMED KHAN
3. GUIDANCE AND EVALUATION COMMITTEE
GEC Members
Dr. Ubaid Ullah Yasin (Co-Supervisor)
(Asst. Prof. AFPGMI, Eye specialist AFIO)
Dr. Aasia Khanum
Dr. Ali Hassan
4. AGENDA
Motivation and problem statement
Introduction
Literature review
Proposed System
Evaluation and Results
Conclusion and Future work
References
7. MOTIVATION
Diabetes Mellitus is a fast growing global disease
In 2007 survey by International Diabetic Federation
(IDF) Pakistan was at 7th in top 10 countries having
diabetic population [1]
This number may rise from 6.9 million in 2007 to
11.5 million in 2025 by IDF [1], almost double
8. STATISTICAL CHART BY IDF
2007 2025
Country Persons
(millions)
Country Persons
(millions)
1 India 40.9 1 India 69.9
2 China 39.8 2 China 59.3
3 USA 19.2 3 USA 25.4
4 Russia 9.6 4 Brazil 17.6
5 Germany 7.4 5 Pakistan 11.5
6 Japan 7.0 6 Mexico 10.8
7 Pakistan 6.9 7 Russia 10.3
8 Brazil 6.9 8 Germany 8.1
9 Mexico 6.1 9 Egypt 7.6
10 Egypt 4.4 10 Bangladesh 7.4
9. MOTIVATION …
DME can lead to complete irreversible blindness
Early stage detection is rare
Doctor to patient ratio is very low in Pakistan
Screening is hectic in populated areas
Sparse resources in rural areas
10. PROBLEM STATEMENT
To develop an automated CAD system to help
ophthalmologists in mass screening by
Identification of abnormalities (exudates),
Finding out their exact location and area within
the macular region,
Stage classification of the disease
11. PUBLICATIONS
CONFERENCES
“Classification of Diabetic Macular Edema and Its Stages Using
Color Fundus Image” (Registered and to be presented in 2013 3rd
International conference on Signal, Image Processing and
Applications (ICSIA))
“Automated Detection of Optic Disc for the Analysis of Retina
Using Color Fundus Image” (Accepted in 2013 IEEE International
conference on Imaging Systems And Techniques (IST))
“Automated Segmentation of Exudates Using Dynamic
Thresholding in Retinal Photographs” (Accepted in 16th IEEE
International Multi Topic Conference (INMIC) 2013)
JOURNALS
“Automated Grading of Diabetic Macular Edema Using Colored
Fundus Photographs” (Submitted in Journal of Digital Imaging)
13. HUMAN EYE
Retinal layer of human eye comprises:
o Optic Disc (Head of optic nerve)
o Macula (Central portion of retina)
o Fovea (Center of macula)
o Network of Blood vessels
15. DIABETIC MACULAR EDEMA
DME is the swelling of the macula
Leaked fluid accumulates on the retina
Leakage within the macula causes swelling
16. SCREENING TESTS FOR DME
Fundus Fluorescein Angiography (FFA)
Retinal image acquisition process
2D colored and red free images
Optical Coherence Tomography (OCT)
3D high resolution image acquisition process
Provides a cross-sectional image
Used as optical biopsy by the ophthalmologists
22. Aquino et al.*
Detect OD using edge detection
Circular Hough transform
Feature extraction
Morphological operations
Accuracy achieved was 86%
A. Aquino, M. E. G. Arias and D. Marin, “Detecting the Optic Disc Boundary in Digital Fundus Images Using
Morphological, Edge Detection and Feature Extraction Techniques,” IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol.
29, No. 11, pp.1860-1869, 2010
23. Siddalingaswamy et al.*
Exudates detection using clustering
Morphological techniques
Exudates location based severity level
Sensitivity achieved 95% and specificity 96%
No publically available database used
Use only 148 local fundus images
P.C. Siddaligaswamy, K. G. Prabhu, “Automatic Grading of Diabetic Maculopathy Severity Levels”, Proceedings of
2010 International Conference on Systems in Medicine and Biology, pp. 331-334, 2010
24. Lim et al.*
Segmentation of exudates using watershed transform
External and internal markers used
Classification done as normal, stage1 and stage2
Use only 88 images of MESSIDOR
Sensitivity and Specificity achieved 80.9%, 90.2%
respectively
Accuracy achieved was 85.2 percent
S.T. Lim, W.M.D.W. Zaki, A. Hussain, S.L. Lim, S. Kusalavan, “Automatic Classification of Diabetic Macular Edema in Digital Fundus
Images", 2011 IEEE Colloquium on Humanities, Science and Engineering (CHUSER), pp. 265-269, 2011.
25. Giancardo et al.*
Unsupervised technique for exudates segmentation
Background estimation used
Image normalization used
8 neighbor connectivity used
Used local database images
No Stage classification was done
L. Giancardo, F. Meriaudeau, T.P. Karnowski, K. W. Tobin Jr, E. Chaum, MD, “AUTOMATIC RETINA EXUDATES SEGMENTATION
WITHOUT A MANUALLY LABELLED TRAINING SET”, IEEE Transactions on Medical Imaging, Vol. 21, No. 5, pp. 1396-1400, 2011
27. PROPOSED SYSTEM
Preprocessing of image
Optic disc elimination
Dynamic Thresholding
All possible exudates detection
Classification of the stage
28. FLOW CHART OF PROPOSED TECHNIQUE
Input Colored Fundus Image
Resizing of Image
Green Component
Preprocessing
OD Removal
Fovea
localization
Grayscale
Classifier
29.
30. Input Colored Image
Green Channel of Image
Contrast Limited Adaptive Histogram Equalization
Contrast Stretching Transform
PREPROCESSING OF IMAGE
31. PREPROCESSING OF IMAGE
CLAHE
Contrast Limited Adaptive Histogram Equalization
(CLAHE)
To make the components visually distinct
Distinction of foreground objects from background
CLAHE used iteratively to get the desired result
33. PREPROCESSING OF IMAGE
CONTRAST STRETCHING TRANSFORM
Expands the range of intensity of the image
Range span is defined by the user
Improves the overall contrast of the image
Increases the contrast b/w dark and lights
Stretches the intensity domain histogram
36. OPTIC DISC (OD) ELIMINATION
OD becomes prominent after preprocessing
Detection on basis of highest intensity value pixels
Extended minima transform applied
Detect all candidate OD regions
Morphological operations to extract real OD
38. OPTIC DISC (OD) ELIMINATION
EXTENDED MINIMA TRANSFORM
Computes the regional minima
Regional minima are connected components of
pixels with constant intensity value
The external boundary pixels have high value
8-neighbor connectivity used
Selects all possible candidate regions for OD
41. MORPHOLOGICAL OPERATIONS
Morphological Erotion
To remove all non OD candidate regions
Structuring element (SE) having size less than OD size
SE chosen is of the size one fourth of the radius of OD
Erotion also shrinks the actual OD region
42. MORPHOLOGICAL OPERATIONS
Morphological Dilation
To get the actual size of OD after erotion
SE is of the size approx. equal to the radius of OD
Dilation restores the size and shape of actual OD
False OD are no more in the image
45. DYNAMIC THRESHOLDING
Fundus images are taken in different illumination
All images have different intensity levels
Intensity based parameters chosen for dynamic
thresholding
Mean and standard deviation of the image is used
Threshold value is set using these parameters
46. CALCULATION OF THRESHOLD VALUE
USING MEAN AND STANDARD DEVIATION
Mean Standard deviation Threshold value
30.05 33.35 2.8
31.25 34.6 2.8
31.25 35.4 2.8
31.9 35.7 2.9
32.45 36.2 2.9
32.55 35.92 3.0
32.84 35.96 3.0
35.08 38.6 3.2
35.09 40.2 3.3
35.49 39.38 3.3
35.85 39.40 3.3
35.95 39.46 3.3
37.15 41.87 3.4
37.35 41.56 3.4
38.57 42.27 3.4
41.00 46.65 3.6
41.22 47.1 3.65
41.53 47.06 3.65
43.10 48.48 3.7
43.07 48.05 3.75
43.27 47.71 3.75
44.58 49.58 3.9
45.56 51.30 3.95
48.20 53.50 4.3
49.4 54.51 4.4
48. DETECTION OF EXUDATES
Exudates are bright lesions
Bright yellowish spots in colored fundus image
OD free image is used for exudates detection
Segmentation is done for exudates detection
50. ETDRS STAGING CRITERIA
Early Treatment Diabetic Retinopathy Study
(ETDRS)
Classification of DME is based on the standard
criteria set by ETDRS
Severity level depends upon size and location of
abnormality
Reference point is the center of macula
53. CLASSIFICATION OF STAGES
Normal Condition
No abnormality is found
Less Significant Stage
Abnormality found outside the circular area of 1 Disc
Diameter 1DD radius but within 2DD from the fovea
Moderate Stage
Abnormality found beyond 1/3DD (radius) but within 1DD
of circle
Severe Stage
Abnormality found within 1/3DD circular area from the
center of fovea region
54. STAGE CLASSIFICATION TABLE
Stage Description
Normal No abnormality is found
Severe
Abnormality found within 1/3DD radius circular
area from the center of fovea region
Moderate
Abnormality found beyond 1/3DD radius but
within 1DD radius of circle
Less
significant
Abnormality found outside the circular area of
1DD radius but within 2DD radius from the
fovea region
1Disc Diameter (DD) = 1500µm (1.5mm)
57. RESULTANT MESSAGES DISPLAYED AFTER THE
CLASSIFICATION OF STAGES
If no exudates are found in the image or the abnormality is found outside the
2DD circle, it is declared as normal case. The message will be displayed “No
exudates found: Normal eye”.
If the exudates are found within the outermost 2DD circle the stage is called
insignificant. The message will be displayed “warning: exudates found within
the outermost circle, insignificant stage”.
If the exudates are found in both outermost 2DD and the middle circle 1DD
circle, it is declared as moderate stage. The message will be displayed
“warning: exudates found within the outermost circle and middle circle,
moderate stage”.
If the exudates are found in the middle circle only 1DD circular area it is known
as moderate stage. The message will be displayed “warning: exudates found
within the middle circle, moderate stage”.
If the abnormality (exudates) found within both the middle circle 1DD circular
area and the innermost 1/3DD circle the stage is known as severe stage. The
message will be displayed “warning: exudates found within the middle circle
and the innermost circle, severe stage”.
If the exudates are found within the innermost 1/3DD circle the stage is called
severe stage. The message displayed in this case “warning: exudates found in
the innermost circle, severe stage”.
66. CONCLUSION
Improved performance of the system in terms of
True localization of optic disc and its removal
Detection of exudates and determining their position
Classifying stage of the disease
The System provide accurate results using less but
effective features of the input image
Efficient system for identifying and classifying the DME
The system can be used practically in diagnostic
environment with a sound reliability
67. FUTURE WORK
Classification of DME using OCT (3D) images
Fusion of FFA and OCT results for easy and better
diagnosis
Detection of soft exudates/drusens in fundus
images
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