1) The document discusses the development of a software to annotate retina fundus images for teaching and learning about diabetic retinopathy.
2) The authors collected 625 fundus images from Bangladesh and designed annotation software to store patient information, infection levels, and locations in the images. They introduced a K-nearest neighbor technique to accurately select regions of interest since it can be difficult for experts.
3) Once experts annotate the images using this software, it can be used by students for learning about diabetic retinopathy stages and symptoms. The dataset and software aim to address the lack of proper diabetic retinopathy datasets and aid medical education.
Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fu...IJAAS Team
In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.
Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fu...IJAAS Team
In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.
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.
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than
190 million people with diabetes worldwide. The World Health Organization (WHO) estimates that this will rise
to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle.
Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence
of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as
our population age.
Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating
complications like blindness. At present, various analyses on complicated interaction between hereditary and
environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic
complication has become a major concern regarding the prognosis of diabetic patients.
Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing
this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure
more Singaporeans from falling prey to this disease
An Amalgamation-Based System for Micro aneurysm Detection and Diabetic Retino...IJMER
We propose an ensemble-based framework to improve microaneurysm detection. Unlike
the well-known approach of considering the output of multiple classifiers, we propose a combination of
internal components of microaneurysm detectors, namely preprocessing methods and candidate
extractors. We have evaluated our approach for microaneurysm detection in an online competition,
where this algorithm is currently ranked as first, and also on two other databases.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
Automated Detection of Microaneurysm, Hard Exudates, and Cotton Wool Spots in...iosrjce
The The automatic identification of Image processing techniques for abnormalities in retinal images.
Its very importance in diabetic retinopathy screening. Manual annotations of retinal images are rare and
exclusive to obtain. The ophthalmoscope used direct analysis is a small and portable apparatus contained of a
light source and a set of lenses view the retina. The existence of diabetic retinopathy detected can be examining
the retina for its individual features. The first presence of diabetic retinopathy is the form of Microaneurysms.
This paper describes different works needed to the automatic identification of hard exudates and cotton wool
spots in retinal images for diabetic retinopathy detection and support vector machine (SVM) for classifying
images. This system is evaluated on a large dataset containing 130 retinal images. The proposed method Results
show that exudates were detected from a database with 96.9% sensitivity, specificity 96.1% and
97.38%accuracy
Diabetic Retinopathy Detection using Neural Networkingijtsrd
The clinical and laboratory studies states that diabetic retinopathy is the major cause of permanent blindness among the aged personalities. The problem with this disease is that there is no cure for it and the only thing we can do is to detect the disease as soon as possible in order to prevent further loss of vision. In this system we propose a CNN approach for diagnosing DR from retinal images and classifying the stages of the disease .The classification is done based on the haemorrhages, micro aneurysms present in the retinal image. We train this network using a high end graphics processor unit GPU using kaggle data set and the disease classification is done, hence we can identify the the disease and prevent the further loss of vision. N Rahul | Roy Eluvathingal | Sanith Jayan K | Mr. Anil Antony "Diabetic Retinopathy Detection using Neural Networking" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31487.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31487/diabetic-retinopathy-detection-using-neural-networking/n-rahul
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.
An Approach for the Detection of Vascular Abnormalities in Diabetic Retinopathyijdmtaiir
Diabetic Retinopathy is a common complication of
diabetes that is caused by changes in the blood vessels of the
retina. The blood vessels in the retina get altered. Exudates are
secreted, micro-aneurysms and hemorrhages occur in the
retina. The appearance of these features represents the degree
of severity of the disease. In this paper the proposed approach
detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing
techniques to the fundus images to extract features such as
blood vessels, micro aneurysms and exudates. These features
are used for the detection of severity of Diabetic Retinopathy.
It can quickly process a large number of fundus images
obtained from mass screening to help reduce the cost, increase
productivity and efficiency for ophthalmologists.
EXUDATES DETECTION FROM DIGITAL FUNDUS IMAGE OF DIABETIC RETINOPATHYijabjournal
The aim of this paper is to detect exudates from the digital fundus images and provide information about Non Proliferative Diabetic Retinopathy. Diabetic retinopathy is very complicated disease that occurs when the retinal blood vessels changes. Exudates are the first sign of the diabetic retinopathy which cause blindness. So it is very important to find out these exudates in fundus image. In this paper we have proposed a method which is used for segmentation of optic disc and exudates. Morphological operations are used for detection of exudates. Before this operation we are applying Contrast Limited Adaptive Histogram Equalization technique. The results are compared with the standard database.
Review of methods for diabetic retinopathy detection and severity classificationeSAT Journals
Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...iosrjce
To diagnosis of Diabetic Retinopathy (DR) it is the prime cause of blindness in the working age
population of the world. Detection method is proposed to detect dark or red lesions such as microaneurysms
and hemorrhages in fundus images.Developed during this work, this first is for collection of lesion data
information and was used by the ophthalmologist in marking images for database while the automatic
diagnosing and displaying the diagnosis result in a more friendly user interface and is as shown in chapter
three of this report. The primary aim of this project is to develop a system that will be able to identify patients
with BDR and PDR from either colour image or grey level image obtained from the retina of the patient. The
algorithm was tested fundus images. The Operating Characteristics (ROC) was determined for red spot lesion
and bleeding, while cross over points were only detected leaving further classification as part of future work
needed to complete this global project. Sensitivity and specificity was calculated for the algorithm is given
respectively as 96.3% and 95.1%
Glaucoma is the most leading cause of irreversible blindness with the population of Africa and Asia ranking the highest over the rate of glaucoma affected regions around the world. The defect will damage eyes irreversibly by affecting the optic cup and optic disc of an eye. The early detection of glaucoma is an unavoidable need in the medical field. The widely used technique to detect glaucoma is an invasive method that may lead to other effects on the eye. This reason led to the introduction of a non invasive method that follows image processing for the detection of glaucoma. Retinal image based detection is the best way to choose as it comes under non invasive methods of detection. Detection of glaucoma using retinal images requires various medical features of the eyes such as optic cup diameter, optic disc diameter and optic cup to disc ratio are used. Glaucoma disease detection from retinal images supports convolutional neural networks CNN . The textual features obtained from retinal images such as the optic cup to optic disc measures are used for this classification. Convolutional Neural Networks use little pre processing techniques that can be implemented relatively uncomplicated compared to other image classification techniques. The implementation of this project follows the traditional CNN architecture, applying filter layers such as Convolution layer and Pooling layer and also activation functions such as ReLu function and sigmoid function to pre process as well as to update weights respectively on the hidden layers of the CNN followed by classifying the image. Vishnubhotla Poornasree | Vijayagiri Ashritha | Venumula Deeksha Reddy | J. Srilatha ""Glaucoma Detection from Retinal Images"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23732.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23732/glaucoma-detection-from-retinal-images/vishnubhotla-poornasree
Segmentation of the Blood Vessel and Optic Disc in Retinal Images Using EM Al...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Tool-Matlab
Drive database is considered for extraction of features and testing images to detect the ground truth and even images from internet.
The image features like Blood vessel area,optic disk area,entropy,energy are calculated.
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than
190 million people with diabetes worldwide. The World Health Organization (WHO) estimates that this will rise
to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle.
Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence
of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as
our population age.
Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating
complications like blindness. At present, various analyses on complicated interaction between hereditary and
environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic
complication has become a major concern regarding the prognosis of diabetic patients.
Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing
this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure
more Singaporeans from falling prey to this disease
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.
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than
190 million people with diabetes worldwide. The World Health Organization (WHO) estimates that this will rise
to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle.
Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence
of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as
our population age.
Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating
complications like blindness. At present, various analyses on complicated interaction between hereditary and
environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic
complication has become a major concern regarding the prognosis of diabetic patients.
Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing
this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure
more Singaporeans from falling prey to this disease
An Amalgamation-Based System for Micro aneurysm Detection and Diabetic Retino...IJMER
We propose an ensemble-based framework to improve microaneurysm detection. Unlike
the well-known approach of considering the output of multiple classifiers, we propose a combination of
internal components of microaneurysm detectors, namely preprocessing methods and candidate
extractors. We have evaluated our approach for microaneurysm detection in an online competition,
where this algorithm is currently ranked as first, and also on two other databases.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
Automated Detection of Microaneurysm, Hard Exudates, and Cotton Wool Spots in...iosrjce
The The automatic identification of Image processing techniques for abnormalities in retinal images.
Its very importance in diabetic retinopathy screening. Manual annotations of retinal images are rare and
exclusive to obtain. The ophthalmoscope used direct analysis is a small and portable apparatus contained of a
light source and a set of lenses view the retina. The existence of diabetic retinopathy detected can be examining
the retina for its individual features. The first presence of diabetic retinopathy is the form of Microaneurysms.
This paper describes different works needed to the automatic identification of hard exudates and cotton wool
spots in retinal images for diabetic retinopathy detection and support vector machine (SVM) for classifying
images. This system is evaluated on a large dataset containing 130 retinal images. The proposed method Results
show that exudates were detected from a database with 96.9% sensitivity, specificity 96.1% and
97.38%accuracy
Diabetic Retinopathy Detection using Neural Networkingijtsrd
The clinical and laboratory studies states that diabetic retinopathy is the major cause of permanent blindness among the aged personalities. The problem with this disease is that there is no cure for it and the only thing we can do is to detect the disease as soon as possible in order to prevent further loss of vision. In this system we propose a CNN approach for diagnosing DR from retinal images and classifying the stages of the disease .The classification is done based on the haemorrhages, micro aneurysms present in the retinal image. We train this network using a high end graphics processor unit GPU using kaggle data set and the disease classification is done, hence we can identify the the disease and prevent the further loss of vision. N Rahul | Roy Eluvathingal | Sanith Jayan K | Mr. Anil Antony "Diabetic Retinopathy Detection using Neural Networking" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31487.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31487/diabetic-retinopathy-detection-using-neural-networking/n-rahul
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.
An Approach for the Detection of Vascular Abnormalities in Diabetic Retinopathyijdmtaiir
Diabetic Retinopathy is a common complication of
diabetes that is caused by changes in the blood vessels of the
retina. The blood vessels in the retina get altered. Exudates are
secreted, micro-aneurysms and hemorrhages occur in the
retina. The appearance of these features represents the degree
of severity of the disease. In this paper the proposed approach
detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing
techniques to the fundus images to extract features such as
blood vessels, micro aneurysms and exudates. These features
are used for the detection of severity of Diabetic Retinopathy.
It can quickly process a large number of fundus images
obtained from mass screening to help reduce the cost, increase
productivity and efficiency for ophthalmologists.
EXUDATES DETECTION FROM DIGITAL FUNDUS IMAGE OF DIABETIC RETINOPATHYijabjournal
The aim of this paper is to detect exudates from the digital fundus images and provide information about Non Proliferative Diabetic Retinopathy. Diabetic retinopathy is very complicated disease that occurs when the retinal blood vessels changes. Exudates are the first sign of the diabetic retinopathy which cause blindness. So it is very important to find out these exudates in fundus image. In this paper we have proposed a method which is used for segmentation of optic disc and exudates. Morphological operations are used for detection of exudates. Before this operation we are applying Contrast Limited Adaptive Histogram Equalization technique. The results are compared with the standard database.
Review of methods for diabetic retinopathy detection and severity classificationeSAT Journals
Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...iosrjce
To diagnosis of Diabetic Retinopathy (DR) it is the prime cause of blindness in the working age
population of the world. Detection method is proposed to detect dark or red lesions such as microaneurysms
and hemorrhages in fundus images.Developed during this work, this first is for collection of lesion data
information and was used by the ophthalmologist in marking images for database while the automatic
diagnosing and displaying the diagnosis result in a more friendly user interface and is as shown in chapter
three of this report. The primary aim of this project is to develop a system that will be able to identify patients
with BDR and PDR from either colour image or grey level image obtained from the retina of the patient. The
algorithm was tested fundus images. The Operating Characteristics (ROC) was determined for red spot lesion
and bleeding, while cross over points were only detected leaving further classification as part of future work
needed to complete this global project. Sensitivity and specificity was calculated for the algorithm is given
respectively as 96.3% and 95.1%
Glaucoma is the most leading cause of irreversible blindness with the population of Africa and Asia ranking the highest over the rate of glaucoma affected regions around the world. The defect will damage eyes irreversibly by affecting the optic cup and optic disc of an eye. The early detection of glaucoma is an unavoidable need in the medical field. The widely used technique to detect glaucoma is an invasive method that may lead to other effects on the eye. This reason led to the introduction of a non invasive method that follows image processing for the detection of glaucoma. Retinal image based detection is the best way to choose as it comes under non invasive methods of detection. Detection of glaucoma using retinal images requires various medical features of the eyes such as optic cup diameter, optic disc diameter and optic cup to disc ratio are used. Glaucoma disease detection from retinal images supports convolutional neural networks CNN . The textual features obtained from retinal images such as the optic cup to optic disc measures are used for this classification. Convolutional Neural Networks use little pre processing techniques that can be implemented relatively uncomplicated compared to other image classification techniques. The implementation of this project follows the traditional CNN architecture, applying filter layers such as Convolution layer and Pooling layer and also activation functions such as ReLu function and sigmoid function to pre process as well as to update weights respectively on the hidden layers of the CNN followed by classifying the image. Vishnubhotla Poornasree | Vijayagiri Ashritha | Venumula Deeksha Reddy | J. Srilatha ""Glaucoma Detection from Retinal Images"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23732.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23732/glaucoma-detection-from-retinal-images/vishnubhotla-poornasree
Segmentation of the Blood Vessel and Optic Disc in Retinal Images Using EM Al...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Tool-Matlab
Drive database is considered for extraction of features and testing images to detect the ground truth and even images from internet.
The image features like Blood vessel area,optic disk area,entropy,energy are calculated.
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than
190 million people with diabetes worldwide. The World Health Organization (WHO) estimates that this will rise
to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle.
Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence
of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as
our population age.
Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating
complications like blindness. At present, various analyses on complicated interaction between hereditary and
environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic
complication has become a major concern regarding the prognosis of diabetic patients.
Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing
this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure
more Singaporeans from falling prey to this disease
Detection of Diabetic Retinopathy in Retinal Image Early Identification using...ijtsrd
Diabetic Retinopathy, the most common reason of vision loss, is caused by damage to the small blood vessels in the retina. If untreated, it may result in varying degrees of vision loss and even blindness. Since Diabetic Retinopathy is a silent disease that may cause no symptoms or only mild vision problems, annual eye exams are crucial for early detection to improve the chances of effective treatment where fundus cameras are used to capture the retinal images. However, fundus cameras are too big and heavy to be transported easily and too costly to be purchased by every health clinic, so fundus cameras are an inconvenient tool for widespread screening. Recent technological developments have enabled using smartphones in designing small sized, low power, and affordable retinal imaging systems to perform Diabetic Retinopathy screening and automated Diabetic Retinopathy detection using machine learning and image processing methods. However, Diabetic Retinopathy detection accuracy depends on the image quality and it is negatively affected by several factors such as Field of View. Since smartphone based retinal imaging systems have much more compact designs than the traditional fundus cameras, the retina images captured are likely to be low quality with smaller Field of View As a result, the smartphone based retina imaging systems can be used as an alternative to the direct ophthalmoscope once it tested in the clinical settings. However, the Field of View of the smartphone based retina imaging systems plays an important role in determining the automatic Diabetic Retinopathy detection accuracy. M. Mukesh Krishnan | J. Diofrin | M. Vadivel "Detection of Diabetic Retinopathy in Retinal Image Early Identification using Deep CNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55047.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/other/55047/detection-of-diabetic-retinopathy-in-retinal-image-early-identification-using-deep-cnn/m-mukesh-krishnan
Detection of Diabetic Retinopathy using Kirsch Edge Detection and Watershed T...IJARIIT
Diabetic retinopathy (DR) is a common retinal complication associated with diabetics. A complication of diabetes is that it can also affect various parts of the body. When the small blood vessels have a high level of glucose in the retina, the vision will be blurred and can cause blindness eventually, which is known as diabetic retinopathy. However, if symptoms are identified in the early stage then proper treatment can be provided to prevent blindness. Usually the retinal images obtained from the fundus camera are examined directly and diagnosed. Due to this certain abnormalities due to diabetic retinopathy are not directly visible through the naked eye .Hence by using the image processing techniques these abnormalities can be extracted accurately and required treatments and precautions can be taken. And this also reduces the time for the ophthalmologists to detect the disease and give accurate treatments.
A Novel Advanced Approach Using Morphological Image Processing Technique for ...CSCJournals
Diabetic retinopathy (DR) is a common complication of diabetes mellitus and can lead to irreversible blindness. To date, DR is the leading cause of blindness and visual impairment among working adults globally. However, this blindness can be prevented if DR is detected early. Diabetes mellitus slowly affects the retina by damaging retinal blood vessels and leading to microaneurysms. The retinal images give detailed information about the health status of the visual system. Analysis of retinal image is important for an understanding of the stages of Diabetic retinopathy. Microaneurysms observed that appear in retina images, usually, the initial visible sign of DR, if detected early and properly treated can prevent DR complications, including blindness. In this research work, an advanced image modal enhancement comprises of a Contrast Limited Adaptive Histogram Equalization (CLAHE), through morphological image, processing technique with final extraction algorithm is proposed. CLAHE is responsible for the detection, and removal of the retinal optical disk. While the microaneurysm initial indicators are detected by using morphological image processing techniques. The extensive evaluation of the proposed advanced model conducted for microaneurysm detection depicts all stages of DR with an increase in the number of data set related to noise in the image. The microaneurysms noise is associated with stage of retina diseases as well as its early possible diagnosis. Evaluation is also conducted against the proposed model to measure its performance in terms of accuracy, sensitivity as well as specificity in real-time. The results show the test image attained 99.7% accuracy for a real-time database that is better compared with anty colony-based method. A sensitivity of 81% with a specificity of 90% was achieved for the detection of microaneurysms for the e-optha database. The detection of several microaneurysms correlates with stages of DR that prove an analysis of detecting its different stages. As well as it reaches our goal of early detection of DR with high performance in accuracy.
Diabetic Retinopathy Detection System from Retinal Imagesijtsrd
Diabetes Mellitus is a disorder in metabolism of carbohydrates, and due to lack of the pancreatic hormone insulin sugars in the body are not oxidized to produce energy. Diabetic Retinopathy is a disorder of the retina resulting in impairment or vision loss. Improper blood sugar control is the main cause of diabetic retinopathy. That is the reason why early detection of retinopathy is crucial to prevent vision loss. Appearance of exudates, microaneurysms and hemorrhages are the early indications. In this study, we propose an algorithm for detection and classification of diabetic retinopathy. The proposed algorithm is based on the combination of various image processing techniques, which includes Contrast Limited Adaptive Histogram Equalization, Green channelization, Filtering and Thresholding. The objective measurements such as homogeneity, entropy, contrast, energy, dissimilarity, asm, correlation, mean and standard deviation are computed from processed images. These measurements are finally fed to Support Vector Machine and k Nearest Neighbors classifiers for classification and their results were analysed and compared. Aditi Devanand Lotliker | Amit Patil "Diabetic Retinopathy Detection System from Retinal Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38353.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38353/diabetic-retinopathy-detection-system-from-retinal-images/aditi-devanand-lotliker
Extraction of Features from Fundus Images for Diabetes Detectionpaperpublications3
Abstract: Diabetes is a quickly increasing worldwide problem which is produced by defective organic process of glucose secretion that produces long-term dis-function and harmful for different organs. The major problem of diabetes is diabetic retinopathy (DR), which are vascular diseases affecting the retina due to long time diabetes. It can produce sudden vision loss due to DR. So we need to develop the system to examine the retinal images for obtain important features of diabetic retinopathy by using the image processing techniques. First the entire image is segmented. From that segmented regions, we can check varying changes in blood vessels and different features for e.g. exudates, microaneurysms, and also a set of features such as color, size, edge and texture which can be used as part of an automatic diabetes recognition system.
Rapid detection of diabetic retinopathy in retinal images: a new approach usi...IJECEIAES
The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.
The legal cause of blindness for the workingage
population in western countries is Diabetic Retinopathy - a
complication of diabetes mellitus - is a severe and wide- spread
eye disease. Digital color fundus images are becoming
increasingly important for the diagnosis of Diabetic Retinopathy.
In order to facilitate and improve diagnosis in different ways, this
fact opens the possibility of applying image processing techniques
.Microaneurysms is the earliest sign of DR, therefore an
algorithm able to automatically detect the microaneurysms in
fundus image captured. Since microaneurysms is a necessary
preprocessing step for a correct diagnosis. Some methods that
address this problem can be found in the literature but they have
some drawbacks like accuracy or speed. The aim of this thesis is
to develop and test a new method for detecting the
microaneurysms in retina images. To do so preprocessing, gray
level 2D feature based vessel extraction is done using neural
network by using extra neurons which is evaluated on DRIVE
database which is superior than rulebased methods. To identify
microaneurysms in an image morphological opening and image
enhancement is performed. The complete algorithm is developed
by using a MATLAB implementation and the diagnosis in an
image can be estimated with the better accuracy and in shorter
time than previous techniques
An improved dynamic-layered classification of retinal diseasesIAESIJAI
Retina is main part of the human eye and every disease shows the effect on retina. Eye diseases such as choroidal neovascularization (CNV), DRUSEN, diabetic macular edema (DME) are the main retinal diseases that damage the retina and if these damages are identified in the later stages, it is very difficult to reverse the vision for these retinal diseases. Optical coherence tomography (OCT) is a non-nosy image testing for finding the retinal diseases. OCT mainly collects the cross-section images of retina. Deep learning (DL) is used to analyze the patterns in several complex research applications especially in the disease prediction. In DL, multiple layers give the accurate detection of abnormalities in the retinal images. In this paper, an improved dynamic-layered classification (IDLC) is introduced to classify retinal diseases based on their abnormality. Image filters are used to filter the noise present in the input images. ResNet is the pre-trained model which is used to train the features of retinal diseases. Convolutional neural networks (CNN) are the DL model used to analyze the OCT image. The dataset consists of three types of OCT disease datasets from Kaggle. Evaluation results show the performance of IDLC compared with state-of-art algorithms. A better performance is obtained by using the IDLC and achieved the better accuracy.
A transfer learning with deep neural network approach for diabetic retinopath...IJECEIAES
Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
Similar to Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopathy Software (20)
Securing Cloud Computing Through IT GovernanceITIIIndustries
Lack of alignment between information technology (IT) and the business is a problem facing many organizations. Most organizations, today, fundamentally depend on IT. When IT and the business are aligned in an organization, IT delivers what the business needs and the business is able to deliver what the market needs. IT has become a strategic function for most organizations, and it is imperative that IT and business are aligned. IT governance is one of the most powerful ways to achieve IT to business alignment. Furthermore, as the use of cloud computing for delivering IT functions becomes pervasive, organizations using cloud computing must effectively apply IT governance to it. While cloud computing presents tremendous
opportunities, it comes with risks as well. Information security
is one of the top risks in cloud computing. Thus, IT governance must be applied to cloud computing information security to help manage the risks associated with cloud computing information security. This study advances knowledge by extending IT governance to cloud computing and information security governance.
Information Technology in Industry(ITII) - November Issue 2018ITIIIndustries
IT Industry publishes original research articles, review articles, and extended versions of conference papers. Articles resulting from research of both theoretical and/or practical natures performed by academics and/or industry practitioners are welcome. IT in Industry aims to become a leading IT journal with a high impact factor.
Design of an IT Capstone Subject - Cloud RoboticsITIIIndustries
This paper describes the curriculum of the three year IT undergraduate program at La Trobe University, and the faculty requirements in designing a capstone subject, followed by the ACM’s recommended IT curriculum covering the five pillars of the IT discipline. Cloud robotics, a broad multidisciplinary research area, requiring expertise in all five pillars with mechatronics, is an ideal candidate to offer capstone experiences to IT students. Therefore, in this paper, we propose a long term
master project in developing a cloud robotics testbed, with many capstone sub-projects spanning across the five IT pillars, to meet the objectives of capstone experience. This paper also describes the design and implementation of the testbed, and proposes potential capstone projects for students with different interests.
Dimensionality Reduction and Feature Selection Methods for Script Identificat...ITIIIndustries
The goal of this research is to explore effects of dimensionality reduction and feature selection on the problem of script identification from images of printed documents. The kadjacent segment is ideal for this use due to its ability to capture visual patterns. We have used principle component analysis to reduce the size of our feature matrix to a handier size that can be trained easily, and experimented by including varying combinations of dimensions of the super feature set. A modular
approach in neural network was used to classify 7 languages – Arabic, Chinese, English, Japanese, Tamil, Thai and Korean.
Image Matting via LLE/iLLE Manifold LearningITIIIndustries
Accurately extracting foreground objects is the problem of isolating the foreground in images and video, called image matting which has wide applications in digital photography. This problem is severely ill-posed in the sense that, at each pixel, one must estimate the foreground and background pixels and the so-called alpha value from only pixel information. The most recent work in natural image matting rely on local smoothness assumptions about foreground and background colours on which a cost function has been established. In this paper, we propose an extension to the class of affinity based matting techniques by incorporating local manifold structural
information to produce both a smoother matte based on the socalled improved Locally Linear Embedding. We illustrate our new algorithm using the standard benchmark images and very comparable results have been obtained.
A Framework for Traffic Planning and Forecasting using Micro-Simulation Calib...ITIIIndustries
This paper presents the application of microsimulation for traffic planning and forecasting, and proposes a new framework to model complex traffic conditions by calibrating and adjusting traffic parameters of a microsimulation model. By using an open source micro-simulator package, TRANSIMS, in this study, animated and numerical results were produced and analysed. The framework of traffic model calibration was evaluated for its usefulness and practicality. Finally, we discuss future applications such as providing end users with real time traffic information through Intelligent Transport System (ITS) integration.
Investigating Tertiary Students’ Perceptions on Internet SecurityITIIIndustries
Internet security threats have grown from just simple viruses to various forms of computer hacking, scams, impersonation, cyber bullying, and spyware. The Internet has great influence on most people. It has profound influence and one can spend endless hours on internet activities. In particular, youth engage in more online activities than any other age group. Excessive internet usage is an emerging threat that has negative impacts on these youth; hence it is vital to investigate youths' online behavior. This work studies tertiary students’ risk awareness, and provides some findings that allow us to understand their knowledge on risks and their behavior towards online activities. It reveals several important online issues amongst tertiary students; Firstly, the lack of online security awareness; second, a lack of awareness and information about the dangers of rootkits, internet cookies and spyware; thirdly, female students are more unflinching than male students when commenting on social networking sites; fourthly, students are cautious only when obvious security warnings are present; and finally, their usage of internet hotspots is common without fully understanding its associated danger. These findings enable us to recommend types of internet security habits and safety practices that students should adopt in future when they are exposed to online activities. A more holistic approach was considered which aims to minimize any future risks and dangers with online activities involving students.
Blind Image Watermarking Based on Chaotic MapsITIIIndustries
Security of a watermark refers to its resistance to unauthorized detecting and decoding, while watermark robustness refers to the watermark’s resistance against common processing. Many watermarking schemes emphasize robustness more than security. However, a robust watermark is not enough to accomplish protection because the range of hostile attacks is not limited to common processing and distortions. In this paper, we give consideration to watermark security. To achieve this, we employ chaotic maps due to their extreme sensitivity to the initial values. If one fails to provide these values, the watermark will be wrongly extracted. While the chaotic maps provide perfect watermarking security, the proposed scheme is also intended to achieve robustness.
Programmatic detection of spatial behaviour in an agent-based modelITIIIndustries
The automated detection of aspects of spatial behaviour in an agent-based model is necessary for model testing and analysis. In this paper we compare four predictors of herding behaviour in a model of a grazing herbivore. We find that a) the mean number of neighbours adjusted to account for population variation and b) the mean Hamming distance between rows of the two-dimensional environment can be used to detect herding. Visual inspection of the model behaviour revealed that herding occurs when the herbivore mobility reaches a threshold level. Using this threshold we identify a limits for these predictors to use in the program code. These results apply only to one set of parameters and environment size; future research will involve a wider parameter space.
Design of an IT Capstone Subject - Cloud RoboticsITIIIndustries
This paper describes the curriculum of the three year IT undergraduate program at La Trobe University, and the faculty requirements in designing a capstone subject, followed by the ACM’s recommended IT curriculum covering the five pillars of the IT discipline. Cloud robotics, a broad multidisciplinary research area, requiring expertise in all five pillars with mechatronics, is an ideal candidate to offer capstone experiences to IT students. Therefore, in this paper, we propose a long term master project in developing a cloud robotics testbed, with many capstone sub-projects spanning across the five IT pillars, to meet the objectives of capstone experience. This paper also describes the design and implementation of the testbed, and proposes potential capstone projects for students with different interests.
A Smart Fuzzing Approach for Integer Overflow DetectionITIIIndustries
Fuzzing is one of the most commonly used methods to detect software vulnerabilities, a major cause of information security incidents. Although it has advantages of simple design and low error report, its efficiency is usually poor. In this paper we present a smart fuzzing approach for integer overflow detection and a tool, SwordFuzzer, which implements this approach. Unlike standard fuzzing techniques, which randomly change parts of the input file with no information about the underlying syntactic structure of the file, SwordFuzzer uses online dynamic taint analysis to identify which bytes in the input file are used in security sensitive operations and then focuses on mutating such bytes. Thus, the generated inputs are more likely to trigger potential vulnerabilities. We evaluated SwordFuzzer with an example program and a number of real-world applications. The experimental results show that SwordFuzzer can accurately locate the key bytes of the input file and dramatically improve the effectiveness of fuzzing in detecting real-world vulnerabilities
The banking experience for many people today is fundamentally an application of technology to be able to carry out their financial tasks. While the need to visit a bank branch remains essential for a number of activities, increasingly the need to support mobile usage is becoming the central focus of many bank strategies. The core banking systems that process financial transactions must remain highly available and able to support large volumes of activity. These systems represent a long term investment for banks and when the need arises to modernize these large systems, the transformation initiative is often very expensive and of high risk. We present in this paper our experiences in bank modernization and transformation, and outline the strategies for rolling out these large programs. As banking institutions embark upon transformation programs to upgrade their banking channels and core banking systems, it is hoped that the insights presented here are useful as a framework to support these initiatives.
Detecting Fraud Using Transaction Frequency DataITIIIndustries
Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. In this paper, we present a fraud detection method which detects irregular frequency of transaction usage in an Enterprise Resource Planning (ERP) system. We discuss the design, development and empirical evaluation of outlier detection and distance measuring techniques to detect frequency-based anomalies within an individual user’s profile, relative to other similar users. Primarily, we propose three automated techniques: a univariate method, called Boxplot which is based on the sample’s median; and two multivariate methods which use Euclidean distance, for detecting transaction frequency anomalies within each transaction profile. The two multivariate approaches detect potentially fraudulent activities by identifying: (1) users where the Euclidean distance between their transaction-type set is above a certain threshold and (2) users/data points that lie far apart from other users/clusters or represent a small cluster size, using k-means clustering. The proposed methodology allows an auditor to investigate the transaction frequency anomalies and adjust the different parameters, such as the outlier threshold and the Euclidean distance threshold values to tune the number of alerts. The novelty of the proposed technique lies in its ability to automatically trigger alerts from transaction profiles, based on transaction usage performed over a period of time. Experiments were conducted using a real dataset obtained from the production client of a large organization using SAP R/3 (presently the most predominant ERP system), to run its business. The results of this empirical research demonstrate the effectiveness of the proposed approach.
Mapping the Cybernetic Principles of Viable System Model to Enterprise Servic...ITIIIndustries
This paper describes the results of a theoretical mapping of the cybernetic principles of the Viable System Model (VSM) to an Enterprise Service Bus (ESB) model, with the aim to identify the management principles for the integration of services at all levels in the enterprise. This enrichment directly contributes to the viability of service-oriented systems and the justification of Business/IT alignment within enterprise. The model was identified to be suitable for further adaption in the industrial setting planned within Australian governmental departments.
Using Watershed Transform for Vision-based Two-Hand Occlusion in an Interacti...ITIIIndustries
To achieve a natural interaction in augmented reality environment, we have suggested to use markerless visionbased two-handed gestures for the interaction; with an outstretched hand and a pointing hand used as virtual object registration plane and pointing device respectively. However, twohanded interaction always causes mutual occlusion which jeopardizes the hand gesture recognition. In this paper, we present a solution for two-hand occlusion by using watershed transform. The main idea is to start from a two-hand occlusion image in binary format, then form a grey-scale image based on the distance of each non-object pixel to object pixel. The watershed algorithm is applied to the negation of the grey scaled image to form watershed lines which separate the two hands. Fingertips are then identified and each hand is recognized based on the number of fingertips on each hand. The outstretched hand is assumed to contain 5 fingertips and the pointing device contains less than 5 fingertips. An example of applying our result in hand and virtual object interaction is displayed at the end of the paper.
Speech Feature Extraction and Data VisualisationITIIIndustries
—This paper presents a signal processing approach to analyse and identify accent discriminative features of four groups of English as a second language (ESL) speakers, including Chinese, Indian, Japanese, and Korean. The features used for speech recognition include pitch, stress, formant frequencies, the Mel frequency coefficient, log frequency coefficient, and the intensity and duration of vowels spoken. This paper presents our study using the Matlab Speech Analysis Toolbox, and highlights how data processing can be automated and results visualised. The proposed algorithm achieved an average success rate of 57.3% in identifying vowels spoken in a speech by the four nonnative English speaker groups.
Bayesian-Network-Based Algorithm Selection with High Level Representation Fee...ITIIIndustries
A real-world intelligent system consists of three basic modules: environment recognition, prediction (or estimation), and behavior planning. To obtain high quality results in these modules, high speed processing and real time adaptability on a case by case basis are required. In the environment recognition module many different algorithms and algorithm networks exist with varying performance. Thus, a mechanism that selects the best possible algorithm is required. To solve this problem we are using an algorithm selection approach to the problem of natural image understanding. This selection mechanism is based on machine learning; a bottom-up algorithm selection from real-world image features and a top-down algorithm selection using information obtained from a high level symbolic world description and algorithm suitability. The algorithm selection method iterates for each input image until the high-level description cannot be improved anymore. In this paper we present a method of iterative composition of the high level description. This step by step approach allows us to select the best result for each region of the image by evaluating all the intermediary representations and finally keep only the best one.
Instance Selection and Optimization of Neural NetworksITIIIndustries
Credit scoring is an important tool in financial institutions, which can be used in credit granting decision. Credit applications are marked by credit scoring models and those with high marks will be treated as “good”, while those with low marks will be regarded as “bad”. As data mining technique develops, automatic credit scoring systems are warmly welcomed for their high efficiency and objective judgments. Many machine learning algorithms have been applied in training credit scoring models, and ANN is one of them with good performance. This paper presents a higher accuracy credit scoring model based on MLP neural networks trained with back propagation algorithm. Our work focuses on enhancing credit scoring models in three aspects: optimize data distribution in datasets using a new method called Average Random Choosing; compare effects of training-validation-test instances numbers; and find the most suitable number of hidden units. Another contribution of this paper is summarizing the tendency of scoring accuracy of models when the number of hidden units increases. The experiment results show that our methods can achieve high credit scoring accuracy with imbalanced datasets. Thus, credit granting decision can be made by data mining methods using MLP neural networks.
Signature Forgery and the Forger – An Assessment of Influence on Handwritten ...ITIIIndustries
Signatures are widely used as a form of personal authentication. Despite ubiquity in deployment, individual signatures are relatively easy to forge, especially when only the static ‘pictorial’ outcome of the signature is considered at verification time. In this study, we explore opinions on signature usage for verification purposes, and how individuals rate a particular third-party signature in terms of ease of forgeability and their own ability to forge. We examine responses with respect to an individual’s experience of the forgeability/complexity of their own signature. Our study shows that past experience does not generally have an effect on perceived signature complexity nor the perceived effectiveness of an individual to themselves forge a signature. In assessing forgeability, most subjects cite the overall signature complexity and distinguishing features in reaching this decision. Furthermore, our research indicates that individuals typically vary their signature according to the scenario but generally little effort into the production of the signature.
Stability of Individuals in a Fingerprint System across Force LevelsITIIIndustries
This research studied the question: “Are all
individual’s performance stable in a fingerprint recognition
system?” The fingerprints of 154 individuals, provided at
different force levels, were examined using the biometric
menagerie tool, first coined by Doddington et al. in 1998. The
Biometric Menagerie illustrates how each person in a given
dataset performs in a biometric system, by using their genuine
and impostor scores, and providing them a classification based
upon those scores. This research examined the biometric
menagerie classifications across different force levels in a
fingerprint recognition study to uncover if individuals performed
the same over five force levels. The study concluded that they did
not, and a new metric has been created to quantify this
phenomenon. As a result of this discovery, the new metric,
Stability Score Index is described to showcase the movement of
individuals in the menagerie.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
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.
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.