ABSTRACT
Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a post processing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Retina is a layer which is found at the back side of the eye ball which plays main role for visualization. Any
disease in the retina leads to severe problems. Blood vessels segmentation and classification of retinal
vessels into arteries and veins is an essential thing for detection of various diseases like Diabetic
Retinography etc. This paper discusses about various existing methodologies for classification of retinal
image into artery and vein which are helpful for the detection of various diseases in retinal fundus image.
This process is basis for the AVR calculation i.e. for the calculation of average diameter of arteries to
veins. One of the symptoms of Diabetic Retinography causes abnormally wide veins and this leads to low
ratio of AVR. Diseases like high blood pressure and pancreas also have abnormal AVR. Thus classification
of blood vessels into arteries and veins is more important. Retinal fundus images are available on the
publically available Database like DRIVE [5], INSPIREAVR [6], VICAVR [7].
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Retina is a layer which is found at the back side of the eye ball which plays main role for visualization. Any
disease in the retina leads to severe problems. Blood vessels segmentation and classification of retinal
vessels into arteries and veins is an essential thing for detection of various diseases like Diabetic
Retinography etc. This paper discusses about various existing methodologies for classification of retinal
image into artery and vein which are helpful for the detection of various diseases in retinal fundus image.
This process is basis for the AVR calculation i.e. for the calculation of average diameter of arteries to
veins. One of the symptoms of Diabetic Retinography causes abnormally wide veins and this leads to low
ratio of AVR. Diseases like high blood pressure and pancreas also have abnormal AVR. Thus classification
of blood vessels into arteries and veins is more important. Retinal fundus images are available on the
publically available Database like DRIVE [5], INSPIREAVR [6], VICAVR [7].
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Determination with Deep Learning and One Layer Neural Network for Image Proce...IJERA Editor
Today’s world Coronary artery disease is the most common cause of death worldwide and thus early diagnosis. Well-timed opportune of this disease can lead to significant reduction in its morbidityand mortality in both younger and older for angiogram test. In this research multi slice CT scanner is used for heart angiogram test. With the help of this multi slice CT angiogram image we detect the hart diseased or not. For this disease identification and classification of angiogram images many machine learning algorithms are previously proposed those are SVM RBF and RBF neural network. Problem with SVM isnon-liner method when use any type of application will miss most liner ways of blood vessels and lack of speed in process. For non linear classification we are using RBF SVM. Problem with RBF neural network is not solve the hierarchal and component based problems, so resolve the problem using deep learning. This issue drastically improves the estimation efficiency for real time application. This methodology consumes less time for both learning as well as testing comparatively than any other methods. This issue highly improves the estimation efficiency and accuracy for real time 256, 512 slices CT scan angiogram image.
High volume computational histopathology 3Scan3Scan
Light microscopy is the gold standard for investigating microscopic structures and
pathological alterations in both human and animal models of disease. However, due
to tedious manual interventions, quantification of histopathologic markers is
classically performed on only a few 2D tissue sections, thus restricting
measurements and observations to limited portions of the sample volume.
Understanding vascular networks within tissues is a critical component to identifying pathophysiology. The work presented here introduces a methodology for multi-parametric quantification of vascular networks for whole-mount tissues. Our approach streamlines the histopathology workflow, enables observations within large 3D sample volumes, and provides opportunities for automated image analysis.
A SIMPLE APPROACH FOR RELATIVELY AUTOMATED HIPPOCAMPUS SEGMENTATION FROM SAGI...ijbbjournal
In this paper, we present a relatively automated method to segment the hippocampus in t1 weighted
magnetic resonance images that can be acquired in the routine clinical setting. This paper describes a
simple approach for segmenting the hippocampus automatically from sagittal view of brain MRI. Large
datasets of structural MR images are collected to quantitatively analyze the relationships between brain
anatomy, disease progression, treatment regimens, and genetic influences upon brain structure..This
method segments the hippocampus without any human intervention for few slices present mid position in
the total volume. Experimental results using this method show a good agreement with the manual
segmented gold standard. These results may support the clinical studies of memory and neurodegenerative
disease
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
Liver segmentation from ct images using a modified distance regularized level...csandit
Organ segmentation from medical images is still an open problem and liver segmentation is a
much more challenging task among other organ segmentations. This paper presents a liver
segmentation method from a sequence of computer to mography images.We propose a novel
balloon force that controls the direction of the evolution process and slows down the evolving
contour in regions with weak or without edges and discourages the evolving contour from going
far away from the liver boundary or from leaking at a region that has a weak edge, or does not
have an edge. The model is implemented using a modified Distance Regularized Level Set
(DRLS) model. The experimental results show that the method can achieve a satisfactory result.
Comparing with the original DRLS model, our model is more effective in dealing with over
segmentation problems.
Performance analysis of retinal image blood vessel segmentationacijjournal
The retinal image diagnosis
is an important methodology for diabetic retinopathy detection and analysis. in
this paper, the morphological operations and svm classifier are used to detect and segment the blood
vessels from the retinal image. the proposed system consists of three stage
s
-
first is preprocessing of retinal
image to separate the green channel and second stage is retinal image enhancement and third stage is
blood vessel segmentation using morphological operations and svm classifier. the performance of the
proposed system is
analyzed using publicly available dataset
Determination with Deep Learning and One Layer Neural Network for Image Proce...IJERA Editor
Today’s world Coronary artery disease is the most common cause of death worldwide and thus early diagnosis. Well-timed opportune of this disease can lead to significant reduction in its morbidityand mortality in both younger and older for angiogram test. In this research multi slice CT scanner is used for heart angiogram test. With the help of this multi slice CT angiogram image we detect the hart diseased or not. For this disease identification and classification of angiogram images many machine learning algorithms are previously proposed those are SVM RBF and RBF neural network. Problem with SVM isnon-liner method when use any type of application will miss most liner ways of blood vessels and lack of speed in process. For non linear classification we are using RBF SVM. Problem with RBF neural network is not solve the hierarchal and component based problems, so resolve the problem using deep learning. This issue drastically improves the estimation efficiency for real time application. This methodology consumes less time for both learning as well as testing comparatively than any other methods. This issue highly improves the estimation efficiency and accuracy for real time 256, 512 slices CT scan angiogram image.
High volume computational histopathology 3Scan3Scan
Light microscopy is the gold standard for investigating microscopic structures and
pathological alterations in both human and animal models of disease. However, due
to tedious manual interventions, quantification of histopathologic markers is
classically performed on only a few 2D tissue sections, thus restricting
measurements and observations to limited portions of the sample volume.
Understanding vascular networks within tissues is a critical component to identifying pathophysiology. The work presented here introduces a methodology for multi-parametric quantification of vascular networks for whole-mount tissues. Our approach streamlines the histopathology workflow, enables observations within large 3D sample volumes, and provides opportunities for automated image analysis.
A SIMPLE APPROACH FOR RELATIVELY AUTOMATED HIPPOCAMPUS SEGMENTATION FROM SAGI...ijbbjournal
In this paper, we present a relatively automated method to segment the hippocampus in t1 weighted
magnetic resonance images that can be acquired in the routine clinical setting. This paper describes a
simple approach for segmenting the hippocampus automatically from sagittal view of brain MRI. Large
datasets of structural MR images are collected to quantitatively analyze the relationships between brain
anatomy, disease progression, treatment regimens, and genetic influences upon brain structure..This
method segments the hippocampus without any human intervention for few slices present mid position in
the total volume. Experimental results using this method show a good agreement with the manual
segmented gold standard. These results may support the clinical studies of memory and neurodegenerative
disease
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
typically comprise noise, inhomogeneity and sometimes deviation. Therefore, precise segmentation of brain
images is a very challenging task. Nevertheless, the process of perfect segmentation of these images is very
important and crucial for a spot-on diagnosis by clinical tools. Also, intensity inhomogeneity often arises in realworld
images, which presents a substantial challenge in image segmentation. The most extensively used image
segmentation algorithms are region-based and usually rely on the homogeneousness of the image intensities in
the sections of interest, which often fail to afford precise segmentation results due to the intensity
inhomogeneity. This Research presents a more accurate segmentation using Gradient Based watershed
transform in level set method for a medical diagnosis system. Experimental results proved that our method
validating a much better rate of segmentation accuracy as compare to the traditional approaches, results are also
validated in terms of certain Measure properties of image regions like eccentricity, perimeter etc.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
Liver segmentation from ct images using a modified distance regularized level...csandit
Organ segmentation from medical images is still an open problem and liver segmentation is a
much more challenging task among other organ segmentations. This paper presents a liver
segmentation method from a sequence of computer to mography images.We propose a novel
balloon force that controls the direction of the evolution process and slows down the evolving
contour in regions with weak or without edges and discourages the evolving contour from going
far away from the liver boundary or from leaking at a region that has a weak edge, or does not
have an edge. The model is implemented using a modified Distance Regularized Level Set
(DRLS) model. The experimental results show that the method can achieve a satisfactory result.
Comparing with the original DRLS model, our model is more effective in dealing with over
segmentation problems.
Performance analysis of retinal image blood vessel segmentationacijjournal
The retinal image diagnosis
is an important methodology for diabetic retinopathy detection and analysis. in
this paper, the morphological operations and svm classifier are used to detect and segment the blood
vessels from the retinal image. the proposed system consists of three stage
s
-
first is preprocessing of retinal
image to separate the green channel and second stage is retinal image enhancement and third stage is
blood vessel segmentation using morphological operations and svm classifier. the performance of the
proposed system is
analyzed using publicly available dataset
Fractals for complexity analysis of diabetic retinopathy in retinal vasculatu...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Fractals for complexity analysis of diabetic retinopathy in retinal vasculatu...eSAT Journals
Abstract Arterial pattern and morphology of distribution is damaged because of diabetes resulting in retinal vasculature deformation. This aspect is studied in terms of quantification of the degree of complexity associated with the distribution of blood vessels in eye for healthy and diabetic humans. Retina images of fifteen healthy subjects are compared with those of diabetic subjects. It is found that the increased complexity of structure and texture of the diabetic subjects results in a higher fractal dimension as compared to those of healthy subjects. Also the blood vessel patterns, both for healthy and diabetic subjects show self-similarity and scale invariance and hence the patterns are fractals. For the purpose of characterization of the irregular patterns of blood vessels in retina, box counting technique is used for the estimation of fractal dimension. A GUI based program is developed in Matlab for implementation of box counting technique and determination of fractal dimensions. Fractal dimension of retina images for diabetic subjects show higher fractal dimensions indicating higher degree of structural complexity associated with the image whereas images of healthy subjects show a lower value of fractal dimension indicating limited complexity of structure. It is shown that fractal dimension can be used to distinguish diabetic subjects from healthy subjects and hence this technique could be used in diagnosis of diabetes using images of retina. It is interesting that during other diagnostic procedures related to retina images, this information can be generated as additional information adding value to the diagnostic procedures. Details of implementation of the technique are presented. Keywords: Diabetic Retinopathy, Fractal Dimension, Box Counting, Segmentation, Image Processing
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 Automated Systems for the Detection of Macular Ischemia based-Diabetic Ret...iosrjce
The proposed methodology in this paper marks out application for automatic detection of eye
diseases called Macular Ischemia using image processing techniques. In semi urban and rural areas large
percentages of people suffer from various eye diseases. For diagnoses of various eye diseases, Image processing
technique is used. . Diseases occur in Macula from retinal images have a huge type of textures, shapes and at
times they are difficult to be recognised and identified by doctors. Thus we are trying to optimize and develop
such system which is based on smart image recognition/classification algorithms. This proposed system
provides accuracy, uniformity and speed in performance and a high credence coefficient in results interpreting.
Keywords: Macular Ischemia, diagnosis, textures, consistence
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Segmentation of Blood Vessels and Optic Disc in Retinal Imagesresearchinventy
Retinal image analysis is increasingly prominent as a non-intrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disc in the fundus retinal images. The method could be used to support non-intrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disc is an important indicator for diseases like diabetic retinopathy, glaucoma and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disc. The optic disc segmentation is performed using two alternative methods. The Markov Random Field (MRF) image reconstruction method segments the optic disc by removing vessels from the optic disc region and the Compensation Factor method segments the optic disc using prior local intensity knowledge of the vessels. The proposed method is tested on three public data sets, DIARETDB1, DRIVE and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disc.
Similar to AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD VESSEL (20)
Advanced Computing: An International Journal (ACIJ) is a peer-reviewed, open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and a practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of computing.
Call for Papers - Advanced Computing An International Journal (ACIJ) (2).pdfacijjournal
Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
Webpage URL: http://airccse.org/journal/acij/acij.html
Submission URL: http://coneco2009.com/submissions/imagination/home.html
Submission Deadline : April 08, 2023
Here's where you can reach us : acijjournal@yahoo.com or acij@aircconline
Advanced Computing: An International Journal (ACIJ
)
is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advancedcomputing. The journal focuses on all technical and practical aspects of high performancecomputing, green computing, pervasive computing, cloud computing etc. The goal of this journalis to bring together researchers anda practitioners from academia and industry to focus onunderstanding advances in computing and establishing new collaborations in these areas
Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
Webpage URL: http://airccse.org/journal/acij/acij.html
Submission URL: http://coneco2009.com/submissions/imagination/home.html
Here's where you can reach us : acijjournal@yahoo.com or acij@aircconline.com
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7thInternational Conference on Data Mining & Knowledge Management (DaKM 2022)acijjournal
7thInternational Conference on Data Mining & Knowledge Management (DaKM 2022)provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum.
7thInternational Conference on Data Mining & Knowledge Management (DaKM 2022)acijjournal
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7thInternational Conference on Data Mining & Knowledge Management (DaKM 2022)provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum.Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
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Advanced Computing: An International Journal (ACIJ) acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
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Detection of Forgery and Fabrication in Passports and Visas Using Cryptograph...acijjournal
In this paper, wepresenta novel solution to detect forgery and fabrication in passports and visas using cryptography and QR codes. The solution requires that the passport and visa issuing authorities obtain a cryptographic key pair and publish their public key on their website. Further they are required to encrypt the passport or visa information with their private key, encode the ciphertext in a QR code and print it on the passport or visa they issue to the applicant.
The issuing authorities are also required to create a mobile or desktop QR code scanning app and place it for download on their website or Google Play Store and iPhone App Store. Any individual or immigration authority that needs to check the passport or visa for forgery and fabrication can scan its QR code, which will decrypt the ciphertext encoded in the QR code using the public key stored in the app memory and displays the passport or visa information on the app screen. The details on the app screen can be compared with the actual details printed on the passport or visa. Any mismatch between the two is a clear indication of forgery or fabrication.
Discussed the need for a universal desktop and mobile app that can be used by immigration authorities and consulates all over the world to enable fast checking of passports and visas at ports of entry for forgery and fabrication.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
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introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
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using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
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adversary training.
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Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD VESSEL
1. Advanced Computing: An International Journal (ACIJ), Vol.7, No.1/2, March 2016
DOI:10.5121/acij.2016.7207 61
AUTOMATED SEGMENTATION OF FLUORESCENT
AND FUNDS IMAGES BASED ON RETINAL BLOOD
VESSEL
P.Sumitra, P.Ponkavitha, K.S.Saravanan and S.Karthika
Department of Computer Science and Applications
Vivekanandha College of Arts and Sciences for Women (Autonomous)
Elayampalayam, Tiruchengode
ABSTRACT
Measurements of retinal blood vessel morphology have been shown to be related to the risk of
cardiovascular diseases. The wrong identification of vessels may result in a large variation of these
measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of
automatically identifying true vessels as a post processing step to vascular structure segmentation. We
model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying
vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to
solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images.
Experiment results are analyzed with respect to actual measurements of vessel morphology. The results
show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true
vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
KEYWORDS
Ophthalmology, optimal vessel forest, retinal image analysis, simultaneous vessel identification , vascular
structure
1. INTRODUCTION
Biological Machine Engineering (BME) is the application of engineering principles and design
concepts to medicine and biology for healthcare purposes (e.g. diagnostic or therapeutic). This
field seeks to close the gap between engineering and medicine. It combines the design and
problem solving skills of engineering with medical and biological sciences to advance healthcare
treatment, including diagnosis, monitoring, and therapy.
Therapeutic medical devices ranging from clinical equipment to micro-implants, common
imaging equipment such as MRIs and EEGs, regenerative tissue growth, pharmaceutical drugs
and therapeutic biological . Subdisciplines of biomedical engineering can be viewed from two
angles, from the medical applications side and from the engineering side. A biomedical engineer
must have some view of both sides. As with many medical specialties (e.g. cardiology,
neurology), some BME sub-disciplines are identified by their associations with particular systems
of the human body, such as:
2. Advanced Computing: An International Journal (ACIJ), Vol.7, No.1/2, March 2016
62
1.1 Cardiovascular technology - which includes all drugs, biologics, and devices related with
diagnostics and therapeutics of cardiovascular systems.
1.2 Neural technology - which includes all drugs, biologics, and devices related with
diagnostics and therapeutics of the brain and nervous systems.
1.3 Orthopaedic technology - which includes all drugs, biologics, and devices related with
diagnostics and particular aspects of anatomy or physiology. A variant on this approach is to
identify types of technologies based on a kind of path physiology sought to remedy apart from
any particular system of the body, for example:
1.4 Cancer technology - which includes all drugs, biologics, and devices related with
diagnostics and therapeutics of cancer.
2. OVERVIEW
In this project we implemented a novel technique that utilizes the global information of the
segmented vascular structure to correctly identify true vessels in a retinal image. We model the
segmented vascular structure as a vessel segment graph and transform the problem of identifying
true vessels to that of finding an optimal forest in the graph. An objective function to score forests
is designed based on directional information. Our proposed solution employs candidate
generation and expert knowledge to prune the search space. We demonstrate the effectiveness of
our approach on a large real-world dataset of 2446 retinal images. We introduce a novel vessel
enhancement technique based on the matched filters with multi wavelet kernels (MFMK). We
identify kernels separating vessels from clutter edges and bright, localized features (e.g.,
lesions).For noise attenuation and vessel localization, we apply a multistate hierarchical
decomposition, which is particularly effective for the normalized enhanced image. This process
performs an iterative segmentation at increasing image resolutions, locating smaller and smaller
vessels. A single scale parameter controls the level of detail included in the vessel map. We show
a necessary condition to achieve the optimal decomposition, deriving a rule to identify the
optimal number of the hierarchical decomposition. Our method does not require pre-processing
and training it can therefore be used directly on images with different characteristics. In addition,
it relies on adaptive thresholding so that no numerical parameter is tuned manually to obtain a
binary mask.
3. LITERATURE SURVEY
3.1 Some links between extreme spanning forests, watersheds and min-cuts
Min-cuts (graph cuts) and watersheds are two popular tools for image segmentation, which can
both be expressed in the framework of graphs and are well suited to computer implementations.
Informally, a cut in a connected graph is a set of edges which, when removed from the graph,
separates it into several connected components. Given a set of nodes or sub graphs called
markers, the goal of these operators is to and a cut for which each induced component contains
exactly one marker and which best matches a criterion based on the image contents.In this paper,
we present some results about the links which exist between these different approaches.
Especially, we show that extreme spanning forests are particular cases of watersheds from
arbitrary markers and that min-cuts coincide with extreme spanning forests for some particular
weight functions[1].
3. Advanced Computing: An International Journal (ACIJ), Vol.7, No.1/2, March 2016
63
3.2 Watershed cuts: minimum spanning forests and the drop of water principle
In order to compute the watershed of a digital image, several approaches have been proposed.
Many of them consider a gray scale digital image as a vertex-weighted graph. One of the most
popular consists of simulating a flooding of the topographic surface from its regional minima.
The divide is made of “dams” built at those points where water coming from different minima
would meet. Another approach, called topological watershed , allows the authors to rigorously
define the notion of a watershed in a discrete space and to prove important properties not
guaranteed by most watershed algorithms . We propose a linear-time algorithm to compute the
watershed-cuts. As far as we know, the proposed algorithm is the most efficient existing
watershed algorithm both in theory and practice. Finally, we illustrate the use of watershed-cuts
for application to image segmentation and show that, in the considered cases, they are able to
improve the quality of the delineation in watershed-based segmentation procedures[2].
3.3 Unsupervised Curvature-Based Retinal Vessel Segmentation
Automatic segmentation of the vessel tree from color retinal images has received much attention
recently given its important role in image registration and in disease identification such as in
diabetic retinopathy and hypertension. Techniques ranging from multi-level thresholding to
model-based have been proposed. In the latter, information about the vessel morphology such as
linearity, coloring, circular cross section, etc., are used to construct feature sets which are used for
either supervised classification or to devise filters for detection. In the course of our experiments,
we discovered that the ground truth markings in the DRIVE dataset tended to be over segmented
at times, perhaps as a sign of the above problem, in which case the vessel thickness obtained prior
to the last dilation step in our algorithm was found to be closer to the true vessel thickness as
detected by a Canny detector[3].
3.4 Automatic Grading of Retinal Vessel Caliber
New clinical studies suggest that narrowing of the retinal blood vessels may be an early indicator
of cardiovascular diseases. One measure to quantify the severity of retinal arteriolar narrowing is
the arteriolar-to-venular diameter ratio (AVR). The manual computation of AVR is a tedious
process involving repeated measurements of the diameters of all arterioles and venules in the
retinal images by human graders. Consistency and reproducibility are concerns. To facilitate
large-scale clinical use in the general population, it is essential to have a precise, efficient and
automatic system to compute this AVR. This paper describes a new approach to obtain AVR. The
starting points of vessels are detected using a matched Gaussian filter. The detected vessel filter.
A modified Gaussian model that takes into account the central light reflection of arterioles is
proposed to describe the vessel profile. The width of a vessel is obtained by data fitting.
Experimental results indicate a 97.1% success rate in the identification of vessel starting points,
and a 99.2% success rate in the tracking of retinal vessels. The accuracy of the AVR computation
is well within the acceptable range of deviation among the human graders, with a mean relative
AVR error of 4.4%. The system has interested clinical research groups worldwide and will be
tested in clinical studies[4].
3.5 Retinal Vascular Tree Morphology: A Semi-Automatic Quantification
Quantitative analyses of retinal blood vessels from fundus images have usually been studied in
terms of individual bifurcations, measuring a few of the most clearly visible bifurcations in an
image. Most of these studies have focused mainly on diameter measurements although others also
included midline detection and tortuosity measurements .Those studies that have characterized
4. Advanced Computing: An International Journal (ACIJ), Vol.7, No.1/2, March 2016
64
continuous blood vessel trees from retinal images, by means of image processing techniques,
have been mainly focused on the detection process rather than the measurement of geometrical or
topological properties a semi-automatic method to measure and tabulate geometrical data as well
as connectivity information from continuous retinal vessel trees is presented. Data are extracted
from binary images obtained from a previously developed segmentation method. The procedure
consists of a semi-automatic labelling of the skeleton trees followed by an automatic procedure
for measurement and generation of tabulated data for further analysis. Several geometrical and
topological indexes are extracted. The methods are validated by comparison with manual
measurements and applied to a pilot study of ten normal and ten hypertensive subjects and
differences between groups in the morphological properties are investigated[5].
4. METHODOLOGY APPLIED
Pre processing
Vessel tracking
Seed point extraction
Segmented retinal vessel morphology
Graph cut algorithm
4.1 Preprocessing
• Preprocessing of an image is done to reduce the noise and to enhance the image for
further processing.
• To improve the image and the image quality to get more accuracy and pixel precision in
segmenting the Retinal blood vessel.
4.2 Vessel Tracking
Find starting and ending points.
In order to identify the vessel profile along the scan line, matched filters are used.
(A) Zone of Interest (B) Line Image
Figure.1.Vessels Tracking
This is a circular ring bounded by two concentric circles of radii 2r and 5r. where r is the radius of
the optic disc (OD). Measurements from this zone are used in a number of clinical studies. Each
vessel starts from a pixel near the circle of radius 2r. These pixels are called root pixels.
4.3 Seed Point Extraction
• A scan analysis is performed on each of these lines, in order to identify sequences of pixels
corresponding to possible vessel profiles.
• Due to the presence in the neighborhood of both vessel" (dark) and non-vessel”(light)
pixels. Mean and standard deviation of all the seed points are computed.
5. Advanced Computing: An International Journal (ACIJ), Vol.7, No.1/2, March 2016
65
Figure.2.Seed Point Tracking
4.4 Segmented retinal vessel morphology
A)Wrong Identification Of I and II B)Correct Identification Of I and II
Figure.3.Vessels Identification
4.5 Segmentation
There are advanced segmentation algorithms in the literature which extends the concepts of graph
cuts. Prominent of them are :
4.5.1 Grab Cut: Grab Cuts extends graph-cut by introducing iterative segmentation scheme that
uses graph-cut for intermediate steps.
4.5.2 Lazy Snapping: Lazy snapping is an interactive image cut out tool. Lazy Snapping
separates coarse and fine scale processing, making object specification and detailed adjustment
easy.
4.5.3 Grow Cut: Given a small number of user-labeled pixels, the rest of the image is segmented
automatically by a Cellular Automation.
A)Grab Cut B)Lazy Snapping C)Grow Cut
Figure 4.Graph Cut Retina Segmentation
5. CONCLUSION
Retinal Blood vessel morphology is an important indicator for many diseases such as diabetes,
hypertension and cardiovascular, and the measurement of geometrical changes in retinal veins
and arteries and is applied to a variety of clinical studies. Two of the major problems in the
segmentation of retinal blood vessels namely the presence of a wide variety of vessel widths and
inhomogeneous background of the retina have been addressed. A method of automated
segmentation for both fluorescent and funds images of the retinal blood vessel has been proposed.
6. Advanced Computing: An International Journal (ACIJ), Vol.7, No.1/2, March 2016
66
ACKNOWLEDGEMENT
We like to thank all those who gave us their support to complete this paper.
6. REFERENCES
[1] S M. Elena Martinez-Perez, Alun D. Hughes, Alice V. Stanton, Simon A. Thom, Neil Chapman, Anil
A. Bharath, and Kim H. Parker Retinal Vascular Tree Morphology: A Semi-Automatic Quantification
IEEE Transactions on biomedical engineering, vol. 49, no. 8, august 2002
[2] H. Li, W. Hsu, M. L. Lee, and T. Y. Wong, “Automatic grading of retinal vessel caliber,” IEEE
Trans. Biomed. Eng., vol. 52, no. 7, pp. 1352–1355, Jul. 2005.
[3] S . Garg, J. Sivaswamy , and S. Chandra, “Unsupervised curvature-based retinal vessel
segmentation,” in Proc. IEEE Int. Symp. Biomed. Imaging, Apr. 2007, pp. 344–347.
[4] J. Cousty, G. Bertrand, L. Najman, and M. Couprie, “Watershed cuts: Minimum spanning forests and
the drop of water principle,” IEEE Trans.Pattern Anal. Mach. Intell., vol. 31, no. 8, pp. 1362–
1374, Aug. 2009.
[5] C. All`ene, J.-Y. Audibert, M. Couprie, and R. Keriven, “Some links between extremum spanning
forests, watersheds and min-cuts,” Imag. Vis.Comput., vol. 28, pp. 1460–1471, 2010.
Authors
Dr.P.Sumitra. received her Ph.D Degree in Computer Science from Mother Teresa
Women’s University, Kodaikannal, Tamil Nadu, India in the year 2013. She is currently
working as a Assistant Professor in Dept of Computer Science, Vivekanandha College of
Arts and Sciences for Women, Elayamapalayam, Tiruchengode, TamilNadu,India. She
published 17 International Journal papers, 4 papers in International Conference and 10
papers in National Conferences. Her research areas include Image Processing, Data
Mining and Artificial Intelligence. She has 13 years 8 Months of teaching experience in
self finance institutions.
P.Ponkavitha M.Phil Research Scholar, Department of Computer Science and
Applications Vivekanandha College of Arts and Sciences for Women (Autonomous)
Elayampalayam , Tiruchengode.
K.S.Saravanan working as a Assistant Professor in Dept of Computer Science &
Applications, Vivekanandha College of Arts and Sciences for Women(Autonomous),
Elayamapalayam, Tiruchengode, TamilNadu,India.
S.Karthika M.Phil Research Scholar, Department of Computer Science and Applications
Vivekanandha College of Arts and Sciences for Women (Autonomous)Elayampalayam ,
Tiruchengode.