Abstract Headlights of vehicles pose a great danger during night driving. The drivers of most vehicles use high, bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction. He experiences a sudden glare for a short period of time. This is caused due to the high intense headlight beam from the other vehicle coming towards him from the opposite direction. We are expected to dim the headlight to avoid this glare. This glare causes a temporary blindness to a person resulting in road accidents during the night. To avoid such incidents, we have fabricated a prototype of automatic headlight dimmer. This automatically switches the high beam into low beam thus reducing the glare effect by sensing the approaching vehicle. It also eliminates the requirement of manual switching by the driver which is not done at all times. The construction, working and the advantages of this prototype model is discussed in detail in this paper. Keywords: Headlight, automatic, dimmer, control, high beam, low beam, Kelvin (K).
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 optic disc and blood vessels from retinal images using...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
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.
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%
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 optic disc and blood vessels from retinal images using...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
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.
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%
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 Screening of Diabetic Retinopathy Using Image Processingiosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
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.
Vessels delineation in retinal images using COSFIRE filtersNicola Strisciuglio
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal images”, Medical Image Analysis, Available Online 3 September 2014, DOI: 10.1016/j.media.2014.08.002
The source code of the B-COSFIRE filters is available at:
http://www.mathworks.com/matlabcentral/fileexchange/49172-trainable-cosfire-filters-for-vessel-delineation-with-application-to-retinal-images
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
Automatic Detection of Diabetic Maculopathy from Funduas Images Using Image A...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
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.
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.
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.
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Retina images are obtained from the fundus camera a
nd graded by skilled professionals. However there i
s
considerable shortage of expert observers has encou
raged computer assisted monitoring. Evaluation of
blood vessels network plays an important task in a
variety of medical diagnosis. Manifestations of
numerous vascular disorders, such as diabetic retin
opathy, depend on detection of the blood vessels
network. In this work the fundus RGB image is used
for obtaining the traces of blood vessels and areas
of
blood vessels are used for detection of Diabetic Re
tinopathy (DR). The algorithm developed uses
morphological operation to extract blood vessels. M
ainly two steps are used: firstly enhancement opera
tion
is applied to original retina image to remove noise
and increase contrast of retinal blood vessels. Se
condly
morphology operations are used to take out blood ve
ssels. Experiments are conducted on publicly availa
ble
DIARETDB1 database. Experimental results obtained b
y using gray-scale images have been presented.
Detection and Grading of Diabetic Maculopathy Automatically in Digital Retina...paperpublications3
Abstract: Diabetic Retinopathy (DR) is a critical eye disease which can be regarded as manifestation of diabetes on the retina the symptoms can blur or distort the patient’s vision and are a main cause of blindness. Exudates are one of the signs of Diabetic Retinopathy. If the disease is detected early and treated promptly many of the visual loss can be prevented. This paper explains the development of an automatic fundus image processing and analytic system to facilitate diagnosis of the ophthalmologists. The algorithms to detect the optic disc; blood vessels and exudates are investigated. The proposed system extracts macula from digital retinal image using the optic disc location. Many common features such as intensity, geometric and correlations are used to distinguish between them. The system uses GLCM for feature extraction. The system uses a SVM based classifier to differentiate the retinal images in different stages of maculopathy by using the macula co-ordinates and exudates feature set.
Diabetic retinopathy is the cause for
blindness in the human society. Early detection of it prevents
blindness. Image processing techniques can reduce the work of
ophthalmologists and the tools used to detect Diabetic
Retinopathy Patients. Proliferative diabetic retinopathy is the
most advanced stage of diabetic retinopathy, and is classified by
the growth of new blood vessels. These blood vessels are
abnormal and fragile, and are susceptible to leaking blood and
fluid onto the retina, which can cause severe vision loss. First,
vessel-like patterns are segmented by using Ridge Strength
Measurement and Watershed lines. The second step is measuring
the vessel pattern obtained [5][10]. Many features that are
extracted from the blood vessels such as shape, position,
orientation, brightness, contrast and line density have been used
to quantitative patterns in retinal vasculature. Based on the seven
features extracted, the segment is classified as normal or
abnormal by using Support Vector Machine Classifier [6][8]. The
obtained accuracy may be sufficient to reduce the workload of an
ophthalmologist and to prioritize the patient grading queues.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Teamed with 2 students to research and implement the automation of diagnosis of Diabetic Retinopathy and co-ordinated with an Ophthalmologist to verify our implementation.
Responsibilities included MATLAB coding, algorithm testing, and product documentation.
• Automation in MATLAB involving retinal image analysis to help
Ophthalmologist increase the productivity and efficiency in a clinical
environment.
• Used Image Processing concepts such as Hough Transform, Bottom Hat
Transform, Edge Detection Technique and Morphological Operators.
Provided our algorithm and documentation to our research faculty advisor to enable him to continue this research to the next phase.
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 Screening of Diabetic Retinopathy Using Image Processingiosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
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.
Vessels delineation in retinal images using COSFIRE filtersNicola Strisciuglio
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal images”, Medical Image Analysis, Available Online 3 September 2014, DOI: 10.1016/j.media.2014.08.002
The source code of the B-COSFIRE filters is available at:
http://www.mathworks.com/matlabcentral/fileexchange/49172-trainable-cosfire-filters-for-vessel-delineation-with-application-to-retinal-images
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
Automatic Detection of Diabetic Maculopathy from Funduas Images Using Image A...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
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.
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.
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.
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Retina images are obtained from the fundus camera a
nd graded by skilled professionals. However there i
s
considerable shortage of expert observers has encou
raged computer assisted monitoring. Evaluation of
blood vessels network plays an important task in a
variety of medical diagnosis. Manifestations of
numerous vascular disorders, such as diabetic retin
opathy, depend on detection of the blood vessels
network. In this work the fundus RGB image is used
for obtaining the traces of blood vessels and areas
of
blood vessels are used for detection of Diabetic Re
tinopathy (DR). The algorithm developed uses
morphological operation to extract blood vessels. M
ainly two steps are used: firstly enhancement opera
tion
is applied to original retina image to remove noise
and increase contrast of retinal blood vessels. Se
condly
morphology operations are used to take out blood ve
ssels. Experiments are conducted on publicly availa
ble
DIARETDB1 database. Experimental results obtained b
y using gray-scale images have been presented.
Detection and Grading of Diabetic Maculopathy Automatically in Digital Retina...paperpublications3
Abstract: Diabetic Retinopathy (DR) is a critical eye disease which can be regarded as manifestation of diabetes on the retina the symptoms can blur or distort the patient’s vision and are a main cause of blindness. Exudates are one of the signs of Diabetic Retinopathy. If the disease is detected early and treated promptly many of the visual loss can be prevented. This paper explains the development of an automatic fundus image processing and analytic system to facilitate diagnosis of the ophthalmologists. The algorithms to detect the optic disc; blood vessels and exudates are investigated. The proposed system extracts macula from digital retinal image using the optic disc location. Many common features such as intensity, geometric and correlations are used to distinguish between them. The system uses GLCM for feature extraction. The system uses a SVM based classifier to differentiate the retinal images in different stages of maculopathy by using the macula co-ordinates and exudates feature set.
Diabetic retinopathy is the cause for
blindness in the human society. Early detection of it prevents
blindness. Image processing techniques can reduce the work of
ophthalmologists and the tools used to detect Diabetic
Retinopathy Patients. Proliferative diabetic retinopathy is the
most advanced stage of diabetic retinopathy, and is classified by
the growth of new blood vessels. These blood vessels are
abnormal and fragile, and are susceptible to leaking blood and
fluid onto the retina, which can cause severe vision loss. First,
vessel-like patterns are segmented by using Ridge Strength
Measurement and Watershed lines. The second step is measuring
the vessel pattern obtained [5][10]. Many features that are
extracted from the blood vessels such as shape, position,
orientation, brightness, contrast and line density have been used
to quantitative patterns in retinal vasculature. Based on the seven
features extracted, the segment is classified as normal or
abnormal by using Support Vector Machine Classifier [6][8]. The
obtained accuracy may be sufficient to reduce the workload of an
ophthalmologist and to prioritize the patient grading queues.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Teamed with 2 students to research and implement the automation of diagnosis of Diabetic Retinopathy and co-ordinated with an Ophthalmologist to verify our implementation.
Responsibilities included MATLAB coding, algorithm testing, and product documentation.
• Automation in MATLAB involving retinal image analysis to help
Ophthalmologist increase the productivity and efficiency in a clinical
environment.
• Used Image Processing concepts such as Hough Transform, Bottom Hat
Transform, Edge Detection Technique and Morphological Operators.
Provided our algorithm and documentation to our research faculty advisor to enable him to continue this research to the next phase.
Top 10 USA Business Future Trends 2015 - Roger James HamiltonRoger Hamilton
Slides from the Top 10 Trends 2014 USA Tour, hosted in Los Angeles, August 2014. How will the waves of the future impact your business? Join Roger James Hamilton in upcoming events and entrepreneur accelerators around the world at http://www.rogerjameshamilton.com
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.
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
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
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
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.
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.
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.
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.
Mechanical properties of hybrid fiber reinforced concrete for pavementseSAT Journals
Abstract
The effect of addition of mono fibers and hybrid fibers on the mechanical properties of concrete mixture is studied in the present
investigation. Steel fibers of 1% and polypropylene fibers 0.036% were added individually to the concrete mixture as mono fibers and
then they were added together to form a hybrid fiber reinforced concrete. Mechanical properties such as compressive, split tensile and
flexural strength were determined. The results show that hybrid fibers improve the compressive strength marginally as compared to
mono fibers. Whereas, hybridization improves split tensile strength and flexural strength noticeably.
Keywords:-Hybridization, mono fibers, steel fiber, polypropylene fiber, Improvement in mechanical properties.
Material management in construction – a case studyeSAT Journals
Abstract
The objective of the present study is to understand about all the problems occurring in the company because of improper application
of material management. In construction project operation, often there is a project cost variance in terms of the material, equipments,
manpower, subcontractor, overhead cost, and general condition. Material is the main component in construction projects. Therefore,
if the material management is not properly managed it will create a project cost variance. Project cost can be controlled by taking
corrective actions towards the cost variance. Therefore a methodology is used to diagnose and evaluate the procurement process
involved in material management and launch a continuous improvement was developed and applied. A thorough study was carried
out along with study of cases, surveys and interviews to professionals involved in this area. As a result, a methodology for diagnosis
and improvement was proposed and tested in selected projects. The results obtained show that the main problem of procurement is
related to schedule delays and lack of specified quality for the project. To prevent this situation it is often necessary to dedicate
important resources like money, personnel, time, etc. To monitor and control the process. A great potential for improvement was
detected if state of the art technologies such as, electronic mail, electronic data interchange (EDI), and analysis were applied to the
procurement process. These helped to eliminate the root causes for many types of problems that were detected.
Managing drought short term strategies in semi arid regions a case studyeSAT Journals
Abstract
Drought management needs multidisciplinary action. Interdisciplinary efforts among the experts in various fields of the droughts
prone areas are helpful to achieve tangible and permanent solution for this recurring problem. The Gulbarga district having the total
area around 16, 240 sq.km, and accounts 8.45 per cent of the Karnataka state area. The district has been situated with latitude 17º 19'
60" North and longitude of 76 º 49' 60" east. The district is situated entirely on the Deccan plateau positioned at a height of 300 to
750 m above MSL. Sub-tropical, semi-arid type is one among the drought prone districts of Karnataka State. The drought
management is very important for a district like Gulbarga. In this paper various short term strategies are discussed to mitigate the
drought condition in the district.
Keywords: Drought, South-West monsoon, Semi-Arid, Rainfall, Strategies etc.
Life cycle cost analysis of overlay for an urban road in bangaloreeSAT Journals
Abstract
Pavements are subjected to severe condition of stresses and weathering effects from the day they are constructed and opened to traffic
mainly due to its fatigue behavior and environmental effects. Therefore, pavement rehabilitation is one of the most important
components of entire road systems. This paper highlights the design of concrete pavement with added mono fibers like polypropylene,
steel and hybrid fibres for a widened portion of existing concrete pavement and various overlay alternatives for an existing
bituminous pavement in an urban road in Bangalore. Along with this, Life cycle cost analyses at these sections are done by Net
Present Value (NPV) method to identify the most feasible option. The results show that though the initial cost of construction of
concrete overlay is high, over a period of time it prove to be better than the bituminous overlay considering the whole life cycle cost.
The economic analysis also indicates that, out of the three fibre options, hybrid reinforced concrete would be economical without
compromising the performance of the pavement.
Keywords: - Fatigue, Life cycle cost analysis, Net Present Value method, Overlay, Rehabilitation
Laboratory studies of dense bituminous mixes ii with reclaimed asphalt materialseSAT Journals
Abstract
The issue of growing demand on our nation’s roadways over that past couple of decades, decreasing budgetary funds, and the need to
provide a safe, efficient, and cost effective roadway system has led to a dramatic increase in the need to rehabilitate our existing
pavements and the issue of building sustainable road infrastructure in India. With these emergency of the mentioned needs and this
are today’s burning issue and has become the purpose of the study.
In the present study, the samples of existing bituminous layer materials were collected from NH-48(Devahalli to Hassan) site.The
mixtures were designed by Marshall Method as per Asphalt institute (MS-II) at 20% and 30% Reclaimed Asphalt Pavement (RAP).
RAP material was blended with virgin aggregate such that all specimens tested for the, Dense Bituminous Macadam-II (DBM-II)
gradation as per Ministry of Roads, Transport, and Highways (MoRT&H) and cost analysis were carried out to know the economics.
Laboratory results and analysis showed the use of recycled materials showed significant variability in Marshall Stability, and the
variability increased with the increase in RAP content. The saving can be realized from utilization of recycled materials as per the
methodology, the reduction in the total cost is 19%, 30%, comparing with the virgin mixes.
Keywords: Reclaimed Asphalt Pavement, Marshall Stability, MS-II, Dense Bituminous Macadam-II
Laboratory investigation of expansive soil stabilized with natural inorganic ...eSAT Journals
Abstract
Soil stabilization has proven to be one of the oldest techniques to improve the soil properties. Literature review conducted revealed
that uses of natural inorganic stabilizers are found to be one of the best options for soil stabilization. In this regard an attempt has
been made to evaluate the influence of RBI-81 stabilizer on properties of black cotton soil through laboratory investigations. Black
cotton soil with varying percentages of RBI-81 viz., 0, 0.5, 1, 1.5, 2, and 2.5 percent were studied for moisture density relationships
and strength behaviour of soils. Also the effect of curing period was evaluated as literature review clearly emphasized the strength
gain of soils stabilized with RBI-81 over a period of time. The results obtained shows that the unconfined compressive strength of
specimens treated with RBI-81 increased approximately by 250% for a curing period of 28 days as compared to virgin soil. Further
the CBR value improved approximately by 400%. The studies indicated an increasing trend for soil strength behaviour with
increasing percentage of RBI-81 suggesting its potential applications in soil stabilization.
Influence of reinforcement on the behavior of hollow concrete block masonry p...eSAT Journals
Abstract
Reinforced masonry was developed to exploit the strength potential of masonry and to solve its lack of tensile strength. Experimental
and analytical studies have been carried out to investigate the effect of reinforcement on the behavior of hollow concrete block
masonry prisms under compression and to predict ultimate failure compressive strength. In the numerical program, three dimensional
non-linear finite elements (FE) model based on the micro-modeling approach is developed for both unreinforced and reinforced
masonry prisms using ANSYS (14.5). The proposed FE model uses multi-linear stress-strain relationships to model the non-linear
behavior of hollow concrete block, mortar, and grout. Willam-Warnke’s five parameter failure theory has been adopted to model the
failure of masonry materials. The comparison of the numerical and experimental results indicates that the FE models can successfully
capture the highly nonlinear behavior of the physical specimens and accurately predict their strength and failure mechanisms.
Keywords: Structural masonry, Hollow concrete block prism, grout, Compression failure, Finite element method,
Numerical modeling.
Influence of compaction energy on soil stabilized with chemical stabilizereSAT Journals
Abstract
Increase in traffic along with heavier magnitude of wheel loads cause rapid deterioration in pavements. There is a need to improve
density, strength of soil subgrade and other pavement layers. In this study an attempt is made to improve the properties of locally
available loamy soil using twin approaches viz., i) increasing the compaction of soil and ii) treating the soil with chemical stabilizer.
Laboratory studies are carried out on both untreated and treated soil samples compacted by different compaction efforts. Studies
show that increase in compaction effort results in increase in density of soil. However in soil treated with chemical stabilizer, rate of
increase in density is not significant. The soil treated with chemical stabilizer exhibits improvement in both strength and performance
properties.
Keywords: compaction, density, subgradestabilization, resilient modulus
Geographical information system (gis) for water resources managementeSAT Journals
Abstract
Water resources projects are inherited with overlapping and at times conflicting objectives. These projects are often of varied sizes
ranging from major projects with command areas of millions of hectares to very small projects implemented at the local level. Thus,
in all these projects there is seldom proper coordination which is essential for ensuring collective sustainability.
Integrated watershed development and management is the accepted answer but in turn requires a comprehensive framework that can
enable planning process involving all the stakeholders at different levels and scales is compulsory. Such a unified hydrological
framework is essential to evaluate the cause and effect of all the proposed actions within the drainage basins.
The present paper describes a hydrological framework developed in the form of a Hydrologic Information System (HIS) which is
intended to meet the specific information needs of the various line departments of a typical State connected with water related aspects.
The HIS consist of a hydrologic information database coupled with tools for collating primary and secondary data and tools for
analyzing and visualizing the data and information. The HIS also incorporates hydrological model base for indirect assessment of
various entities of water balance in space and time. The framework would be maintained and updated to reflect fully the most
accurate ground truth data and the infrastructure requirements for planning and management.
Keywords: Hydrological Information System (HIS); WebGIS; Data Model; Web Mapping Services
Forest type mapping of bidar forest division, karnataka using geoinformatics ...eSAT Journals
Abstract
The study demonstrate the potentiality of satellite remote sensing technique for the generation of baseline information on forest types
including tree plantation details in Bidar forest division, Karnataka covering an area of 5814.60Sq.Kms. The Total Area of Bidar
forest division is 5814Sq.Kms analysis of the satellite data in the study area reveals that about 84% of the total area is Covered by
crop land, 1.778% of the area is covered by dry deciduous forest, 1.38 % of mixed plantation, which is very threatening to the
environmental stability of the forest, future plantation site has been mapped. With the use of latest Geo-informatics technology proper
and exact condition of the trees can be observed and necessary precautions can be taken for future plantation works in an appropriate
manner
Keywords:-RS, GIS, GPS, Forest Type, Tree Plantation
Factors influencing compressive strength of geopolymer concreteeSAT Journals
Abstract
To study effects of several factors on the properties of fly ash based geopolymer concrete on the compressive strength and also the
cost comparison with the normal concrete. The test variables were molarities of sodium hydroxide(NaOH) 8M,14M and 16M, ratio of
NaOH to sodium silicate (Na2SiO3) 1, 1.5, 2 and 2.5, alkaline liquid to fly ash ratio 0.35 and 0.40 and replacement of water in
Na2SiO3 solution by 10%, 20% and 30% were used in the present study. The test results indicated that the highest compressive
strength 54 MPa was observed for 16M of NaOH, ratio of NaOH to Na2SiO3 2.5 and alkaline liquid to fly ash ratio of 0.35. Lowest
compressive strength of 27 MPa was observed for 8M of NaOH, ratio of NaOH to Na2SiO3 is 1 and alkaline liquid to fly ash ratio of
0.40. Alkaline liquid to fly ash ratio of 0.35, water replacement of 10% and 30% for 8 and 16 molarity of NaOH and has resulted in
compressive strength of 36 MPa and 20 MPa respectively. Superplasticiser dosage of 2 % by weight of fly ash has given higher
strength in all cases.
Keywords: compressive strength, alkaline liquid, fly ash
Experimental investigation on circular hollow steel columns in filled with li...eSAT Journals
Abstract
Composite Circular hollow Steel tubes with and without GFRP infill for three different grades of Light weight concrete are tested for
ultimate load capacity and axial shortening , under Cyclic loading. Steel tubes are compared for different lengths, cross sections and
thickness. Specimens were tested separately after adopting Taguchi’s L9 (Latin Squares) Orthogonal array in order to save the initial
experimental cost on number of specimens and experimental duration. Analysis was carried out using ANN (Artificial Neural
Network) technique with the assistance of Mini Tab- a statistical soft tool. Comparison for predicted, experimental & ANN output is
obtained from linear regression plots. From this research study, it can be concluded that *Cross sectional area of steel tube has most
significant effect on ultimate load carrying capacity, *as length of steel tube increased- load carrying capacity decreased & *ANN
modeling predicted acceptable results. Thus ANN tool can be utilized for predicting ultimate load carrying capacity for composite
columns.
Keywords: Light weight concrete, GFRP, Artificial Neural Network, Linear Regression, Back propagation, orthogonal
Array, Latin Squares
Experimental behavior of circular hsscfrc filled steel tubular columns under ...eSAT Journals
Abstract
This paper presents an outlook on experimental behavior and a comparison with predicted formula on the behaviour of circular
concentrically loaded self-consolidating fibre reinforced concrete filled steel tube columns (HSSCFRC). Forty-five specimens were
tested. The main parameters varied in the tests are: (1) percentage of fiber (2) tube diameter or width to wall thickness ratio (D/t
from 15 to 25) (3) L/d ratio from 2.97 to 7.04 the results from these predictions were compared with the experimental data. The
experimental results) were also validated in this study.
Keywords: Self-compacting concrete; Concrete-filled steel tube; axial load behavior; Ultimate capacity.
Evaluation of punching shear in flat slabseSAT Journals
Abstract
Flat-slab construction has been widely used in construction today because of many advantages that it offers. The basic philosophy in
the design of flat slab is to consider only gravity forces; this method ignores the effect of punching shear due to unbalanced moments
at the slab column junction which is critical. An attempt has been made to generate generalized design sheets which accounts both
punching shear due to gravity loads and unbalanced moments for cases (a) interior column; (b) edge column (bending perpendicular
to shorter edge); (c) edge column (bending parallel to shorter edge); (d) corner column. These design sheets are prepared as per
codal provisions of IS 456-2000. These design sheets will be helpful in calculating the shear reinforcement to be provided at the
critical section which is ignored in many design offices. Apart from its usefulness in evaluating punching shear and the necessary
shear reinforcement, the design sheets developed will enable the designer to fix the depth of flat slab during the initial phase of the
design.
Keywords: Flat slabs, punching shear, unbalanced moment.
Evaluation of performance of intake tower dam for recent earthquake in indiaeSAT Journals
Abstract
Intake towers are typically tall, hollow, reinforced concrete structures and form entrance to reservoir outlet works. A parametric
study on dynamic behavior of circular cylindrical towers can be carried out to study the effect of depth of submergence, wall thickness
and slenderness ratio, and also effect on tower considering dynamic analysis for time history function of different soil condition and
by Goyal and Chopra accounting interaction effects of added hydrodynamic mass of surrounding and inside water in intake tower of
dam
Key words: Hydrodynamic mass, Depth of submergence, Reservoir, Time history analysis,
Evaluation of operational efficiency of urban road network using travel time ...eSAT Journals
Abstract
Efficiency of the road network system is analyzed by travel time reliability measures. The study overlooks on an important measure of
travel time reliability and prioritizing Tiruchirappalli road network. Traffic volume and travel time were collected using license plate
matching method. Travel time measures were estimated from average travel time and 95th travel time. Effect of non-motorized vehicle
on efficiency of road system was evaluated. Relation between buffer time index and traffic volume was created. Travel time model has
been developed and travel time measure was validated. Then service quality of road sections in network were graded based on
travel time reliability measures.
Keywords: Buffer Time Index (BTI); Average Travel Time (ATT); Travel Time Reliability (TTR); Buffer Time (BT).
Estimation of surface runoff in nallur amanikere watershed using scs cn methodeSAT Journals
Abstract
The development of watershed aims at productive utilization of all the available natural resources in the entire area extending from
ridge line to stream outlet. The per capita availability of land for cultivation has been decreasing over the years. Therefore, water and
the related land resources must be developed, utilized and managed in an integrated and comprehensive manner. Remote sensing and
GIS techniques are being increasingly used for planning, management and development of natural resources. The study area, Nallur
Amanikere watershed geographically lies between 110 38’ and 110 52’ N latitude and 760 30’ and 760 50’ E longitude with an area of
415.68 Sq. km. The thematic layers such as land use/land cover and soil maps were derived from remotely sensed data and overlayed
through ArcGIS software to assign the curve number on polygon wise. The daily rainfall data of six rain gauge stations in and around
the watershed (2001-2011) was used to estimate the daily runoff from the watershed using Soil Conservation Service - Curve Number
(SCS-CN) method. The runoff estimated from the SCS-CN model was then used to know the variation of runoff potential with different
land use/land cover and with different soil conditions.
Keywords: Watershed, Nallur watershed, Surface runoff, Rainfall-Runoff, SCS-CN, Remote Sensing, GIS.
Estimation of morphometric parameters and runoff using rs & gis techniqueseSAT Journals
Abstract
Land and water are the two vital natural resources, the optimal management of these resources with minimum adverse environmental
impact are essential not only for sustainable development but also for human survival. Satellite remote sensing with geographic
information system has a pragmatic approach to map and generate spatial input layers of predicting response behavior and yield of
watershed. Hence, in the present study an attempt has been made to understand the hydrological process of the catchment at the
watershed level by drawing the inferences from moprhometric analysis and runoff. The study area chosen for the present study is
Yagachi catchment situated in Chickamaglur and Hassan district lies geographically at a longitude 75⁰52’08.77”E and
13⁰10’50.77”N latitude. It covers an area of 559.493 Sq.km. Morphometric analysis is carried out to estimate morphometric
parameters at Micro-watershed to understand the hydrological response of the catchment at the Micro-watershed level. Daily runoff
is estimated using USDA SCS curve number model for a period of 10 years from 2001 to 2010. The rainfall runoff relationship of the
study shows there is a positive correlation.
Keywords: morphometric analysis, runoff, remote sensing and GIS, SCS - method
-
Effect of variation of plastic hinge length on the results of non linear anal...eSAT Journals
Abstract The nonlinear Static procedure also well known as pushover analysis is method where in monotonically increasing loads are applied to the structure till the structure is unable to resist any further load. It is a popular tool for seismic performance evaluation of existing and new structures. In literature lot of research has been carried out on conventional pushover analysis and after knowing deficiency efforts have been made to improve it. But actual test results to verify the analytically obtained pushover results are rarely available. It has been found that some amount of variation is always expected to exist in seismic demand prediction of pushover analysis. Initial study is carried out by considering user defined hinge properties and default hinge length. Attempt is being made to assess the variation of pushover analysis results by considering user defined hinge properties and various hinge length formulations available in literature and results compared with experimentally obtained results based on test carried out on a G+2 storied RCC framed structure. For the present study two geometric models viz bare frame and rigid frame model is considered and it is found that the results of pushover analysis are very sensitive to geometric model and hinge length adopted. Keywords: Pushover analysis, Base shear, Displacement, hinge length, moment curvature analysis
Effect of use of recycled materials on indirect tensile strength of asphalt c...eSAT Journals
Abstract
Depletion of natural resources and aggregate quarries for the road construction is a serious problem to procure materials. Hence
recycling or reuse of material is beneficial. On emphasizing development in sustainable construction in the present era, recycling of
asphalt pavements is one of the effective and proven rehabilitation processes. For the laboratory investigations reclaimed asphalt
pavement (RAP) from NH-4 and crumb rubber modified binder (CRMB-55) was used. Foundry waste was used as a replacement to
conventional filler. Laboratory tests were conducted on asphalt concrete mixes with 30, 40, 50, and 60 percent replacement with RAP.
These test results were compared with conventional mixes and asphalt concrete mixes with complete binder extracted RAP
aggregates. Mix design was carried out by Marshall Method. The Marshall Tests indicated highest stability values for asphalt
concrete (AC) mixes with 60% RAP. The optimum binder content (OBC) decreased with increased in RAP in AC mixes. The Indirect
Tensile Strength (ITS) for AC mixes with RAP also was found to be higher when compared to conventional AC mixes at 300C.
Keywords: Reclaimed asphalt pavement, Foundry waste, Recycling, Marshall Stability, Indirect tensile strength.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
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
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
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
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Automatic identification and classification of microaneurysms for detection of diabetic retinopathy
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 464
AUTOMATIC IDENTIFICATION AND CLASSIFICATION OF
MICROANEURYSMS FOR DETECTION OF DIABETIC RETINOPATHY
Gowthaman R1
1
Coimbatore, Tamilnadu, India
Abstract
Diabetic Retinopathy is major cause for visual loss and visual impaired vision worldwide. A proper detection and treatment of this
disease in needed in time. Microaneurysms detection is difficult process because they appeared as a first sign of diabetic retinopathy
disease. In past few years, many approaches raised for the identification and detection of this diseases using some features extraction
techniques, mathematical algorithms and artificial neural network classifiers which lacks in some drawbacks in preprocessing,
extraction of appropriate features, blood vessels extraction and in chosing classification techniques. This paper is developed to
prefectly detect the candidate regions by using Gabor filter bank and separation of blood vessels from the retina image. Then for each
candidate region different feature vectors are extracted. These features are given to multi class classifier for training and testing.
Performance of this proposed work is evaluated with performance metrics such as accuracy, sensitivity, specificity and execution time
and proved as a successful method for automatic early detection of diabetic retinopathy.
Keywords: Microaneurysms (MAs), Diabetic Retinopathy (DR), Support Vector Machine (SVM), Extreme Learning
Machine (ELM).
----------------------------------------------------------------------***-----------------------------------------------------------------------
1. INTRODUCTION
Eye is a very essential and critical organ of the human body
which only gives vision. It is a complex organ next to human
brain. There are huge eye diseases spreading nowadays due to
improper care. Among those diseases Diabetic Retinopathy
(DR) is severe and wide spreading diseases. It has been
identified as one of the cause for blindness or vision
impairment.
According to recent estimates, approximately 285 million
people worldwide (6.6%) in the 20–79 year age group have
diabetes in 2010 and by 2030, 438 million people (7.8%) of
the adult population, is expected to have diabetes [1]. And one
noticeable thing is India at first position of 50.8 million people
affected by diabetics by the survey taken on 2010 [2].
Thus there is a much urged need to control and early detection
of this disease. Early detection and management of risk factors
responsible for diabetic retinopathy could postpone
development of diabetic retinopathy or control its progression.
Microaneurysms (MAs) are among the earliest clinical signs
of diabetic retinopathy [4], and [5]. They arise due to high
sugar levels in the blood. MAs are of small, almost round and
red in color.The next sign of DR is hemorrhages which are
also referred to as dot or blot hemorrhages.When the wall of
thin vessels or MAs is sufficiently weakened, it may rupture
and give rise to a hemorrhages. Dot hemorrhages appear as
bright small red dots and blot hemorrhages are larger red
lesions.Sometimes dot hemorrhages and Mas are considered
as a single red lesion class known as HMAs [3].
The initial detection of this disease can be done manually. But
it is very tough and waste of time and not sure about the
accurate detection. This urged to develop automated
techniques which are probably accurate and more number of
images can be processed together. For this huge techniques are
proposed by many authors for early detection of DR in Image
Processing.
The retinal image of the patients affected by this disease is
captured intially. Those images are subjected first for pre
processing because they may be in low resolution and noisy.
Then from pre processed image the features are extracted for
the identification. Feature extraction is an important technique
in which appropriate and effective features must be extracted
which only helps for perfect identification. From those
features they are finally given for the identification and
classification technique.
This Computer aided diagnostic systems for eye diseases use
digital retina images which are an essential mean to document
and diagnose various eye diseases in clinics.Such a system
should be able to distinguish between affected retina images
and normal retina images.This will significantly reduce the
work load for the ophthalmologists as they have to examine
only those images diagnosed by the system as potential
anomalies containing affected retina [3].
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 465
The paper is organized as follows: section 2 gives the related
works; section 3 explains detailed about the proposed work
which involves pre processing of the retina image; feature
extraction, identification and classification; section 4 presents
the experimental results and finally section 5 provides
conclusion and future extension of this work.
2. RELATED WORK
Automated microaneurysm detection was first attempted in the
early 1980s, and was on fluorescein angiographic images. Lay
et al [11] illustrated a morphological approach to MA
detection. Image resolution and dynamic range were low
(256 × 256 pixels with 100 gray levels); Eventhough, by
means of imaging microscope to digitize film negatives, the
digital image covered a small field-of-view of the macula so
that MAs were well resolved. As negatives were used, the
MAs appeared as dark dots, thus as localized minima in the
intensity image. A top hat transform was applied to identify
the local minima. The automated MA detector achieved nearly
60% sensitivity with 4.2 false detections per image for
detecting and locating MAs.
Author Hipwell et al [12] modified past microaneurysm
detector system. They achieved 43% sensitivity at 0.11 false
positives per image, the lower sensitivity reflecting the greater
difficulty to visualise microaneurysms without fluorescein
angiography. Using the presence of one or more
microaneurysms in an image to indicate the presence of
diabetic retinopathy their system achieved a disease detection
sensitivity of 85% with a specificity of 76%. They
demonstrated in a controlled clinical trial that their method
could be useful in a screening situation to identify 50% of
normal retina.
Low-resolution colour retinal images was investigated by
Yang et al [13], but removed the matched-filtering stage and
replaced it with an extra dilation applied after the opening
image reconstruction (within the top hat transform) using a
3 × 3 structuring element. They achieved a microaneurysm
detection sensitivity of 85% with a specificity of 90% but used
a testing procedure that is quite disputable. They also report a
preliminary sensitivity of 90% to detect at least one true
microaneurysm in a training set of 46 normal and diseased
images of various qualities
Fleming et al [14] proposed a local contrast normalization
based method. They used watershed transform to detect MAs
by distinguishing between MAs and other dots present on
retina. The reported sensitivity and specificity were 85.4% and
83.1%, respectively. An online competition for MAs detection
with the name of Retinopath Online Challenge (ROC) is
introduced by the University of Iowa and ROC organizers
[15]. The purpose is to improve the quality of computer aided
and automated diagnoses of DR.The results of first
international competition were reported in [16].
3. PROPOSED WORK
Many methods are followed for the detection of diabetic
retinopathy. And some methods followed by identification and
classification of MAs for detection of diabetic retinopathy.
Here this proposed work also follows by detection of MAs for
identification of diabetic retinopathy. This work is composed
of mainly three main processes of preprocessing, feature
extraction and classification.
The block diagram of this work is shown in figure 1 which
gives complete details of this whole process. The
preprocessing phase removes noises, enhances the image and
extracts candidate regions for MAs. Then the feature
extraction phase extracts features in different properties for
each candidate lesions and followed by classification phase
which identifies MAs and Non MAs separately. To prove this
works efficiency results are displayed finally.
3.1 Pre Processing
Pre processing is an initial and vital process in any image
processing based system.This process makes the image
prepared for the further extraction of features and for
classification.
Here, the captured retina images are at low light and there is
maximum chance of noise to affect images. In this pre
processing the images are denoised and enhanced.
At first they are given for noise removal technique. In medical
image processing noise removal is crucial step because this
may give the chance to affect the result widely. There are
various noise removal techniques available in image
processing.
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 466
Fig-1: Block Diagram of the Proposed Work
Fig 2.1: Original Input
Retinal Image
Fig 2.2: Smothing by
Morpological Operation
INPUT RETINA
IMAGE
PRE PROCESSING
CANDIDATE
REGION
EXTRACTION
GABOR FILTER IN
DIFF
ORRIENTATION
BLOOD VESSEL
DETECTION &
SEPERATION
NOISE
REMOVAL
CONTRAST
ENHANCEMENT
FEATURES EXTRACTION
AREA
ECCENTRICIT
Y
ASPECT RATIO
MEAN
STANDARD
DEVIATION
ENTROPY
ENERGY
HOMOGENEIT
Y
NON-MAs
MAs
CLASSIFICATION
TRAINING
CLASSIFIER
TESTING
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 467
Fig 2.3: Obtained Green
Channel Image
Fig 2.4: Contrast Enhanced
Image
Fig -2: Preprocessing Output
Here Gaussian filter is chosen among them because it is
efficient in removal of noise and for smoothing the image.
Then after removal of noise the morphological operations of
opening and closing are done with the Structuring Element
(SE) which is defined in equation (3.1 and 3.2).
𝑂𝑝𝑒𝑛𝑖𝑛𝑔: 𝛾 𝑠𝐵
𝑓 = 𝛿(𝑠𝐵)
∈ 𝑠𝐵
𝑓 (3.1)
𝐶𝑙𝑜𝑠𝑖𝑛𝑔: ∅ 𝑠𝐵
𝑓 = 𝑐(𝑠𝐵)
𝛿 𝑠𝐵
𝑓 (3.2)
Where sB is taken as structuring element B of size s. f is the
gray level image. This gives smooth retina region which
contains dark red lesions and Optical Vessels. But the lesions
are not improved to maximum contrast.
MAs are dark red dot they are highly visual in green plane.
Thus from the smoothed image only green channel image is
extracted from the RGB Image which used for further
processing.
To improve the lesions for easy detection adaptive contrast
enhancement transformation is done.This is performed by
w × w sliding window with assumption that w is large enough
to contain a statistically representative distribution of the local
variation of lesions [6].
𝑔 = 255
ΨW f − ΨW fmin
𝛹 𝑊 𝑓𝑚𝑎𝑥 − 𝛹 𝑊 𝑓𝑚𝑖𝑛
(3.3)
Where the sigmoidal function for a window defined as
Ψ 𝑊 𝑓 = 1 + exp
𝑚 𝑊 − 𝑓
𝜎 𝑊
−1
(3.4)
While fmax and fmin are the maximum and minimum intensity
of the smooth green channel image, respectively. mW and σW
is the mean and variance of intensity values within the
window.
3.1.1 Candidate Region Extraction
Candidate region (lesion) is a small circular object which is
dark red dot and patchs in retinal image. By our naked eye it
can be able to identify but they varies based on its texture,
contrast and blood vessels in the image makes difficult to
identify it clearly. In this phase they are extracted by gabor
filter and blood vessels are segmented to extract it without any
difficulty.
3.1.1.1 Gabor Filter
The contrast enhanced image is given for Gabor filter banks for
enhancement of lesions.Gabor filters are Famous due to their
fine frequency tuning and orientations electiveness. They are
appropriate for texture representation and discrimination [7].
Gabor filter is represented by a Gaussian kernel function which
can model a wide range of shapes depending upon the values
of its parameters [7] which is shown in equation (3.5). This
property makes them suitable for MAs and dot hemorrhage
detection.
𝐺 𝑥, 𝑦, 𝜎, Ω, 𝜃, 𝑟 =
1
𝜋𝑟𝜎
𝑒
−
1
2
[(
𝑑1
𝜎
)2+ (
𝑑2
𝜎
)2Ι
(𝑑1(cos Ω
+ 𝑙 sin Ω)) (3.5)
Where σ, Ω and 𝑟 are the standard deviations of gaussian,
spatial frequency and aspect ratio respectively, y is the
orientation of filter and d1 = x cosθ + y sinθ and d2 =
−x sinθ + y cosθ. The contrast enhanced image g is convolved
with Gabor filter G centered at location(s, t) to generate Gabor
filter response g for selected values of s, O and y is given in the
following equation (3.6) [7].
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 468
𝛾 𝜎, Ω, 𝜃 = 𝑔 𝑥, 𝑦 𝐺 𝑠 − 𝑥, 𝑡 − 𝑦, 𝜎, Ω, 𝜃, 𝑟
𝑦𝑥
3.6
To obtaine maximum scale and frequecy values, the maximum
Gabor filter bank response Mγ(σ, Ω) is computed by the
equation for θ spanning 45°
, 90°
, 135°
and 180°
defined in
equation (3.7).
𝑀𝛾 𝜎, 𝛺 = max 𝛾 𝜎, 𝛺, 𝜃 3.7
From this binary candidate regions for MAs and H are
extracted by applying a small threshold valueT1. These regions
contain actual lesion region, false lesion regions and blood
vessels also. From this blood vessels and false lesion must be
removed before given for further process.
Fig -3.1: Orientation at
45 degree
Fig -3.2: Orientation at
90 degree
Fig -3.3: Orientation at
135 degree
Fig -3.4: Orientation at
180 degree
Fig -3: Gabor Filter Response at Different Orientations
Fig -4: Segmented Red Lesions Containing Spurious Region
3.1.2 Detection and Removal of Blood Vessels
Blood vessels are important structure in retinal images. It
contains enough information for the localization of some
anchor points and it maps the whole retina.
For the diagnosis or evaluation of ocular or systemic diseases,
examination of retinal blood vessels is important. It offers
much information however for easy detection of pathological
lesions like exudate or microaneurysms it must be excluded.
Blood vessel segmentation is crucial process they are
segmented basically based on mainly three approaches:
thresholding method, tracking method and machine trained
classifier [8]. Here threshold based blood vessel segmentation
is followed.
It composed of four steps, matched filtering, entropy based
thresholding, length filtering, and vascular intersection
detection proposed in [9].
3.1.2.1 Matched Filtering
To detect the piecewise linear segments of blood vessels in
retinal images, matched filtering is performed. It is designed in
two dimensions for the retinal images which enhance the blood
vessels. A prototype matched filter is expressed as in equation
(3.8).
f x, y = − exp
−x2
2σ2
, for y ≤
L
2
(3.8)
Where L is taken as length of the segment for which the vessel
is assumed to have a fixed orientation. Here the direction of the
vessel is supposed to be aligned along the y-axis. Because a
vessel may be oriented at any angles, the kernel needs to be
rotated for all possible angles. A set of twelve 16 × 15 pixel
kernels is applied by convolving to a retina image and at each
pixel only the maximum of their responses is retained.
3.1.2.2 Local Entroy Thresholding
Here the blood vessels segments are extracted from the retinal
image. To extract vessels a local entropy based thresholding
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 469
technique is implemented here which helps in preserving the
spatial structures in the thresholded/binarized image [10].
The co-occurrence matric of the image F is an P × Q
dimensional matrix T = tij P × Q which gives an initiative
about the transition of intensities between adjacent pixels,
indicating spatial structural information of an image.
Depending upon the ways in which the gray level i follows
gray level j, different definitions of cooccurrence matrix are
possible. Here, we made the co-occurrence matrix asymmetric
by considering the horizontally right and vertically lower
transitions.
Fig -5: Segmented Blood
Vessels
Fig -6: Candidate lesions
after blood Vessel
Subtraction
Fig -7: Enhanced
Candidate Lesion
By performing the above four operations bloods vessels are
accurately detected and blood vascular patterns are segmented
from Gabor filtered image.
3.2 Feature Vector Construction
Features are essential for any classification or analysis in
image processing. There are several types of features which
can be extracted from the images each gives its identical
informations about the image. Here MAs region supposed with
properties such as shape, color and size which appears as a
dark red colored circle shape. To identify MA and non-MA
region feature vectors are formed for each candidate regions.
Each Candidate region in the image has their identical feature
values which can be taken as potential MA region. If the image
f contains N number of candidate regions, then f =
f1, f2, … . , fN . Therefore for the each candidate region contains
k features (fi = k1, k2, … . . , kN , where i = 1, 2, 3… N).
Area is the total number of pixels in candidate region.
Eccentricity is the ratio of the distance between foci
of ellipse and its major axis length and it is equal to 0
for a circular region.
Aspect Ratio is the ratio of major axis length to
minor axis length of the candidate region.
Mean and Standard Deviation value of all green
channel pixels within the candidate region.
Entropy value of all pixels in the square region
including candidate region pixels and its neighboring
pixels.
Energy value of all pixels in the square region
including candidate region pixels and its neighboring
pixels.
Homogeneity value of all pixels in the square region
including candidate region pixels and its neighboring
pixels.
3.3 Classification
The classification is the final process which classifies the result
(I.e, Normal, Abnormal etc). There are various classifiers used
in literature which divides into two classes majorly called as
dichotomies and some classifies into multi classes (e.g.
decision trees [17], feedforward neural networks). Here SVM
and ELM classifier is taken to compare the effeciency of
proposed work.
3.3.1 SVM Classification
Support Vector Machine (SVM) is a useful method for
classification of high dimensional problems which suits for
only 2 class classification. For multi class classification (K)
the classifier has to be trained typically placed in parallel and
each one of them is trained to separate one class from the K - 1
others. This way of decomposing a general classification
problem into dichotomies is known as a one-per-class
decomposition, and is independent of the learning method used
to train the classifiers. This process is little difficult and lacks
in time consumption.
Thus a multi class classification of SVM is chosen here for the
classification from Cody Neuburger [18]. In traditional SVM;
the structure of trained SVM is formed in a 1 × 1 structure.
And from that structure the SVM classifier classifies into two
classes of 1’s and 0’s. The user can take on their own choose
of 1’s as normal or abnormal vise versa based on the training
given.
Here in this multi class SVM classification, the candidate
region features which are extracted for the dataset previously
are divided into two segments for training segment and testing
segment.
7. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 470
The number of training segment (t) and its features (trset) are
given for the svmtrain MATLAB function.
svmStruct = svmtrain trset, bgroup
t
1
(3.9)
Where, bgroup is the binarized group in which 1 is the current
class and 0 is all other classes. This svmStruct contains t
number of structures. For the classification of testing segments,
the svmStruct is utilized with testing segments features in
svmclassify MATLAB function same as the above equation. It
is the Strong classification performance with simple
implementation.
3.3.2 ELM Classification:
A new learning algorithm for Single Hidden Layer Feed-
Forward Networks (SLFNs), called Extreme Learning Machine
(ELM), has been proposed by Huang et al. [21, 22], which
helps in solving regression and classification problems. It can
also used to reach good solutions analytically, and its learning
speed is extremely faster than other traditional methods.
Here, the input weights and biases are determined randomly
and they are not updated during training iterations [22]. The
activation function like sine, gaussian, sigmoidal etc., can be
chosen for hidden neuron layer and linear activation functions
for the output neurons. It is a Multi-class classification where
number of output neurons will be automatically set equal to
number of classes. (For example, if there are 7 classes in all,
there will have 7 output neurons; neuron 5 has the highest
output means input belongs to 5-th class).
The output weights are obtained by using norm Least Squares
(LS) and pseudo inverse of a linear system.
For a given N arbitrary input-output relation (xi, ti), where
xi = [xi1, xj2, … , xin ]T
∈ Rn
and ti = [ti1, tj2, … , tin ]T
∈ Rm
,
a single layered network with N neurons in hidden layer and a
given activation function g(x) is modeled as
βig (wi
N
i=1
. xj + bi) = oj, j = 1, … , N (3.10)
Where wi = [ωi1, ωi2, … , ωin ]T
shows the weight of the ith
hidden neuron, and βi = [βi1, βi2, … , βin ]T
shows the weight of
the ith
hidden neuron to the output neurons, and bi is the bias
of the ith
hidden neuron.
The SLFNs which is defined in Eq. (3.10) can be approximated
with zero error [22]. In other words, the Eq. (3.10) can be re-
written in the following form:
Hβ = T (3.11)
The description of equation (3.11) is explained in [23]. After
calculation of H, the output weights are determined by using
equation (3.12).
β = H∗
× T (3.12)
4. EXPERIMENTAL RESULTS
To evaluate the proposed work for the identification of diabetic
retinopathy using detection of MAs is performed using the
retina images taken from publicly available databases such as
DRIVE [19], and DIARETDB1 [20] in MATLAB
environment. The results obtained from the SVM and ELM
classification is compared and discussed here.
Table -1: Dataset Specification
Dataset
Total
Images
Training
Segment
Testing
Segment
DRIVE 40 20 20
DIARETDB1 89 45 44
The datasets specified in table 1 is taken for the evaluation
because these dataset images contain varieties of DR lesions
which suits well for evaluation. The results are taken from both
the dataset which contains both normal and abnormal (i.e
contains lesions) images that are equally divided for both
training and testing.
Initially the training set features are given to the svm classifier
which produces the svm structure that is saved separately.
Then testing set is given to the system which follows same
procedure as training set till feature extraction. Then in
classification, the structured svm which is saved before is
loaded and based on this structure, the classifier classifies the
testing images individually. The svm classifier produces the
result based on its feature values that match approximately
with the feature values from the trained images. (For example,
we take one single test image features, if its features match in
svm classifier with the features of 5 in the trained image, then
the result will be 5).
As SVM classification, in ELM training along with its Number
of Hidden Neurons, Activation Function, Type- 0 for
regression; 1 for (both binary and multi-classes) classification
is given as input for the ELM training. It produces output of
ELM model.
This ELM model is given for the testing along with the testing
features of MAs to the classifier. That model helps to predict
correctly the testing image features candidate lesions as MAs,
Non-MAs accurately.
8. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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4.1 Performance Metrics
For the evaluation of this proposed work the performance
metrics such as Sensitivity (Sen), Specificity (Spec), Positive
Predictive Value (PPV) and Accuracy (Acc) is taken.These
parameters are calculated using the following equations
respectively:
Sen % =
TP
TP + FN
× 100 4.1
Spec % =
TN
TN + FP
× 100 4.2
PPV % =
TP
TP + FP
× 100 4.3
Acc % =
TP + TN
TP + TN + FP + FN
× 100 4.4
Where
TruePositive (TP): MA regions that are correctly
classified by the classifier.
FalsePositive (FP): Non-MA regions that are
wrongly classified as MA regions by the classifier.
TrueNegative (TN): Non-MA regions that are
correctly classified bythe classifier.
FalseNegative (FN): MA regions that are wrongly
classified as non-MA regions by the classifier.
Table -2: Performance assessment between SVM and ELM for
MAs detection for DRIVE
Factors SVM ELM
TP 90 92
TN 140 143
FP 6 3
FN 4 2
Sen (%) 95.74 97.87
Spec (%) 95.89 97.94
PPV (%) 93.75 96.84
Acc (%) 95.83 97.91
The results presented in the table 2 compares the performance
of the classifier SVM and ELM for the detection of MAs. It
clearly reveals that proposed technique with ELM classifier
outperforms when compared with SVM in Sensitivity,
Specificity, PPV and Accuracy for DRIVE dataset.
Table -3: Performance assessment between SVM and ELM for
MAs detection for DIARETDB1
Factors SVM ELM
TP 154 159
TN 230 237
FP 11 4
FN 15 10
Sen (%) 91.12 94.08
Spec (%) 95.43 98.34
PPV (%) 93.33 97.54
Acc (%) 93.65 96.58
Here from the table 3, the performance of the classifier SVM
and ELM for the detection of MAs is compared. It clearly
highlights that proposed technique with ELM classifier
outperforms when compared with SVM in Sensitivity,
Specificity, PPV and Accuracy for DIARETDB1 dataset.
The execution time is also another factor which must be
minimum because any proposed system must satisfy in the
means of both efficiency and time.
Chart-1: Comparison of Execution Time for datasets
The above figure shows the comparison of time taken by the
proposed work to complete its process for the dataset DRIVE
and DIARETDB1. From the chart ELM with the proposed
work archives efficient result in reduced time than SVM for the
both dataset.
Chart-2: Comparison of Accuracy of Proposed Work for the
Classifiers.
0
2
4
6
8
10
12
DRIVE DIARETDB1
ExecutionTime(Sec)
Datasets
SVM
ELM
91
92
93
94
95
96
97
98
99
DRIVE DIARETDB1
Accuracy(%)
Datasets
SVM ELM
9. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 472
The chart 2 shows the comparison graph of accuracy produced
by the proposed work with the classifier SVM and ELM for
DRIVE and DIARETDB1 dataset. This graph shows clearly
that ELM produces maximum accuracy than SVM for the
proposed technique.
5. CONCLUSIONS
Automatic detection of diabetic retinopathy turns out to be
active research because of recent spread of these diseases
largely. The first manifestation of diabetic retinopathy is
microaneurysms which visible as a small reddish dot in human
retinal image. The number of microaneurysms is the essential
parameter used to identify the severity of the diabetic
retinopathy. Detection of diabetic retinopathy at early stage can
reduce the development of diseases significantly. This work is
proposed to detect the diabetic retinopathy at early stage
automatically which helps to cure fully or helps to reduce its
growth. Here patients retinal images are captured intially and
they are stored in a database. After that, they are preprocessed
to reduce the noise and to enhance. Then candidate regions are
extracted from the image along with blood vessels are removed
to effectively extract the candidate regions. And MAs are
enhanced by Gabor filter and from that different feature are
extracted and they are given for the classification. The multi
class classifier SVM and ELM taken as a classifier here
confirms its efficient performance and proved for automatic
detection of diabetic retinopathy in retinal images.
This work can be further extended in future with detecting
Exudates, optic disk, vascular structures by employing hybrid
techniques to produce best results in reduced complexity and
time.
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