Eye disease identification techniques are highly im
portant in the field of ophthalmology. A
vertical Cup-to-Disc Ratio which is the ratio of th
e vertical diameter of the optic cup to that of
the optic disc, of the fundus eye image is one of t
he important signs of glaucoma. This paper
presents an automated method for the extraction of
optic disc and optic cup using Fuzzy C
Means clustering technique. The validity of this ne
w method has been tested on 454 colour
fundus images from
three different publicly available databases DRION,
DIARATDB0 and
DIARETDB1 and, images from an ophthalmologist. The
average success rate of optic disc and
optic cup segmentation is 94.26percentage. The scat
ter plot depicts high positive correlation
between clinical CDR and the CDR obtained using the
new method. The result of the system
seems to be promising and useful for clinical work
Haemorrhage Detection and Classification: A ReviewIJERA Editor
Ā
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
Binary operation based hard exudate detection and fuzzy based classification ...IJECEIAES
Ā
Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
Ā
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE projectās dataset and the success percentage was found
to be 96%.
Retinal image analysis using morphological process and clustering techniquesipij
Ā
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Publishing House
Ā
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
Haemorrhage Detection and Classification: A ReviewIJERA Editor
Ā
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
Binary operation based hard exudate detection and fuzzy based classification ...IJECEIAES
Ā
Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
Ā
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE projectās dataset and the success percentage was found
to be 96%.
Retinal image analysis using morphological process and clustering techniquesipij
Ā
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Publishing House
Ā
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
Proposition of local automatic algorithm for landmark detection in 3D cephalo...journalBEEI
Ā
This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
Vessels delineation in retinal āØimages using COSFIRE filtersNicola Strisciuglio
Ā
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal imagesā, 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
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Ā
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
An Automatic ROI of The Fundus Photography IJECEIAES
Ā
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photographyās of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of
vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using
intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the
damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper
presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc
segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic
cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included
into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR
for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier
to confirm Glaucoma given patient.
Keywords ā IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
The main cause of eye diseases in the working human is Diabetic retinopathy. Eye disease can
be prevented if detects early. The extraction of blood vessels from retinal images is an essential and challenging
task in medical diagnosis and analysis. This paper describes the effective and efficient extraction of blood
vessels from retinal image by using Kirschās templates. The Kirschās edge operators detect the edges using eight
filters, generated by the compass rotation mechanism. The method is used to automatic detection of landmark
features of the fundus, such as the optic disc, fovea and blood vessels.
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.
Research on Iris Region Localization AlgorithmsIJERA Editor
Ā
Iris recognition is a biometric technique that offers premium performance. Iris localization is critical to the success of an iris recognition system, since data that is falsely represented as iris pattern data will corrupt the biometric templates generated, resulting in poor recognition rates. So far different algorithms for iris localization having been proposed. This paper explored four efficient methods for iris localization, out of these three methods of iris localization in circular form and one methods of unwrapping the iris in to a flat bed. Experimental results are reported to demonstrate performance evaluation of every implemented algorithms. Conclusion based on comparisons can provide most significant information for further research. A CASIA and UPOL iris databases of iris images has been used for implementation of iris localization General Term Biometrics,Iris Recognition,Iris Localization
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
Ā
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss.
Glaucoma Screening Test By Segmentation of Optical Disc& Cup Segmentation Usi...IJERA Editor
Ā
Glaucoma is one of the most common causes of blindness and it is becoming even more important considering
the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is
essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We
devised a novel, automated, appearance based glaucoma classification system that does not depend on
segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening
examinations. The proposed segmentation methods have been evaluated in a database of 650 images with optic
disc and optic cup boundaries manually marked by trained professionals. Our expected Experimental results
may be average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
Retinal blood vessel extraction and optical disc removaleSAT Journals
Ā
Abstract Retinal image processing is an important process by which we can detect the blood vessels and this helps us in detecting the DIABETIC RETINOPATHY at a early stage and this is very helpful because the symptoms are not known by anyone unless we have blur eye sight or we get blind. And this mainly occurs in people suffering from high diabetes. So by extracting the blood vessels using the algorithm we can see which blood vessels are actually damaged. So by using the algorithm we can continuously survey the situation and can protect our eye-sight. Keywords: field of view, retinopathy, thresholding, morphology, Otsu's algorithm, MATLAB.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
Ā
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
Proposition of local automatic algorithm for landmark detection in 3D cephalo...journalBEEI
Ā
This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
Vessels delineation in retinal āØimages using COSFIRE filtersNicola Strisciuglio
Ā
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal imagesā, 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
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Ā
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
An Automatic ROI of The Fundus Photography IJECEIAES
Ā
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photographyās of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of
vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using
intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the
damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper
presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc
segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic
cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included
into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR
for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier
to confirm Glaucoma given patient.
Keywords ā IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
The main cause of eye diseases in the working human is Diabetic retinopathy. Eye disease can
be prevented if detects early. The extraction of blood vessels from retinal images is an essential and challenging
task in medical diagnosis and analysis. This paper describes the effective and efficient extraction of blood
vessels from retinal image by using Kirschās templates. The Kirschās edge operators detect the edges using eight
filters, generated by the compass rotation mechanism. The method is used to automatic detection of landmark
features of the fundus, such as the optic disc, fovea and blood vessels.
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.
Research on Iris Region Localization AlgorithmsIJERA Editor
Ā
Iris recognition is a biometric technique that offers premium performance. Iris localization is critical to the success of an iris recognition system, since data that is falsely represented as iris pattern data will corrupt the biometric templates generated, resulting in poor recognition rates. So far different algorithms for iris localization having been proposed. This paper explored four efficient methods for iris localization, out of these three methods of iris localization in circular form and one methods of unwrapping the iris in to a flat bed. Experimental results are reported to demonstrate performance evaluation of every implemented algorithms. Conclusion based on comparisons can provide most significant information for further research. A CASIA and UPOL iris databases of iris images has been used for implementation of iris localization General Term Biometrics,Iris Recognition,Iris Localization
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
Ā
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss.
Glaucoma Screening Test By Segmentation of Optical Disc& Cup Segmentation Usi...IJERA Editor
Ā
Glaucoma is one of the most common causes of blindness and it is becoming even more important considering
the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is
essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We
devised a novel, automated, appearance based glaucoma classification system that does not depend on
segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening
examinations. The proposed segmentation methods have been evaluated in a database of 650 images with optic
disc and optic cup boundaries manually marked by trained professionals. Our expected Experimental results
may be average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
Retinal blood vessel extraction and optical disc removaleSAT Journals
Ā
Abstract Retinal image processing is an important process by which we can detect the blood vessels and this helps us in detecting the DIABETIC RETINOPATHY at a early stage and this is very helpful because the symptoms are not known by anyone unless we have blur eye sight or we get blind. And this mainly occurs in people suffering from high diabetes. So by extracting the blood vessels using the algorithm we can see which blood vessels are actually damaged. So by using the algorithm we can continuously survey the situation and can protect our eye-sight. Keywords: field of view, retinopathy, thresholding, morphology, Otsu's algorithm, MATLAB.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
Ā
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Ā
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
Detection of Glaucoma using Optic Disk and Incremental Cup Segmentation from ...theijes
Ā
Medical researchers, detection of eye disease is very important because it may causes blindness. Glaucoma is one of the diseases that cause blindness. Standard procedure for detection glaucoma is to analysis of optic disk (OD) and cup region in retinal image. In this paper, introduce an automatic OD parameterized technique which is based on segmentation and Incremental Cup segmentation. The incremental cup segmentation method is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is applied to derive a reliable subset of vessel bends called r-bends followed by a local 2-D spline fitting to derive the desired cup boundary. The results are compared with existing methods using different retinal images.
Performance analysis of retinal image blood vessel segmentationacijjournal
Ā
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
Abstract:āThe main cause of eye diseases in the working human is Diabetic retinopathy. Eye disease can
be prevented if detects early. The extraction of blood vessels from retinal images is an essential and challenging
task in medical diagnosis and analysis. This paper describes the effective and efficient extraction of blood
vessels from retinal image by using Kirschās templates. The Kirschās edge operators detect the edges using eight
filters, generated by the compass rotation mechanism. The method is used to automatic detection of landmark
features of the fundus, such as the optic disc, fovea and blood vessels.
Keywords: āDiabetic retinopathy, Retinal image, Oculist
GLAUCOMA is a chronic eye disease that can damage optic nerve. According to WHO It
is the second leading cause of blindness, and is predicted to affect around 80 million people by 2020.
Development of the disease leads to loss of vision, which occurs increasingly over a long period of
time. As the symptoms only occur when the disease is quite advanced so that glaucoma is called the
silent thief of sight. Glaucoma cannot be cured, but its development can be slowed down by
treatment. Therefore, detecting glaucoma in time is critical. However, many glaucoma patients are
unaware of the disease until it has reached its advanced stage. In this paper, some manual and
automatic methods are discussed to detect glaucoma. Manual analysis of the eye is time consuming
and the accuracy of the parameter measurements also varies with different clinicians. To overcome
these problems with manual analysis, the objective of this survey is to introduce a method to
automatically analyze the ultrasound images of the eye. Automatic analysis of this disease is much
more effective than manual analysis.
Segmentation of optic disc in retinal images for glaucoma diagnosis by salie...IJECEIAES
Ā
Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-theart approaches in terms of qualitative and quantitative outcomes.
An Efficient Integrated Approach for the Detection of Exudates and Diabetic M...acijjournal
Ā
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 patientās 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.
Segmentation of Blood Vessels and Optic Disc in Retinal Imagesresearchinventy
Ā
Retinal image analysis is increasingly prominent as a non-intrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disc in the fundus retinal images. The method could be used to support non-intrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disc is an important indicator for diseases like diabetic retinopathy, glaucoma and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disc. The optic disc segmentation is performed using two alternative methods. The Markov Random Field (MRF) image reconstruction method segments the optic disc by removing vessels from the optic disc region and the Compensation Factor method segments the optic disc using prior local intensity knowledge of the vessels. The proposed method is tested on three public data sets, DIARETDB1, DRIVE and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disc.
Optic Disc and Macula Localization from Retinal Optical Coherence Tomography ...IJECEIAES
Ā
This research used images from Optical Coherence Tomography (OCT) examination as well as fundus images to localize the optical disc and macular layer of retina. The researchers utilized the OCT and fundus image to interpret the distance between macular center and optic disc in the image. The distance will express the area of macula that can be employed for further research. This distance could recognize the thickness of macula parameters diameter that will be used in localizing process of optic disc and macula. The parameters are the circle radius, the size of windowās filter, the constant value and the size of optic disc element structure as well as the size of macula. The results of this study are expected to improve the accuracy of macula detection that experience the edema.
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Ā
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.
Similar to A UTOMATIC C OMPUTATION OF CDR U SING F UZZY C LUSTERING T ECHNIQUES (19)
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Ā
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Ā
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as āpredictable inferenceā.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
Ā
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
Ā
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Ā
Are you looking to streamline your workflows and boost your projectsā efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, youāre in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part āEssentials of Automationā series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Hereās what youāll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
Weāll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Donāt miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Ā
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
Ā
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. Whatās changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Ā
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Ā
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
2. 28 Computer Science & Information Technology (CS & IT)
diseases by the ophthalmologists. Figure1 shows the important features of a retinal colour fundus
image.
Figure 1. Colour Fundus Image
This paper is organized as follows:
Section II presents a brief survey of existing literature. Section III describes the materials used for
the present work. A new algorithm to efficiently extract optic disc and optic cup in ocular fundus
images is given in Section IV. The results are presented in Section V, and Conclusions are given
in the final Section VI.
2. LITERATURE SURVEY
The Active Shape Model (ASM) based optical disk detection is implemented by Huiqi et al.[4].
The initialization of the parameters for this model is based on Principal Component Analysis
technique. The faster convergence rate and the robustness of the technique are proved by
experimental results. Huajun Ying et al. [5] designed a fractal-based automatic localization and
segmentation of optic disc in retinal images. K. Shekar [6] developed a method for OD
segmentation using Hussain, A.R. et al. [7] proposed a method for optic nerve head segmentation
using genetic active contours. Zhuo Zhang et al. [8] designed a convex hull based neuro-retinal
optic cup ellipse optimization technique. Wong, D.W.K. et al. [9] developed SVM-based model
optic cup detection for glaucoma detection using the cup to disc ratio in retinal fundus images.
Joshi G.D. et al. [10] developed vessel bend-based cup segmentation in retinal images. Shijian Lu
et al. [11] proposed a background elimination method for the automatic detection of OD.
Morphological operations were used for locating the optic disc in retinal images by Angel Suero
et al. [12].
In this paper, a new algorithm based on Fuzzy C-Means Clustering (FCM) technique combined
with thresholding, is used for OD and OC extraction. This new method, firstly, extracts the OD
and OC of the colour fundus image and computes the vertical CDR automatically. This is an
efficient method for the automatic screening of colour fundus image for CDR computation.
3. MATERIALS AND METHODS
The fundus images used in these experiments are taken from publicly available databases
DRION, DIARATDB0 and DIARETDB1 and, images from Giridhar Eye Institute, Kochi,
Kerala. The CDRs obtained from an ophthalmologist is used as ground truth for the evaluation.
4. DEVELOPED ALGORITHM
The new approach is composed of four steps. The channels of the colour retinal are separated.
The blood vessels are removed, applying the contrast adjustment to enhance the low contrast
3. Computer Science & Information Technology (CS & IT) 29
image image. The Fuzzy C Means combined with thresholding is applied on the red channel of
the input image for the extraction of the OD and the same technique is applied on the green
channel of the input image for the extraction of OC. The CDR is computed using the ratio of
vertical diameter of OC and OD.
4.1 Preprocessing
The preprocessing step excludes variations due to image acquisition, such as inhomogeneous
illumination. In preprocessing, techniques such as morphological operations and contrast
enhancement are applied on the input image [13]. The following sections include different
preprocessing operations used in this paper.
4.1.1 Preprocessing steps for Optic Disc Extraction
4.1.1.1 Selection of Red Channel
From the previous studies it is shown that even though the green component of an RGB
retinography is the one with highest contrast, the OD is often present in the red field as a well-
defined white shape, brighter than the surrounding area [14]. Therefore the red channel of the RGB
colour images is used for the extraction of OD regions in the retinal fundus images.
4.1.1.2 Removal of Blood Vessels
Since blood vessels within the OD are strong distracters, they should be erased from the image
beforehand. In this method a morphological closing operation is performed on the red channel.
The dilation operation first removes the blood vessels and then the erosion operation
approximately restores the boundaries to their former position.
Closing : )),,((),( BBADEBABAC āā=ā¢= (1)
where A is the red channel of the input image and B is a 10x10 symmetrical disc structuring
element, to remove the blood vessels[15]. C is the resultant vessel free, smoothed output image.
4.1.2 Preprocessing steps for Optic Cup Extraction
4.1.2.1 Selection of Green Channel
The green channel has low contrast variation which gives more differentiation between the blood
vessel and OC. The green channel, therefore, is selected for the extraction of the OC of the retinal
image.
4.1.2.2 Removal of Blood Vessels
Blood vessels in the green channel were removed using a morphological closing procedure,
)),,((),(2 BBIDEBABII āā=ā¢= (2)
where I is the green channel of the input image and B is an 8x8 symmetrical disc structuring
element, to remove the blood vessels[13]. I2 is the smoothed, vessel free output image. Figure 2
shows the preprocessing operations on the input image.
4. 30 Computer Science & Information Technology (CS & IT)
4.2. Feature Extraction
Medical image segmentation is a difficult task due to the complexity of segmentation. Because of
its simplicity and efficiency, threshold segmentation is wildly used in many fields. Assessment of
OD and OC is important in discriminating between normal and pathological retinal images. The
OD is a bright pattern of the fundus image. Recently, many studies on the use of fundus images in
extracting OD and OC have been reported. Fuzzy C Means Clustering with thresholding is used in
this work for the extraction of OD and OC.
4.3. Fuzzy C Means Clustering with Thresholding
The proposed method is a combination of fuzzy algorithm, C Means clustering and thresholding.
Clustering involves the task of dividing data points into homogeneous classes or clusters so that
items in the same class are as similar as possible and items in different classes are as dissimilar as
possible. Clustering can also be thought of as a form of data compression, where a large number
of samples are converted into a small number of representative prototypes or clusters. Different
types of similarity measures may be used to identify classes depending on the data and the
application, where the similarity measure controls the formation of the clusters. In the following
new method intensity value is used as the similarity measure. Thresholding is one of the most
powerful techniques for image segmentation, in which the pixels are partitioned depending on
their intensity value.
4.3.1. Fuzzy C-Means Clustering Algorithm
Fuzzy C-Means (FCM) Clustering is a clustering technique and it employs fuzzy partitioning
such that a data point can belong to all groups with different membership grades between 0 and 1.
It is an iterative algorithm. The aim of FCM is to find cluster centers (centroids) that minimize a
dissimilarity function. Corresponding to each cluster center, this algorithm works by assigning
membership to each data point on the basis of the difference between the cluster center and the
data point. The more the data is near to the cluster center, the more is its membership towards the
particular cluster center. It is obvious that the summation of membership of each data point
should be equal to one.
ā=
=ā=
c
i
ij nju
1
,...,1,1
(3)
dik = [ ām
j=1 [xkj - vj]2
]1/2
, (4)
5. Computer Science & Information Technology (CS & IT) 31
where xkj is data element, dik is the distance matrix and vij is the element of the cluster center
vector.
The dissimilarity function which is used in FCM is given Equation (5)
āāā = ==
==
c
i
n
j
ij
m
ij
c
i
ic duJcccUJ
1 1
2
1
21 ),...,,,(
(5)
uij is between 0 and 1;
ci is the centroid of cluster i;
dij is the Euclidian distance between ith centroid(ci) and jth data point;
m Ń [1,ā] is a weighting exponent.
To reach a minimum of dissimilarity function there are two conditions. These are given in
Equation (6) and Equation (7)
ā
ā
=
=
= n
j
m
ij
n
j j
m
ij
i
u
xu
c
1
1
(6)
ā =
ā
ļ£·
ļ£·
ļ£ø
ļ£¶
ļ£¬
ļ£¬
ļ£
ļ£«
=
c
k
m
kj
ij
ij
d
d
u
1
)1/(2
1
(7)
This algorithm determines the following steps [4].
Step1. Randomly initialize the membership matrix (U) that has constraints in Equation 7.
Step2. Calculate centroids (Ci) by using Equation(6).
Step3. Compute dissimilarity between centroids and data points using equation (5). Stop if its
improvement over previous iteration is below a threshold.
Step4. Compute a new U using Equation(7). Go to Step 2 [16][17].
By iteratively updating the cluster centers and the membership grades for each data point, FCM
iteratively moves the cluster centers to the apt location within a data set. To accommodate the
introduction of fuzzy partitioning, the membership matrix (U) is randomly initialized according to
Equation (7).
The Fuzzy Logic Toolbox command line function fcm is used for generating clusters, and in this
paper three clusters are generated from the vessel free enhanced image. The fcm function
iteratively moves the cluster centers to the right location within the data set. The outputs are 3
cluster centers C1, C2 and C3 and membership function matrix M with membership-grades,
which is the intensity value of each pixel.
6. 32 Computer Science & Information Technology (CS & IT)
4.4. Thresholding
Thresholding is the operation of converting a multilevel image into a binary image i.e., it assigns
the value of 0 (background) or 1 (objects or foreground) to each pixel of an image based on a
comparison with some threshold value T (intensity or colour value) [13][14][15]. By applying the
threshold T on an image, the image is converted to a binary image. The following formula (8)
[13] is used for the binary image extraction.
IT(x, y) = 1, if I (x, y) > T (8)
0, if I (x, y) <= T,
where I is the input image, T is the threshold and IT is the binary image after thresholding.
4.5. Extraction of Optic Disc
The main feature of the OD is that it is having the highest intensity. Therefore the highest
intensity is used as the threshold for the OD extraction. The threshold T is computed using the
following method. From the generated clusters, first the cluster with maximum membership grade
is found, and the corresponding grades are assigned with the same identification label. From the
smoothed image, pixels with this gray level value are accessed, the average of the maximum and
minimum intensity values are computed to obtain the threshold value T1.
i.e., T1 =
ą¬µ
ą¬¶
[Max (data (value)) + Min (data (value))] (9)
In the above equation, data represents the data points of the smoothed red channel image and
label represents the cluster value with the highest membership grade. By applying the threshold
T1 on the smoothed image IS the image is converted to a binary image. The formula (9) is used for
the binary image extraction.
Since the OD is of circular shape, the OD region selection process needs to be made specific to
the circular region. So the largest connected component Ri whose shape is approximately circular
is selected using the compactness measure
C (Ri) =
ą(ąą§)
ą¬øĻą (ąą§)
(10),
where, P(Ri) is the perimeter of the region Ri and A(Ri) is the area of the region Ri. The binary
image with the compactness smaller than the pre-specified value, (5 in the present study) is
considered as the optic disc approximation. Thus using the condition C < 5, extraction of round
objects is done, eliminating those objects that do not meet the criteria. In some cases the extracted
image contains small unwanted objects. The erosion operation is used to remove these objects.
The mean of the rows and columns form the centroid (Y1, X1) of the OD.
Y1 =
ą¬µ
ą«
ā row1ią«
ą§ąą¬µ (11)
X1 =
ą¬µ
ą¬
ā col1ią¬
ą§ąą¬µ , (12)
where m is the number of rows and n is the number of columns.
7. Computer Science & Information Technology (CS & IT) 33
From the above coordinates of the optic disc the minimum coordinates (ymin1, xmin1) is
calculated. The distance between the centroid and (ymin1, xmin1) represents the radius of the
disc.
Rąą ąµ Y1 ąµ ymin1 , (13)
where ROD is the radius of the optic disc.
4.6. Optic Disc Segmentation
OD segmentation obtains a circular boundary approximation within a retinal image. A circle is
plotted using the centroid (Y1, X1) and radius ROD, gives segmented OD on the colour fundus eye
image. Figure 3 shows the segmented OD [18].
4.7. Extraction of Optic Cup
The above mentioned FCM clustering with thresholding is applied on the smoothed green channel
for the extraction of OC.
The following algorithm includes four steps [4].
Step1. Randomly initialize the membership matrix (U) that has constraints in Equation (6).
Step2. Calculate centroids (Ci) by using Equation (7).
Step3. Compute dissimilarity between centroids and data points using equation (5). Stop if its
improvement over previous iteration is below a threshold.
Step4. Compute a new U using Equation (6). Go to Step 2[16][17].
The threshold values T2 is calculated using the following equation.
T2 =
ą¬µ
ą¬¶
[Max (data (value)) + Min (data (value))], (14)
where data represents the data points of the vessel free green channel and label represents the
cluster value with the highest membership grade.
Since the OC is the brightest portion in the green channel, thresholding with threshold T2 in
im2bw function helps to extract OC. This function returns the binary image forming the object
OC.
i ii iii
Figure 3 i. Extracted optic disc ii. Centre and radius of extracted disc
iii. Optic disc segmentation
8. 34 Computer Science & Information Technology (CS & IT)
The average of the rows and columns forms the centroid (Y2, X2) of the OC.
Y2 =
ą¬µ
ą«ą¬¶
ā row2ią«ą¬¶
ą§ąą¬µ (15)
X2 =
ą¬µ
ą¬ą¬¶
ā col2ią¬ą¬¶
ą§ąą¬µ , (16)
where m2 is the number of rows and n2 is the number of columns.
From the above coordinates of the optic cup the minimum coordinates (ymin2, xmin2) is
calculated. The Euclidian distance between the centroid and (ymin1, xmin1) returns the radius of
the cup.
ROC = Y2 ąµ ymin2, (17)
where ROC is the radius of the cup.
4.8. Optic Cup Segmentation
OC within OD usually appears in circular shape. Therefore the OC segmentation is a circular
boundary approximation. Using the centroid (Y2, X2) and radius ROC a circle is drawn onto the
current axes of the input image which would give the segmented OC on the colour fundus eye
image.
4.9. Computation of CDR
The manual method uses the ratio of the vertical diameter of OC and OD for the computation of
CDR. From the segmented OD the minimum row coordinate ymin1and maximum row coordinate
ymax1 are calculated. The Euclidian distance between these coordinates is the vertical diameter of
the OD, ODvdiam .
ODvdiam = ā«1ŻÜ½ŻŻā¬ ąµ ā«1ŻŻ ŻŻā¬ (18)
Similarly from the segmented OC the minimum row coordinate ymin2and maximum row
coordinate ymax2 are calculated. The Euclidian distance between these coordinates is the vertical
diameter of the OC, OCvdiam.
OCvdiam = ā«2ŻÜ½ŻŻā¬ ąµ ā«2ŻŻ ŻŻā¬ (19)
The CDR is calculated using the following formula
CDR = OCvdiam / ODvdiam (20)
i ii iii
Figure 4 i. Extracted Optic cup ii. Localization of centre and
radius of optic cup iii. Optic cup segmentation
9. Computer Science & Information Technology (CS & IT) 35
The following figure shows the OD vertical diameter ODvdiam and OC vertical diameter
OCvdiam of the input image.
5. RESULTS AND DISCUSSION
The automatic detection and evaluation of OD and OC is required for automatic diagnosis using
retinal images. This study thus brings to light simple but efficient methods for the extraction and
segmentation of OD and OC in retinal images. The CDR values are also automatically calculated.
The new method is evaluated on the basis of the ground truth data, where, vertical CDR values
are obtained from an expert ophthalmologist. Four hundred and fifty four color retinal images,
including thirty normal and four hundred and thirty three pathological images, are used in this
test. The performance evaluation is done by making use of the scatter plot analysis.
5.1 Image Data Sets
4.1.1 The DIARETDB0 and DIARETDB1 Databases
The DIARETDB0 and DIARETDB1 Database images were captured using an FOV of 50Ā° and
the size of each image is 1500 x 1152 x 3. Out of the 130 images of the DIARETDB0 database,
20 have normal architecture and 110 have various types of pathology. Out of the 89 images of the
DIARETDB1 database, 5 have normal architecture and 84 have various types of pathology.
5.1.2 DRION Database
It has 110 retinal images with each image having the resolution of 600 x 400 pixels and the optic
disc annotated by two experts with 36 landmarks. The mean age of the patients was 53.0 years
(standard Deviation 13.05), with 46.2% male and 53.8% female and all of them were Caucasian
ethnicity 23.1% patients had chronic simple glaucoma and 76.9% eye hypertension. The images
were acquired with a colour analogical fundus camera, approximately centered on the ONH and
they were stored in slide format. In order to have the images in digital format, they were digitized
using a HP-PhotoSmart-S20 high-resolution scanner, RGB format, resolution 600x400 and 8
bits/pixel.
5.1.3 Images from the ophthalmologist
125 images from Giridhar Eye Institute, Kochi was also used in this paper. All the images were
obtained using Carlzeiss fundus camera. In total 5 are normal images and remaining 120 are
diseased and the size of each image is 576 x 768 x 3.
10. 36 Computer Science & Information Technology (CS & IT)
5.2 Implementation
The new algorithm was applied on 454 images obtained from the above mentioned databases and
ophthalmologists. Seven of the input images from each data set, along with their OD and OD
segmentation on the input image, is shown in fig.6 (a) and fig.6 (b) respectively.
5.3 Performance Evaluation
The performance evaluation is done using the following parameters.
5.3.1 Success rate
The decision for successful segmentation or failed segmentation is based on human eye
observation. Table 1 shows the success rate of OD and OC segmentation using 454 images.
Table1. Success Rate Table
Database Normal Pathological Total Success Rate
(%)
Drion 0 110 110 94.5
Diaretdb0 20 110 130 95.4
Diaretdb1 5 84 89 93.3
Ophthalmologist 5 129 125 97.3
Total
30 433 454 94.26
(Average)
5.3.2 Accuracy
The accuracy of the technique was evaluated quantitatively by comparing the obtained vertical
CDR values with ophthalmologistsā ground-truth vertical CDR values. Fifteen examples of
(a) (b) (c) (d) (e) (f) (g) (h)
Figure 6 Input Image and OD & OC segmentation using new method on
images from (a) and(b) DIARATDB0 (e) and(f) DRION (c) and(d)
O
11. Computer Science & Information Technology (CS & IT) 37
detailed results of performance measurement using FCM clustering combined with thresholding
are displayed in Table II using fifteen test images of DRION database and fifteen test images
from the ophthalmologist.
Table II CDR Comparison Table shows the comparison of clinical CDR values with CDR values obtained
using the new method.
Images
Clinical
CDR
(1)
Obtained
CDR
(2)
Difference
(1) ā(2)
Clinical
CDR of
DRION
Database
(3)
Obtained
CDR
(4)
Difference
(3)-(4)
Image 1 0.5000 0.6082 0.1082 0.3333 0.4000 0.0667
Image 2 0.5714 0.6231 0.0517 0.5734 0.5310 0.0424
Image 3 0.6666 0.5505 0.1161 0.6666 0.6080 0.0586
Image 4 0.8517 0.7871 0.0646 0.7000 0.7543 0.0543
Image 5 0.7142 0.6412 0.073 0.6578 0.5816 0.0762
Image 6 0.4864 0.4173 0.0691 0.5625 0.5045 0.0580
Image 7 0.7060 0.6275 0.0785 0.4062 0.4995 0.0933
Image 8 0.9000 0.8367 0.0633 0.5000 0.6020 0.1020
Image 9 0.6801 0.7287 0.0486 0.6097 0.6696 0.0626
Image 10 0.9026 0.8206 0.0820 0.6410 0.6610 0.0200
Image 11 0.4631 0.4012 0.0619 0.5162 0.4011 0.1151
Image 12 0.6267 0.5151 0.1116 0.5010 0.5362 0.0352
Image 13 0.5147 0.4736 0.0411 0.6097 0.5215 0.0882
Image 14 0.7318 0.7180 0.0138 0.7408 0.6943 0.0465
Image 15 0.4265 0.3162 0.1103 0.6981 0.6208 0.0773
Mean Difference 0.07292 Mean Difference 0.06643
From the table it is shown that the mean differences between the clinical CDR values and
obtained CDR values are very low.
5.3.3 Scatter plot Analysis
The statistical analysis is done using the scatter plot diagram. The clinical CDR vales and the
obtained CDR values of the above mentioned data set are analysed using the scatter plot analysis.
From the diagram it is shown that there exists highly positive linear relationship between both
CDR values. Figure7 depicts the comparison between the clinical CDR and obtained CDR values.
12. 38 Computer Science & Information Technology (CS & IT)
Figure7. Scatter Plot Diagram (a) Diaretdb0 (b) Diaretdb1 (c) Drion (d) Ophthalmologist
6. CONCLUSION
This paper presents a new fuzzy based approach for OD and OC extraction and segmentation
together with CDR computation. The results presented in this paper show that the new
methodology offers a reliable and robust solution for CDR computation. The scatter plot analysis
results a high positive correlation between the clinical CDR and the obtained CDR. This
automated method is very useful for the automatic screening of retinal images. However the
present method has the following limitations. It is assumed that the OD and OC are brighter than
the surrounding pixels and therefore cannot handle retinal images with a relatively dark OD.
Hence advanced extraction methods are required for future studies and research.
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AUTHORS
Thresiamma Devasia was graduated with Bachelor of Mathematics (BSc.Maths) from
Mahatma University, Kerala, India in 1991, and finished her Master of Computer
Applications (MCA) and M.Phil Computer Science from Alagappa University
Tamilnadu, India in 1995 and 2010, respectively.
Currently, she is the Head and Associate professor, Department of Computer Science at
Assumption College Changanacherry, Kerala, India and working toward her Ph.D. at
Cochin University of Science And Technology on glaucoma detection using image
processing.
She completed UGC sponsored minor research project based on image processing. She was a member of
IEEE. Her interest areas include image processing and medical imaging.
Dr. K.Poulose Jacob, Professor of Computer Science at Cochin University of Science
and Technology since 1994, is currently Pro Vice Chancellor of Cochin University of
Science & Technology. He has presented research papers in several International
Conferences in Europe, USA, UK, Australia and other countries. He has served as a
Member of the Standing Committee of the UGC on Computer Education &
Development. He is the Zonal Coordinator of the DOEACC Society under the Ministry
of Information Technology, Government of India. He serves as a member of the AICTE
expert panel for accreditation and approval. He has been a member of several academic bodies of different
Universities and Institutes. He is on the editorial board of two international journals in Computer Science.
Dr. K.Poulose Jacob is a Professional member of the ACM (Association for Computing Machinery) and a
Life Member of the Computer Society of India.
Dr.Tessamma Thomas received her M.Tech. and Ph.D from Cochin University of
Science and Technology, Cochin-22, India. At present she is working as Professor in
the Department of Electronics, Cochin University of Science and Technology. She has
to her credit more than 100 research papers, in various research fields, published in
International and National journals and conferences. Her areas of interest include digital
signal / image processing, bio medical image processing, super resolution, content
based image retrieval, genomic signal processing, etc.