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.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
Glaucoma Disease Diagnosis Using Feed Forward Neural Network ijcisjournal
Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to
complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure
(IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible
blindness, so it should be diagnosed and treated properly at an early stage. In this paper,
thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for
obtaining average and energy texture feature which are used to classify glaucoma disease with high
accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%.
In this work, the computational complexity is minimized by reducing the number of filters while retaining
the same accuracy.
Classification and Segmentation of Glaucomatous Image Using Probabilistic Neu...ijsrd.com
The gradual visual field loss and there is a characteristic type of damage to the retinal nerve fiber layer associated with the progression of the disease glaucoma. Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subband is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the Daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. Here my project aims at the use of Probabilistic Neural Network (PNN), Fuzzy C-means (FCM) and K-means helps for the detection of glaucoma disease. For this, fuzzy c-means clustering algorithm and k-means algorithm is used. Fuzzy c-means results faster and reliably good clustering when compare to k-means.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
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.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
Glaucoma Disease Diagnosis Using Feed Forward Neural Network ijcisjournal
Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to
complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure
(IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible
blindness, so it should be diagnosed and treated properly at an early stage. In this paper,
thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for
obtaining average and energy texture feature which are used to classify glaucoma disease with high
accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%.
In this work, the computational complexity is minimized by reducing the number of filters while retaining
the same accuracy.
Classification and Segmentation of Glaucomatous Image Using Probabilistic Neu...ijsrd.com
The gradual visual field loss and there is a characteristic type of damage to the retinal nerve fiber layer associated with the progression of the disease glaucoma. Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subband is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the Daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. Here my project aims at the use of Probabilistic Neural Network (PNN), Fuzzy C-means (FCM) and K-means helps for the detection of glaucoma disease. For this, fuzzy c-means clustering algorithm and k-means algorithm is used. Fuzzy c-means results faster and reliably good clustering when compare to k-means.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
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.
Haemorrhage Detection and Classification: A ReviewIJERA Editor
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
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
Automated Diagnosis of Glaucoma using Haralick Texture FeaturesIOSR Journals
Abstract : Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid
pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational
decision support systems for the early detection of glaucoma can help prevent this complication. The retinal
optic nerve fibre layer can be assessed using optical coherence tomography, scanning laser polarimetry, and
Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma
detection using an Haralick Texture Features from digital fundus images. K Nearest Neighbors (KNN)
classifiers are used to perform supervised classification. Our results demonstrate that the Haralick Texture
Features has Database and classification parts, in Database the image has been loaded and Gray Level Cooccurrence
Matrix (GLCM) and thirteen haralick features are combined to extract the image features, performs
better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than
98%. The impact of training and testing is also studied to improve results. Our proposed novel features are
clinically significant and can be used to detect glaucoma accurately.
Keywords: Glaucoma, Haralick Texture features, KNN Classifiers, Feature Extraction
Fundus Image Classification Using Two Dimensional Linear Discriminant Analysi...INFOGAIN PUBLICATION
It is constructed in this study a classification system of diabetic retinopathy fundus image. The system consists of two phases: training and testing. Each stage consists of preprocessing, segmentation, feature extraction and classification. The tested image comes from the MESSIDOR dataset which has a total of 100 images. The number of classes to be classified consists of four classes with each class consists of 25 images. The classes are normal, mild, moderate and severe of Diabetic retinopathy. In this study, the level of preprocessing uses grayscales green channel, Wavelet Haar, Gaussian filter and Contrast Limited Adaptive Histogram Equalization. The level of segmentation uses masking as a process of doing the subtracting operation of between the original image and the masking image. The purpose of the masking is to split between the object and the background. The feature extraction uses Two Dimensional Linear Discriminant Analysis (2DLDA). The classification uses Support Vector Machine (SVM). The test results of some scenarios show that the highest percentage of accuration of the system is up to 90%.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
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 SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found
to be 96%.
Binary operation based hard exudate detection and fuzzy based classification ...IJECEIAES
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A SIMPLE APPROACH FOR RELATIVELY AUTOMATED HIPPOCAMPUS SEGMENTATION FROM SAGI...ijbbjournal
In this paper, we present a relatively automated method to segment the hippocampus in t1 weighted
magnetic resonance images that can be acquired in the routine clinical setting. This paper describes a
simple approach for segmenting the hippocampus automatically from sagittal view of brain MRI. Large
datasets of structural MR images are collected to quantitatively analyze the relationships between brain
anatomy, disease progression, treatment regimens, and genetic influences upon brain structure..This
method segments the hippocampus without any human intervention for few slices present mid position in
the total volume. Experimental results using this method show a good agreement with the manual
segmented gold standard. These results may support the clinical studies of memory and neurodegenerative
disease
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
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
DETECTION OF HARD EXUDATES USING SIMULATED ANNEALING BASED THRESHOLDING MECHA...cscpconf
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes severe damage to retina and may lead to complete or partial visual loss. In case of diabetic retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked out of the damaged blood vessels and are deposited in the intra-retinal space. They are normally seen as whitish marks of various shape and are called as exudates. Exudates are primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible in nature, the disease must be detected in early stages to prevent visual loss. But detection of exudates in early stages of the disease is extremely difficult only by visual inspection because of small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such method.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes
severe damage to retina and may lead to complete or partial visual loss. In case of diabetic
retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked
out of the damaged blood vessels and are deposited in the intra-retinal space. They are
normally seen as whitish marks of various shape and are called as exudates. Exudates are
primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible
in nature, the disease must be detected in early stages to prevent visual loss. But detection of
exudates in early stages of the disease is extremely difficult only by visual inspection because of
small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such
method.
Haemorrhage Detection and Classification: A ReviewIJERA Editor
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
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
Automated Diagnosis of Glaucoma using Haralick Texture FeaturesIOSR Journals
Abstract : Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid
pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational
decision support systems for the early detection of glaucoma can help prevent this complication. The retinal
optic nerve fibre layer can be assessed using optical coherence tomography, scanning laser polarimetry, and
Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma
detection using an Haralick Texture Features from digital fundus images. K Nearest Neighbors (KNN)
classifiers are used to perform supervised classification. Our results demonstrate that the Haralick Texture
Features has Database and classification parts, in Database the image has been loaded and Gray Level Cooccurrence
Matrix (GLCM) and thirteen haralick features are combined to extract the image features, performs
better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than
98%. The impact of training and testing is also studied to improve results. Our proposed novel features are
clinically significant and can be used to detect glaucoma accurately.
Keywords: Glaucoma, Haralick Texture features, KNN Classifiers, Feature Extraction
Fundus Image Classification Using Two Dimensional Linear Discriminant Analysi...INFOGAIN PUBLICATION
It is constructed in this study a classification system of diabetic retinopathy fundus image. The system consists of two phases: training and testing. Each stage consists of preprocessing, segmentation, feature extraction and classification. The tested image comes from the MESSIDOR dataset which has a total of 100 images. The number of classes to be classified consists of four classes with each class consists of 25 images. The classes are normal, mild, moderate and severe of Diabetic retinopathy. In this study, the level of preprocessing uses grayscales green channel, Wavelet Haar, Gaussian filter and Contrast Limited Adaptive Histogram Equalization. The level of segmentation uses masking as a process of doing the subtracting operation of between the original image and the masking image. The purpose of the masking is to split between the object and the background. The feature extraction uses Two Dimensional Linear Discriminant Analysis (2DLDA). The classification uses Support Vector Machine (SVM). The test results of some scenarios show that the highest percentage of accuration of the system is up to 90%.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
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 SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found
to be 96%.
Binary operation based hard exudate detection and fuzzy based classification ...IJECEIAES
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A SIMPLE APPROACH FOR RELATIVELY AUTOMATED HIPPOCAMPUS SEGMENTATION FROM SAGI...ijbbjournal
In this paper, we present a relatively automated method to segment the hippocampus in t1 weighted
magnetic resonance images that can be acquired in the routine clinical setting. This paper describes a
simple approach for segmenting the hippocampus automatically from sagittal view of brain MRI. Large
datasets of structural MR images are collected to quantitatively analyze the relationships between brain
anatomy, disease progression, treatment regimens, and genetic influences upon brain structure..This
method segments the hippocampus without any human intervention for few slices present mid position in
the total volume. Experimental results using this method show a good agreement with the manual
segmented gold standard. These results may support the clinical studies of memory and neurodegenerative
disease
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
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
DETECTION OF HARD EXUDATES USING SIMULATED ANNEALING BASED THRESHOLDING MECHA...cscpconf
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes severe damage to retina and may lead to complete or partial visual loss. In case of diabetic retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked out of the damaged blood vessels and are deposited in the intra-retinal space. They are normally seen as whitish marks of various shape and are called as exudates. Exudates are primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible in nature, the disease must be detected in early stages to prevent visual loss. But detection of exudates in early stages of the disease is extremely difficult only by visual inspection because of small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such method.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes
severe damage to retina and may lead to complete or partial visual loss. In case of diabetic
retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked
out of the damaged blood vessels and are deposited in the intra-retinal space. They are
normally seen as whitish marks of various shape and are called as exudates. Exudates are
primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible
in nature, the disease must be detected in early stages to prevent visual loss. But detection of
exudates in early stages of the disease is extremely difficult only by visual inspection because of
small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such
method.
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.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
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
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
A Deep Learning Approach To Evaluation Of Augmented Evidence Of Diabetic Reti...Christo Ananth
Christo Ananth, D.R. Denslin Brabin, Jenifer Darling Rosita, “A Deep Learning Approach To Evaluation Of Augmented Evidence Of Diabetic Retinopathy”, Turkish Journal of Physiotherapy and Rehabilitation, Volume 32,Issue 3, December 2021,pp. 11813-11817.
Description:
Christo Ananth et al. discussed about diabetic retinopathy from retinal pictures utilizing cooperation and information on state of the art sign dealing with and picture preparing. The Pre-Processing stage remedies the lopsided lighting in fundus pictures and furthermore kills the fight in the picture. Although the Disease Classifier step was used to identify arising wounds and other data, the Division stage divides the image into two distinct classes. The methodology for ensuring red spots, exhausting and recognizing evidence of vein-lobby hybrid focuses was also developed in this work, using the hidden data, shape, size, object length to expansiveness distribution as contained in the general fundus picture in the problem area. Besides the Diabetic Retinopathy (DR) analysis, two graphical user interfaces (GUIs) were produced throughout this project. The primary GUI is for the mix of sore information data and was utilized by the ophthalmologist in venturing pictures for enlightening assortment, while the subsequent GUI is for redone diagnosing and showing the examination to accomplish a significantly seriously satisfying UI.
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.
This research detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing to the fundus
images to extract features such as blood vessels, micro aneurysms,
haemorrhages ,exudates and neo vascularization.
Optic Disc and Macula Localization from Retinal Optical Coherence Tomography ...IJECEIAES
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 window’s 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.
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 — IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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Gen AI Study Jams _ For the GDSC Leads in India.pdf
Detection of exudates draft
1. Detection of Exudates
Abstract:
A technique for exudate detection in 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.
I. INTRODUCTION
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.
Fig. 1. Retinal image with symptoms of diabetic retinopathy
2. The paper as a whole speaks about extracting the Exudate region from the loaded test image.
Initially green channel from the loaded colored retina image is extracted. Further segmentation of
green channel using discreet wavelet transformer which is pre-processed using histogram analysis
and feature extraction is initiated. Following extracting of feature for all the database images in the
same process is functioned. The database features and extracted test image feature are applied for
3 different classifiers, the classifier results are compared in terms of accuracy sensitivity and
specificity. Finally if an exudate is present in the test image, the extraction of exudate region ROI
is done based on canny edge detection and morphological operations
Organization of the paper is done as follows: A brief idea about diabetic retinopathy is given in
introduction section. In literature survey section we have explained few existing methodologies
and their outcomes. Proposed methodologies is explained in section 3, followed by results and
comparison. The reference papers are given in last section.
II. LITERATURE SURVEY
There are several algorithms available in the literature for detection of exudates in fundus
images. Exudates regions show higher gray level intensity and contrast in fundus images. Some
work using image processing algorithms on Fundus image to find exudates has been reported.
Morphological and Neural Network Based Approach by Sangita Bharkad et.al [1], In this work,
optic disc (OD) is extracted with the help of morphological operators. OD is masked in green
component image to avoid the misclassification between OD region and hard exudates region.
Then features of green component image are computed and applied to neural network for detection
of hard exudates. Experimental results show the better competency of algorithm 100% sensitivity
95.45% specificity and 97.46% accuracy. This work can be extended for lesion based detection of
hard exudates in retinal images.
3σ Control Method by Mohammed Shafeeq Ahmed et.al [2], the work presented in this is to detect
the exudates (yellowish fat deposit on the retinal surface) from fundus images in RGB color space,
thereby facilitate a realistic diagnosis close to the method adopted by ophthalmologist. A Statistical
measure-three sigma is used to compute the color intensity range of exudates pixels. The retinal
images are preprocessed to enhance the color intensity and optic disk (OD) is eliminated because,
it shares similar features with exudates. The aim and objective of this work is to detect the exudates
from RGB fundus images, the pre-processed images are then classified based on the information
extracted from threesigma control method. The elimination of optic disc is also a key step in
preprocessing; Hough Transform method assures that the eliminated part is OD not exudates and
it also used to obtain good results. The method presented has yielded encouraging results with
sensitivity of 99.93% and specificity of 99.99 %. The results so obtained are promising and also
facilitates the ophthalmologist in diagnosing the disease.
Wavelet Transform and PNN Approach by C. Nivetha et.al [3] proposes a method to find the
exudates (patches) from the blood vessels of an eye during diabetic retinopathy treatment. For
analysis, input retinal image is separated into three planes i.e., red, green and blue, from which
green channels are selected, which are further processed using Daubechies wavelet transform to
estimate the grey level co-occurrence matrix (GLCM) features. These features are processed using
probabilistic neural network (PNN) and input retinal image is compared with the database image
to classify it as normal or abnormal. Morphological operations are applied to the abnormal image
3. to extract the blood vessels and then fuzzy C-means clustering is applied in the extracted blood
vessels to detect the exudates. The accuracy and sensitivity of the result obtained by the proposed
method is comparatively better and are 0.9776 and 0.9677 respectively. The method can be applied
successfully and the blood vessels and exudates can be effectively detected.
III. PROPOSED METHODOLOGY
The detailed methodology and process of the proposed exudates detection is been described
in the below block diagram.
Fig. Detection and Classification of Exudates from Fundus Images
3.1 Image Acquisition (Describe of Dataset)
In this work, the input images used obtained from the DIAREDB1 database [1]. It consists
of 89 color fundus images of 1500x1152 pixels of which 84 contain non-proliferative signs of the
diabetic retinopathy, and 5 are considered as normal. Images were captured using the same 50
degree field-of-view digital fundus camera with varying imaging settings. This data set is referred
to evaluate the performance of this method.
Input
Image
Green
Channel
Pre-
processing
Segmenta
tion
Feature
Extraction
Classification
Process
Normal Image Exudate Image
Exudates Detection
Result Analysis
Database
Images
Green
Channel
Pre-
processing
Segmenta
tion
Feature
Extraction
4. 3.2 Pre-Processing using Histogram Equalization
An image histogram is a type of histogram that acts as a graphical representation of the
tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking
at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at
a glance. This method usually increases the global contrast of many images, especially when the
usable data of the image is represented by close contrast values. Through this adjustment, the
intensities can be better distributed on the histogram. This allows for areas of lower local contrast
to gain a higher contrast. Histogram equalization accomplishes this by effectively spreading out
the most frequent intensity values.
3.3 Image Segmentation using 2-DWT
The wavelet transform has gained widespread acceptance in signal processing and image
compression. Recently the JPEG committee has released its new image coding standard, JPEG-
2000, which has been based upon DWT. Wavelet transform decomposes a signal into a set of basis
functions. These basis functions are called wavelets. Wavelets are obtained from a single prototype
wavelet called mother wavelet by dilations and shifting[8]. The DWT has been introduced as a
highly efficient and flexible method for sub band decomposition of signals. The 2DDWT is
nowadays established as a key operation in image processing .It is multi-resolution analysis and it
decomposes images into wavelet coefficients and scaling function. In Discrete Wavelet Transform,
signal energy concentrates to specific wavelet coefficients. This characteristic is useful for
compressing images[9].
3.4 Feature Extraction
Shape feature extraction used in this paper are solidity, extent, minor axis length and
eccentricity. This features taken Shape feature extraction used in this paper are solidity, extent,
minor axis length and eccentricity. This features taken from research [3] in order to extract shape
feature in diseased region.
Eccentricity is used to recognize whether the rust shape is a circle or line segment.
Eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. An
ellipse whose eccentricity is 0 can recognized as a circle, while an ellipse whose eccentricity is 1
can recognized as a line segment.
Gray Level Co-occurrence Matrix (GLCM) extract second order statistical texture features.
Texture feature extraction used in this research are contrast, correlation, energy and homogeneity.
This features taken from research [3] to extract texture feature in leaf diseased region.
Contrast of the pixel and its neighbors is calculated over all of the image pixels. Contrast
is used to measure contrast between neighborhood pixels.
Color is a distinctive feature for image representation that is invariant with respect to
scaling, translation and rotation of an image [9]. Mean, skewness and kurtosis are used to represent
color as features.
5. 3.5 Classification
Data mining algorithms which carry out the assigning of objects into related classes are
called classifiers. Classification algorithms include two main phases; in the first phase they try to
find a model for the class attribute as a function of other variables of the datasets, and in the second
phase, they apply previously designed model on the new and unseen datasets for determining the
related class of each record [1].
3.5.1 KNN Classifier
Nearest neighbor classifiers are based on learning by analogy, that is by comparing a given
test tuple with training tuples which are similar to it. The training tuples are described by n
attributes. Each tuple represents a point in an n-dimensional space. In this way, all of the training
tuples are stored in an n-dimensional pattern space. When given an unknown tuple, a k-nearest
neighbor (k-NN) classifier searches the pattern space for the k training tuples which are closest to
the unknown tuple. These k training tuples are the k-nearest neighbors of the unknown tuple [20].
“Closeness” is defined in terms of a distance metric, such as Euclidean distance. The
Euclidean distance between two points or tuples X1=(x11, x12,…, x1n) and X2=(x21, x22,…, x2n)
obtained from Equation 3.
3.5.2 Support Vector Machine
A support vector machine (SVM) is an algorithm that uses a nonlinear mapping to
transform the original training data into a higher dimension. Within this new dimension, it searches
for the linear optimal separating hyperplane. A hyperplane is a “decision boundary” separating the
tuples of one class from another. With an appropriate nonlinear mapping to a sufficiently high
dimension, data from two classes can always be separated by a hyperplane. The SVM finds this
hyperplane using support vectors (“essential” training tuples) and margins (defined by the support
vectors) [20].
3.5.3 Neural Network
The term neural network was traditionally used to refer to a network or circuit of neurons.
The modern usage of the term often refers to artificial neural networks, which are composed of
artificial neurons or nodes. Thus the term may refer to either biological neural networks, made up
of real biological neurons, or artificial neural networks, for solving artificial intelligence (AI)
problems. The connections of the biological neuron are modeled as weights. A positive weight
reflects an excitatory connection, while negative values mean inhibitory connections. All inputs
are modified by a weight and summed.
3.6 Exudate segmentation
Based on the classifier results following steps are used for segmentation of the exudate in
the original image.
6. 3.6.1 Canny edge detection
Canny edge detection is a technique to extract useful structural information from different
vision objects and dramatically reduce the amount of data to be processed. It has been widely
applied in various computer vision systems. Canny has found that the requirements for the
application of edge detection on diverse vision systems are relatively similar. Thus, an edge
detection solution to address these requirements can be implemented in a wide range of situations.
3.6.2 Morphological operations
Binary images may contain numerous imperfections. In particular, the binary regions produced by
simple thresholding are distorted by noise and texture. Morphological image processing pursues
the goals of removing these imperfections by accounting for the form and structure of the image.
These techniques can be extended to greyscale images. Dilation and erosion are the two basic
morphological operation involved in gray image processing.
Algorithm: Detection and Classification of Exudates from Fundus Images
Step 1:- Read the input color retinal image (select only one image--normal or soft or hard)
Step 2:- Select the green channel image
Step 3:- Pre-Processing using histogram
Step 4:- Segmentation using 2D-DWT
Step 5:- Feature Extraction Process
Step 6:- Database Loading Process (load all images at a time)
Select the green channel image
Pre-Processing using histogram
Segmentation using 2D-DWT
Feature Extraction Process
Step 7:- Classification / Matching Process using KNN (1), SVM (2) and NN (3) Methods
Normal Image
Soft Exudate Image
Hard Exudate Image
Step 8:- Comparison (1, 2 & 3) and Classification results analysis (Accuracy, Sensitivity and
Specificity etc.)
Step 9:- if image is Exudate image, detect the exudates part in retina image
Take the Step 4 output image
Apply the ROI Segmentation
Apply the Canny edge detection
Apply the Morphological operations
Detect the exudates
Step 10:- Calculate the affected area of exudates (based on total White Pixels)
7. IV. RESULTS AND DISCUSSIONS
4.1 Comparison of Classifier Results:
Fig. Classifier results
8. 4.2 Extraction of exudates
V. CONCLUSION
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