S. Vasanthi

Bio: S. Vasanthi is an academic researcher. The author has contributed to research in topics: Pixel & Feature vector. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

An Efficient ELM Approach for Blood Vessel Segmentation in Retinal Images

Abstract: Diabetic Retinopathy (DR) is one of the most important ophthalmic pathological reasons of blindness among people of working age. Previous techniques for blood vessel detection in retinal images can be categorized into rule- based and supervised methods. This research presents a new supervised technique for blood vessel detection in digital retinal images. This novel approach uses an Extreme Learning Machine (ELM) approach for pixel classification and calculates a 7-D vector comprises of gray-level and moment invariants-based features for pixel representation. The approach is based on pixel classification using a 7-D feature vector obtained from preprocessed retinal images and given as input to a ELM. Classification results (real values between 0 and 1) are thresholded to categorize each pixel into two classes namely vessel and nonvessel. Ultimately, a post processing fills pixel gaps in detected blood vessels and eliminates falsely-detected isolated vessel pixels. The technique was assessed on the publicly available DRIVE and STARE databases, widely used for this purpose, as they comprises of retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The approach proves particularly accurate for vessel detection in STARE images. Its efficiency and strength with different image conditions, along with its simplicity and fast implementation, make this blood vessel segmentation proposal appropriate for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

Automatic detection of microaneurysms in color fundus images for screening of diabetic retinopathy.

Abstract: This paper addresses the automatic detection of microaneurysms in color fundus images, which plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease. The algorithm can be divided into four steps. The first step consists in image enhancement, shade correction and image normalization of the green channel. The second step aims at detecting candidates, i.e. all patterns possibly corresponding to MA, which is achieved by diameter closing and an automatic threshold scheme. Then, features are extracted, which are used in the last step to automatically classify candidates into real MA and other objects; the classification relies on kernel density estimation with variable bandwidth. A database of 21 annotated images has been used to train the algorithm. The algorithm was compared to manually obtained gradings of 94 images; sensitivity was 88.5% at an average number of 2.13 false positives per image.

Severity grading of diabetic retinopathy using extreme learning machine

Abstract: A computationally effective method for diagnosing the severity of Diabetic Retinopathy is proposed. The proposed approach follows 3 stages; the preprocessing, feature extraction and classification. The purpose of the first step is to make the image suitable for subsequent process. In feature extraction, number of micro aneurysms and texture features were extracted by morphological process and the last step is the classification, in which the images are categorized by these features with the help of ELM classifier. The above procedure was tested and analyzed using images in DIARETDB0 and DRIVE database and we were able to achieve accuracy of 95% with good training speed.

Detection of the Emergence of Exudate on the Image of Retina Using Extreme Learning Machine Method

Abstract: Diabetic retinopathy is a health problem that cause damage to the retinal blood vessels and occurs in more than half of people who suffer from diabetes. It is estimated that around 28 million people experience loss of sight for this reason. Thus, the system for detecting early signs of diabetic retinopathy will be very helpful and one of first signs of the onset of symptoms of diabetic retinopathy is the appearance of exudates in the retinal image of the eye. To build an exudate emergence detection system, in this study use the method of extreme learning machine (ELM) which has a fast learning speed. This system uses the gray level co-occurrence matrix feature extraction with 6 features, namely contrast, homogeneity, correlation, ASM, energy and dissimilarity. To get the best model, six scenarios are used by distinguishing the preprocessing flow. The pre processing stage carried out by all scenarios is optic disc removal, green channel separation, contrast limited adaptive histogram equalization (CLAHE) followed by two different preprocessing lines, namely applying brightness and dilation and erosion operations. Then the second path is radon transform, top-hat filtering, discrete wavelet transform and dilation and erosion. The best model results reached the best accuracy value of 65% with a combination of multiquadric activation functions and 30 hidden neurons.

An Improved Retinal Blood Vessel Detection System Using an Extreme Learning Machine

Abstract: Retinal images are commonly used to diagnose various diseases, such as diabetic retinopathy, glaucoma, and hypertension. An important step in the analysis of such images is the detection of blood vessels, which is usually done manually and is time consuming. The main proposal in this work is a fast method for retinal blood vessel detection using Extreme Learning Machine (ELM). ELM requires only one iteration to complete its training and it is a robust and fast network in all aspects. The proposal is a compact and efficient representation of retinal images in which the authors achieved a reduction up to 39% of the initial data volume, while still keeping representativeness. To achieve such a reduction whilst maintaining the representativeness, three features (local tophat, local average, and local variance) were used. According to the simulations carried out, this proposal achieved an accuracy of about 95% for most results, outperforming most of the state-of-art methods. Furthermore, this proposal has greater sensitivity, meaning that more vessel pixels are detected correctly.

Retinal Image Analysis for Biometrics

Abstract: A design of retinal image analysis using feature detection and feature matching techniques is proposed. Biometric systems perform person's authentication based on one’s physical features. A number of biometric systems has been developed in the last few years such as fingerprints, iris etc. The retinal scans serves the biometric based security systems since the unique pattern of blood vessels serves the purpose. The retinal images are acquired from the DRIVE and STARE database and various feature detection algorithms are used to detect and extract features. The original image is recovered from the distorted image using MSAC algorithm and the extracted features are then compared and feature matching is done to identify the amount of matching to identify and authorize the person. The main limitation of this process lies in the image acquisition. Hence retinal image database is used as the source of