Santhakumar R

Bio: Santhakumar R is an academic researcher from VIT University. The author has contributed to research in topics: Fundus (eye) & Support vector machine. The author has an hindex of 3, co-authored 4 publications receiving 13 citations.

Machine learning algorithm for retinal image analysis

Abstract: Diabetic retinopathy is the most general diabetes complication that affects eyes and results in blindness. It's due to impairment of the arteries a veins located in the fundus of eye (retina) that are composed of light sensitive tissues. The aim of this research work is to design an efficient and sensitive tool for Diabetic Retinopathy using the images acquired from portable fundus camera. The screening tool is based on advanced machine learning and computer vision algorithm which includes patch level prediction. In patch level prediction algorithm will localize the diseased region in the Diabetic Retinopathy image like Hard Exudates and Hemorrhage. The patch level classification uses Support Vector Machine (SVM) machine learning classifier model to predict the potential patch of Hard Exudates and Hemorrhage. In this algorithm, the image is broken into regular rectangular patch. The feature for each patch along with the different class label based on the ground truth is computed and passed to strong classifier SVM. The data sets are split into training dataset and testing dataset. The classifier model is built on training dataset and tested against the test dataset. The performance results of rectangular patch level prediction using SVM the average performance for Hard Exudates was Accuracy 96 %, Sensitivity 94%, Specificity 96%. The average performance for Hemorrhage was Accuracy 85 %, Sensitivity 77%, and Specificity 85%.

Novel method for automatic generation of fundus mask

Abstract: Fundus imaging is a vital diagnostic tool in detection of retinal diseases. It is one of the routinely used method for screening Diabetic Retinopathy. Diabetic Retinopathy is one of the vision threatening and leading cause for blindness, early detection of which would prevent it Processing the fundus image for automated detection of features describing Diabetic Retinopathy includes extraction of foreground from background. This needs generation of a proper mask. Previously used techniques include thresholding and morphology operations. We have used image processing tools like ImageJ software [10] which also have tools to generate masks. All of these existing methods fail to give a perfect mask when the fundus image is not well exposed. In this paper we propose a simple yet effective method to overcome this drawback. The proposed method uses a log operation on the image for mask generation. The effectiveness of the proposed method is validated by experiments on different public databases like DIARETDB1 [11] and MESSIDOR [12]. The average error rate of our proposed method is found to be 0.4237%.

A fast algorithm for optic disc segmentation in fundus images

Abstract: Advances in computational complexity of the computer have made Computer-Aided Diagnosis a reality. Automation of computer aided diagnosis using advance algorithm helps to solve complex problem in medical imaging. In the frame of Computer-Aided Diagnosis, this paper presents a fast and efficient method for optic disc detection in fundus Images captured from a portable fundus camera. The algorithm uses a combination of image scaling by adaptive mean thresholding, region splitting and statistical evaluation to detect optic disc. Experiments show that optic disc detection accuracies of 98%, 95%, and 90% are obtained for the OPTOMED database, the MESSIDOR database, and the DIARETDB1 database, respectively. Average runtime of our algorithm is 0.8 s which is substantially faster than many of the existing methods.

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Abstract: Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

A Support Vector Regression-Based Method for Target Direction of Arrival Estimation From HF Radar Data

Abstract: High-frequency (HF) radars have great potential for maritime surveillance, and the multiple signal classification (MUSIC) algorithm is usually used to estimate the direction of arrival (DOA) of targets for a wide-beam radar. However, the performance of the MUSIC algorithm relies on the precision of the antenna pattern, which could be contaminated by nearby electromagnetic interference. Therefore, the actual antenna pattern must be measured and used. In order to remove the requirement of antenna pattern measurement, a new method for target DOA estimation from wide-beam HF radar data using support vector regression (SVR) is proposed in this letter. A system model that relates target bearing and radar data feature is obtained through the SVR-based machine learning using the automatic identification system data and data associated with the vessels successfully detected by the HF radar. Then, such a model is used to determine the DOAs of targets from new data. The field experimental results at two sites demonstrate that the performance of the SVR method is better than that of the MUSIC algorithm.