C. Eswaran

Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.

Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds

The detection of bright objects such as optic disc (OD) and exudates in color fundus images is an important step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. In this paper, a novel approach to automatically segment the OD and exudates is proposed. The proposed algorithm makes use of the green component of the image and preprocessing steps such as average filtering, contrast adjustment, and thresholding. The other processing techniques used are morphological opening, extended maxima operator, minima imposition, and watershed transformation. The proposed algorithm is evaluated using the test images of STARE and DRIVE databases with fixed and variable thresholds. The images drawn by human expert are taken as the reference images. The proposed method yields sensitivity values as high as 96.7%, which are better than the results reported in the literature.

Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation

Due to increasing number of diabetic retinopathy cases, ophthalmologists are experiencing serious problem to automatically extract the features from the retinal images. Optic disc (OD), exudates, and cotton wool spots are the main features of fundus images which are used for diagnosing eye diseases, such as diabetic retinopathy and glaucoma. In this paper, a new algorithm for the extraction of these bright objects from fundus images based on marker-controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps. The concept of the markers is used to modify the gradient before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. It is shown that the proposed method can yield an average sensitivity value of about 95%, which is comparable to those obtained by the known methods.

An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals

Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods.

A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy

The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.

Diabetic Retinopathy: A Quadtree Based Blood Vessel Detection Algorithm Using RGB Components in Fundus Images

Blood vessel detection in retinal images is a fundamental step for feature extraction and interpretation of image content. This paper proposes a novel computational paradigm for detection of blood vessels in fundus images based on RGB components and quadtree decomposition. The proposed algorithm employs median filtering, quadtree decomposition, post filtration of detected edges, and morphological reconstruction on retinal images. The application of preprocessing algorithm helps in enhancing the image to make it better fit for the subsequent analysis and it is a vital phase before decomposing the image. Quadtree decomposition provides information on the different types of blocks and intensities of the pixels within the blocks. The post filtration and morphological reconstruction assist in filling the edges of the blood vessels and removing the false alarms and unwanted objects from the background, while restoring the original shape of the connected vessels. The proposed method which makes use of the three color components (RGB) is tested on various images of publicly available database. The results are compared with those obtained by other known methods as well as with the results obtained by using the proposed method with the green color component only. It is shown that the proposed method can yield true positive fraction values as high as 0.77, which are comparable to or somewhat higher than the results obtained by other known methods. It is also shown that the effect of noise can be reduced if the proposed method is implemented using only the green color component.

Automated Detection of Anesthetic Depth Levels Using Chaotic Features with Artificial Neural Networks

Monitoring the depth of anesthesia (DOA) during surgery is very important in order to avoid patients’ interoperative awareness. Since the traditional methods of assessing DOA which involve monitoring the heart rate, pupil size, sweating etc, may vary from patient to patient depending on the type of surgery and the type of drug administered, modern methods based on electroencephalogram (EEG) are preferred. EEG being a nonlinear signal, it is appropriate to use nonlinear chaotic parameters to identify the anesthetic depth levels. This paper discusses an automated detection method of anesthetic depth levels based on EEG recordings using non-linear chaotic features and neural network classifiers. Three nonlinear parameters, namely, correlation dimension (CD), Lyapunov exponent (LE) and Hurst exponent (HE) are used as features and two neural network models, namely, multi-layer perceptron network (feed forward model) and Elman network (feedback model) are used for classification. The neural network models are trained and tested with single and multiple features derived from chaotic parameters and the performances are evaluated in terms of sensitivity, specificity and overall accuracy. It is found from the experimental results that the Lyapunov exponent feature with Elman network yields an overall accuracy of 99% in detecting the anesthetic depth levels.

Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks

Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.

Morphology approach for features extraction in retinal images for diabetic retionopathy diagnosis

In this paper, a novel approach for vessels extraction edge-based image segmentation is proposed. Vessels segmentation and extraction play an important role in supporting computer assistance for diagnosis of Diabetic Retinopathy (DR). Diabetic Retinopathy is a severe and widely spread eye disease. The algorithms to detect and extract vessels from retinal images are mainly based on morphological filtering and segmentation methods. We proposed an image segmentation algorithm by integrating mathematical morphological edge detector with TopHat technique. In this paper, theoretical backgrounds and procedure illustrations of the proposed algorithm are presented. Furthermore, the proposed algorithm has been evaluated on several images of publicly available database. The results are compared with those obtained by other known methods as well as with the golden images. It is shown that the proposed method can yield a specificity value as high as 82%, which is comparable to the results obtained by other known methods.