Abstract

Brain is the most complex organ amongst all the systems in the human body. The study of the electrical signals produced by neural activities of the human brain is called Electroencephalogram. Electroencephalogram (EEG) is a technique, which is used to identify the neurological disorder of the brain. Epilepsy is one of the most common neurological disorders of brain. Epilepsy needs to be detected efficiently using required EEG feature extraction, such as mean, standard deviation, median, entropy, kurtosis, skewness, etc. The framework of proposed technique is an efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. Extraction of the options is done by applying separate ripple remodel (DWT) so as to decompose the graph signals into sub-bands. These options, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA) The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM), Neural Network Analysis (NNA), and K-Nearest Neighbour (K-NN). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and k-NN. It has been found that the computation time of NNA classifier is lesser than SVM and k-NN to provide 100% accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NNA classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.

Abstract

MRI (Magnetic Resonance Imaging) images suffer from noise due to long acquisition time and high resolution is needed for post processing the noisy image for further analysis like segmentation and registration, which is required for the clinical practice for better analysis of disease detection. In MRI, the noise from the acquisition device follows the complex noise distribution. For better analysis of MRI data, the complex noisy data follows the magnitude data which obeys Rician Distribution. So the magnitude data need to be denoised rather than the complex data in which, it suffers from phase artifacts. Unfortunately, the presence of Rician noise in these images affect edges and fine details, which limit the contrast resolution and make diagnostic more difficult. The key idea from using diffusion tensor is to adapt the flow diffusion towards the local orientation by applying anisotropic diffusion along the coherent structure direction of interesting features in the image, which enhances the quality and improves the Signal-to-Noise Ratio (SNR) and edge preservation of anatomical details. In this paper, the filtering parameter is automatically chosen from the estimated standard deviation of noise using standard Linear Minimum Mean Square Error (LMMSE) method in which, it improves the convergence rate of diffusion along with preservation of anatomical details which is required for clinical diagnosis. Also, matrix extension of the scalar diffusion filter, is proposed which automatically adapts to the local structure of the image and the level of noise along with the coherent structure direction of interesting features in the image, which improves the SNR and edge preservation of anatomical details.

In order to illustrate the effective performance of the algorithm, some experimental results are presented on synthetic and clinical images. The proposed filter shows better preservation of edges and efficient noise reduction at both low SNR and high SNR levels.

Abstract

Natural User Interface system is adopted by using the technology available for the purpose of controlling the devices as well as the systems used in the home automatically. For making the Natural User Interface system easy, the control of all the devices should be brought into one place, which makes home incredible. A variety of home devices can be controlled with the help of a Natural User Interface system.

Natural User Interface is built upon aiming to develop the most simple yet, highly efficient form of User interface having touch, voice, and human movements as the key elements. The traditional switches are upgraded with aid of Capacitive Touch Technology. Add-on like Voice is processed with the help of Voice Recognition (VR) module and Gestures are processed with Open Computer Vision Python (CV-Python) using Convex Hull algorithm. These controls are used in order to make it easy for any category of people to use the system with very minimal efforts. It has wide variety of applications and it can also be implemented in Industrial, Educational, Medical, Military Applications.

Abstract

The major aim of compression is to remove insignificance attached in redundancy of image-information in order to store or transmit information efficiently. Compression is a way of writing secret information using less fragments than an original picture would use. The Discrete-Wavelet-Transform (DWT) is comparatively recent compared to Discrete-Cosine Transform (DCT) with necessary properties. It achieves this with the redundancy problem of only for 2-dimensional signals that are considerably not up to the non-decimated DCT. A new compression technique supported DWT and Set Partitioning In Hierarchical Trees (SPIHT) has been discussed. Approximate shift is not varied, smart directional-property process influences the properties of DWT making it a better object for compression. A varied SPIHT process is introduced to expand its efficiency for compression. To increase security of the hosts and raise the effectiveness of the image, the DWT and SPIHT methods have been employed.

Abstract

Many individuals are not interested in health and find self-tracking to be an alien concept. Health is still perceived as the responsibility of physicians, and health-related information is thought to be deterministic, negative, and unwanted. Ophthalmic Research has given better understanding of the “sight-threatening disease / vision loss” processes, is now opening up new avenues in the line of prevention and treatment. Diabetes Retinopathy (DR) only affects people, who have diabetes for a longer period of time and result in blindness or loss of vision. This review would certainly focus on importance behind the research motivation on DR’s early intervention, its niceties, role, and potential use of Big Data Analytics (Strategic Technology). Hadoop's core components [like HDFS (HIPI-Hadoop Image Processing Interface - Executable Algorithms), MapReduce and Yet Another Resource Negotiar (YARN) and its Ecosystem (Hive, Hbase, Pig, etc.,) are employed everywhere in the corners of medical domain. Since, Healthcare constituents and researchers are being impacted by big data arrival, from which better treatment efficiency in the prediction task on DR would be possible by these players.