Medical Image Segmentation Using Multifractal Analysis

Image segmentation plays a key role in image analysis processes. The operations performed on a segmented image tend to affect it differently than if they were performed on the original image; therefore, segmenting an image can show radically different results from the original image and successfully doing so can yield features and other important information about the image. Proper image analysis is of high importance to the medical community as accurately classifying different conditions and diseases can be facilitated with excellent patient imaging. Multifractal analysis can be leveraged for performing texture classification and image segmentation. In this paper, we propose fusion-based algorithms utilizing multifractal analysis for medical image segmentation. We use two specific multifractal masks: square and quincunx. Our techniques show new insights by using methods such as histogram decomposition in conjunction with new techniques, such as fusion. By fusing different slope images, we can extract more features thus making our proposed algorithms more robust and accurate than traditional multifractal analysis techniques. These methods are further capable of reliably segmenting medical images by implementing multifractal analysis techniques in coordination with methods such as gaussian blurring and morphological operations. The resulting image can then be easily analyzed by medical professionals for diagnosing medical conditions. The outcomes show that the proposed algorithms extract dominant features that are more encompassing and powerful than classical techniques

Anomaly Prediction in Non-Stationary Signals using Neural Network Based Multi-Perspective Analysis

Abstract: A new technique for predicting anomalies in the near future of an observed signal is being presented. Before any data analysis can be performed on an observed signal, the signal's underlying pattern must be cleared. A wavelet de-noising scheme is used because it provides a better result compared to other de-noising algorithms and it is simple from a computational standpoint. Robust peak-finding algorithm is used to find smaller anomalies that appear frequently throughout the signal pattern. In addition to or in place of wavelet de-noising, other views of the signal may be generated for analysis. The generated perspectives are used as input to a feed-forward neural network that will predict the likelihood of an anomalous event occurring later in the signal. The neural network is trained using the Resilient Backpropagation of Errors (Rprop) supervised learning algorithm with data sets consisting of a mix of signals known to precede anomalous events as well as signals known to be free of significant anomalies. This paper provides a means of predicting large or abnormal events in signals such as seismograms, EKGs, EEGs, and other non-stationary signals. Our algorithm has been tested on a large collection of seismic and EKG (electrocardiogram) signals. The obtained accuracy as high as 70% with EKG signals and as high as 83% with seismic signals, when the test data is taken from within the same time frame as the training set. Though there was greater consistency found at a lower degree of accuracy for seismic signals

Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods

Cyber Physical Systems (CPSs) have become widely popular in recent years, and their applicability have been growing exponentially. A CPS is an advanced system that incorporates a computation unit along with a hardware unit, allowing for computing processes to interact with the physical world. However, this increased usage has also led to the security concerns in them, as they allow potential attack vendors to exploit the possibilities of committing misconduct for their own benefit. It is of paramount importance that these systems have comprehensive security mechanisms to mitigate these security threats. A typical attack vector for a CPS is malicious data supplied by compromised sensors that are part of the CPSs. To combat this attack vector, many systems are secured through fault tolerance, including methods such as checkpointing to recover the system. Looking at the diverse nature of attacks and their ever growing complexities, traditional security approaches may not counter them efficiently, which creates a vacuum to be filled with sophisticated state-of-the-art techniques. In this paper, Deep Learning methods such as autoencoders, and Support Vector Machines are proposed to secure CPSs against these attacks. The networks in these applied methods are trained with a normal data profile devoid of any malicious data. Data collected from the system’s sensors at specified intervals is used to form a data series and input to the neural networks. The networks compare and analyze new data to the normal profile to detect anomalies, if there is any. In the presence of anomalous data, the networks generate corrective action(s) for these sensors and the physical states they are recording. Through detection of anomalies, effective security of CPSs may be improved in addition to providing protection for the sensors. Moreover, the proposed method of securing CPSs opens up the possibility of further research by showcasing the applicability of neural networks in securing CPSs.peerReviewe