Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours

Thalamus is an important neuro-anatomic structure in the brain. In this paper, an automated method is presented to segment thalamus from magnetic resonance images (MRI). The method is based on a discrete dynamic contour model that consists of vertices and edges connecting adjacent vertices. The model starts from an initial contour and deforms by external and internal forces. Internal forces are calculated from local geometry of the model and external forces are estimated from desired image features such as edges. However, thalamus has low contrast and discontinues edges on MRI, making external force estimation a challenge. The problem is solved using a new algorithm based on fuzzy C-means (FCM) unsupervised clustering, Prewitt edge-finding filter, and morphological operators. In addition, manual definition of the initial contour for the model makes the final segmentation operator-dependent. To eliminate this dependency, new methods are developed for generating the initial contour automatically. The proposed approaches are evaluated and validated by comparing automatic and radiologist's segmentation results and illustrating their agreement.     

Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events

With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the field-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.      

Successively Structured Gaussian Two-terminal Source Coding

Multiterminal source coding refers to separate encoding and joint decoding of multiple correlated sources. Joint decoding requires all the messages to be decoded simultaneously which is exponentially more complex than a sequence of single-message decodings. Inspired by previous work on successive coding, we apply the successive Wyner-Ziv coding, which is inherently a low complexity approach of obtaining a prescribed distortion, to the two-terminal source coding scheme. First, we consider 1-helper problem where one source provides partial side information to the decoder to help the reconstruction of the main source. Our results show that the successive coding strategy is an optimal strategy in the sense of achieving the rate-distortion function. By developing connections between source encoding and data fusion steps, it is shown that the whole rate-distortion region for the 2-terminal source coding problem is achievable using the successive coding strategy. Comparing the performance of the sequential coding with the performance of the successive coding, we show that there is no sum-rate loss when the side information is not available at the encoder. This result is of special interest in some applications such as video coding where there are processing and storage constraints at the encoder. Finally, we provide an achievable rate-distortion region for the m-terminal source coding.

Effect of Number of Coupled Structures on the Segmentation of Brain Structures

This paper reports the effect of the coupling information on the performance of model-based segmentation of the brain structures from magnetic resonance images (MRI). We have developed a three-dimensional, nonparametric, entropy-based, and multi-shape method that benefits from coupling of the shapes. The proposed method uses principal component analysis (PCA) to develop shape models that capture structural variability and integrates geometrical relationship among different structures into the algorithm by coupling them (limiting their independent deformations). At the same time, to allow variations of the coupled structures, it registers each structure individually when building the shape models. It defines an entropy-based energy function which is minimized using quasi-Newton algorithm. Probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, analytical derivatives are used for maximum speed and accuracy. Sample results are given for the segmentation of caudate, thalamus, putamen, pallidum, hippocampus, and amygdala illustrating superior performance of the proposed method compared to the most similar method in the literature. The similarity of the results obtained by the proposed method with the expert segmentation is 4% to 12% higher than that of the most similar method. Experimental studies show that the proposed coupling method, which regulates shape variability during segmentation, improves accuracy of the results of the proposed method by 6% and those of the other method by 1%. In addition, the more the structures are used in the coupling process, the more accurate the results are obtained.     

Noise reduction, smoothing and time interval segmentation of noisy signals using an energy optimisation method

Noise reduction and time interval segmentation of a noise-contaminated piecewise continuous signal is considered by the authors as a non-linear optimisation problem. The mathematical framework of this method is presented both in continuous-time and discrete-time domains. The smoothed signal and segmented time intervals of the original noisy signal are calculated as an optimised solution for an energy functional. An algorithm similar to the level set method is developed to find the optimised solution. In this algorithm, the discontinuity points separating consecutive continuous signals are preserved while the noise is reduced. Therefore this method fundamentally exhibits a better performance compared with a traditional low-pass filter suppressing high frequency components, including discontinuity points. The results also demonstrate a better quality in noise reduction in comparison to the median and Gaussian filters.     

Simple precoding algorithms using Gram–Schmidt orthonormalization process for multiuser relay communications with optimized power allocation

This paper proposes a new simple precoding solution based on the Gram–Schmidt orthonormalization to be used at the relay station of a multirelay wireless networks where the different mobile stations belong to the same network, in order to mitigate the multiuser interference at each mobile station. The strength of this method is that it only requires the knowledge of all channel impulse responses from a given relay to all the mobile stations. In other words, to compute its precoding vectors, each relay does not need to know the channel impulse responses of the channels of other relays. Unlike the centralized reference method where each mobile station benefits from the same diversity gain, using this algorithm, some mobile stations will improve their diversity gain at the cost of a loss in the diversity gain of other users. This constitutes a simple solution to supply different qualities of service in the case of a multiservices network. Furthermore, this work proposes an optimized power allocation between the relays. Analytical and accurate performance analyses for the different studied contexts are provided.     

Positive L_1 Observer Design for Positive Switched Systems

This paper investigates the problem of \(L_1\) observer design for positive switched systems. Firstly, a new kind of positive \(L_1\) observer is proposed for positive switched linear delay-free systems with observable and unobservable subsystems. Based on the average dwell time approach, a sufficient condition is proposed to ensure the existence of the positive \(L_1\) observer. Under the condition obtained, the estimated error converges to zero exponentially, and the \(L_1\) -gain from the disturbance input to the estimated error is less than a prescribed level. Then the proposed design result is extended to positive switched systems with mixed time-varying delays, where the mixed time-varying delays are presented in the form of discrete delay and distributed delay. Finally, two numerical examples are given to demonstrate the feasibility of the obtained results.     

Process Noise Parameters of Beamforming Green Nodes

Signals arrive out of phase at the intended receiver from collaborative beamforming (CB) nodes due to the instability in the output frequency signals of the universal software radio peripheral's (USRP) local oscillator (LO). These nodes including the target must synchronize their oscillator frequencies for coherent signal reception. In order to do this, frequencies and phases of the signals should be estimated in software defined radio (SDR) and smoothen with nonlinear filters such as the extended Kalman filter (EKF). The process noise parameters of the NI USRP-2920 nodes will have to be calculated and used with the EKF process noise covariance matrix. These nodes are green communication hardware devices where most of the hardware units are now software defined. This article uses the direct spectrum method to obtain the phase noise values at various frequency offsets of the NI USRP-2920 in order to calculate the power spectral density of fractional frequency fluctuation. By applying the power-law noise model to this obtained value, the generated white frequency noise and random walk frequency noise values are q1=1.9310-21 and q2=5.8610-18, respectively.