Fourier and spectral envelope analysis of medically important bacterial and fungal sequences

In this paper, we introduce the Fourier and spectral envelope analysis methods to analyze some biomolecular sequences, particularly medically important bacteria and fungi DNA sequences, to get their interesting frequency properties. Fourier analysis includes mapping character strings into numerical sequences, calculating spectra of DNA sequences and setting and solving optimization problem in order to construct a powerful predictor of exons along the long DNA sequences. The spectral envelope analysis makes use of spectral envelope for analyzing periodicities in categorical-valued time series and it is useful for the scaling of non-numeric sequences. The spectral envelope analysis utilizes optimization procedure to improve upon traditional analysis performance in distinguishing coding from non-coding regions in DNA sequences. The two approaches greatly facilitate the understanding of local nature, structure and function of biomolecular sequences. They also provide useful techniques to combine bioinformatics analysis with modern computer power to quickly search for diagnostic patterns within long sequences.published_or_final_versio

Visual evoked potential estimation by eigendecomposition

In this paper an eigendecomposition method is presented to estimate evoked potentials (EP). Taking into account of the characteristic of evoked potentials, the method uses two observations both of which contain desired EP signal and undesired EEG signal. If the desired and undesired signal are uncorrelated (i.e. they are orthgonal) and the signal-to-noise-ratios (SNR) of each observations are different, we can use the eigendecomposition method to separate EP signal from EEG. Visual evoked potentials (VEP) of humans have been estimated and good results obtained by this method.published_or_final_versio

A time domain binaural model based on spatial feature extraction for the head-related transfer function

A complex-valued head-related transfer function (HRTF) can be represented as a real-valued head-related impulse response (HRIR). The interaural time and level cues of HRIRs are extracted to derive the binaural model and also to normalize each measured HRIR. Using the Karhunen–Loeve expansion, normalized HRIRs are modeled as a weighted combination of a set of basis functions in a low-dimensional subspace. The basis functions and the space samples of the weights are obtained from the measured HRIR. A simple linear interpolation algorithm is employed to obtain the modeled binaural HRIRs. The modeled HRIRs are nearly identical to the measured HRIRs from an anesthetized live cat. Typical mean-square errors and cross-correlation coefficients between the 1816 measured and modeled HRIRs are 1% and 0.99, respectively. The real-valued operations and linear interpolating in the model are very effective for speeding up the model computation in real-time implementation. This approach has made it possible to simulate real free-field signals at the two eardrums of a cat via earphones and to study the neuronal responses to such a virtual acoustic space (VAR). ©1997 Acoustical Society of America.published_or_final_versio

Generation of noise sequences with desired non-Gaussian distribution and covariance

It is necessary to generate a noise sequence in the simulation of the communication system and signal processing. In the design of some practical system such as radar system, we Must generate a stationary noise sequence with a specified non-Gaussian probability density function and a desired power spectrum to test the performance of the system. For this purpose, this paper presents a method by which such a clutter sequence can be generated. The examples are also demonstrated to show the effectiveness of the proposed method.published_or_final_versio

B-spline snakes in two stages

In using Snake algorithms, the slow convergence speed is due to the large number of control points to be selected, as well as difficulties in setting the weighting factors that comprise the internal energies of the curve. Even in using the B-Spline snakes, splines cannot be fitted into the corner of the object completely. In this paper, a novel two-stage method based on B-Spline Snakes is proposed. It is superior both in accuracy and fast convergence speed over previous B-Spline Snakes. The first stage reduces the number of control points using potential function V(x,y) minimization. Hence, it allows the spline to quickly approach the minimum energy state. The second stage is designed to refine the B-Spline snakes based on the node points of the polynomials without knots. In other words, an elasticity spline is controlled by node points where knots are fixed. Simulation and validation of results are presented. Compared to the traditional B-Spline snakes, better performance was achieved using the method proposed in this paper.published_or_final_versio

A method for generating non-Gaussian noise series with specified probability distribution and power spectrum

It is necessary to generate a non-Gaussian noise sequence in the simulation of the communication system and signal processing. In the design of some practical system such as radar system, we must generate a stationary noise sequence or clutter series with a specified non-Gaussian probability density function and a desired power spectrum for the purpose of testing the performance of the system. This paper presents a novel method by which such a non-Gaussian noise sequence can be generated. The simulation results are also demonstrated to show the effectiveness of the proposed method.published_or_final_versio

A fast deformable region model for brain tumor boundary extraction

We present a modified deformable region model for extraction of a brain tumor boundary in 2D MR images. The deformable region model tolerates a rough initial plan when compared with the active contour model. However, it is time consuming to compute and compare the gray level distribution of the object and all its boundary points. Using a point sampling technique, the number of boundary point processed is greatly reduced. Performance of our modified deformable region model is evaluated on a MR image. The modified model is fast while similar results are obtained.published_or_final_versio

3D reconstruction of coronary artery using biplane angiography

In this paper, we present a new method for the 3D reconstruction and visualization of coronary arteries in biplane angiography. The proposed method performs direct reconstruction of 3D coronary artery pathways without computing the 2D or 3D vessel centerlines. A front propagation algorithm is used to reconstruct the coronary artery pathways in 3D space. Starting from one or more 3D points, the front is expanded with a propagation speed controlled by the combined image information from two 2D projections. Then the reconstructed 3D coronary artery pathways are smoothed to reflect the real situation of a smoothed vessel surface. As shown in the experiment result, the coronary artery can be successfully reconstructed from two projections of biplane angiograms.published_or_final_versio

Multiclass segmentation based on generalized fuzzy Gibbs random fields

The model of Gibbs random fields is widely applied to Bayesian segmentation due to its best property of describing the spatial constraint information. However, the general segmentation methods, whose model is defined only on hard levels but not on fuzzy set, may come across a lot of difficulties, e.g., getting the unexpected results or even nothing, especially when the blurred or degraded images are considered. In this paper, two multiclass approaches, based on the model of piecewise fuzzy Gibbs random fields (PFGRF) and that of generalized fuzzy Gibbs random fields (GFGRF) respectively, are presented to address these difficulties. In our experiments, both magnetic resonance image and simulated image are implemented with the two approaches mentioned above and the classical 'hard' one. These three different results show that the approach of GFGRF is an efficient and unsupervised technique, which can automatically and optimally segment the images to be finer.published_or_final_versio

Adaptive thresholding by variational method

When using thresholding method to segment an image, a fixed threshold is not suitable if the background is uneven. Here, we propose a new adaptive thresholding method using variational theory. The method requires only one parameter to be selected and the adaptive threshold surface can be found automatically from the original image.published_or_final_versio