The modified probabilistic neural network as a nonlinear correlator detector

A nonlinear correlator detector for the detection of a signal class with some intra class variance is developed using the modified probabilistic neural network and the general regression neural network. An application, involving the detection of regular tone bursts transmitted over a poor and noisy radio channel subjected to fading, random noise and impulse noise effects, is used to show the effectiveness of the method as compared to a linear correlato

Non-linear quantisation effects in digital colour systems

This paper shows that quantisation of colour pixels, essential to digital imagery, causes non-linear correlation in the permitted values of chromaticity coordinates in linear-intensity CIE colour spaces. It follows that colour video and image coding schemes based on these colour spaces will suffer an inherent preference for certain chromaticitie

Combining data from different algorithms to segment the skin-air interface in mammograms

This paper presents a method for combining several different estimates of the mammographic skin-air interface in order to eliminate noise inherent to each individual segmentation algorithm. Given that each algorithm provides a binary mask of the breast, the first step is to isolate pixels adjacent to the skin-air interface. A final estimate of the skin-air interface for each point results from the combination of skin-air interface location data from each procedure. Data for each point is grouped as a set, upon which statistical operators, such as the elimination of outliers, are applied. Since the skin-air interface is a continuous line, data from prior points is also used as an estimate of points that follow. Results are evaluated in terms of success with the combination of two skin-air interface segmentation algorithms. The resulting `hybrid' technique overcomes several problems that beset each individual algorith

Neural network transient stability assessment of a single-machine system under asymmetrical fault conditions

The authors propose a neural network approach for transient stability assessment and for critical fault clearing time estimation for a single-machine system under asymmetrical fault conditions. They describe the back-propagation neural network configurations adopted and detail the different stages in the training process of the neural networks. Results obtained by applying the neural network approach to a single-machine system show that fast and accurate assessment of transient stability boundaries can be achieved but the approach requires further improvement for use in the estimation of critical fault clearing times

Simultaneous MAP estimation of inhomogeneity and segmentation of brain tissues from MR images

Intrascan and interscan intensity inhomogeneities have been identified as a common source of making many advanced segmentation techniques fail to produce satisfactory results in separating brains tissues from multi-spectral magnetic resonance (MR) images. A common solution is to correct the inhomogeneity before applying the segmentation techniques. This paper presents a method that is able to achieve simultaneous semi-supervised MAP (maximum a-posterior probability) estimation of the inhomogeneity field and segmentation of brain tissues, where the inhomogeneity is parameterized. Our method can incorporate any available incomplete training data and their contribution can be controlled in a flexible manner and therefore the segmentation of the brain tissues can be optimised. Experiments on both simulated and real MR images have demonstrated that the proposed method estimated the inhomogeneity field accurately and improved the segmentation

Improving three layer neural net convergence

The authors investigate the relationship between three layer feed forward back-propagation nets (using the terminology of Rumelhart et al., see Nature vol.323, p.533 et seq., 1986) and the committee net of (Nilsson, see Learning Machines, McGraw-Hill, 1956), and show that a simple modification to the algorithm of the latter makes them, in respect of their power to classify data sets, equivalent. Two algorithms may, however, be equivalent in power but differ greatly in their practicality. In the second part the authors conduct some experiments in order to determine whether the modified committee algorithm can compete with back-propagation in a variety of applications. It is found that the committee algorithm (a) is about ten times as fast in some applications and (b) is much less prone to getting trapped in local minima. The theoretical interest in the paper stems from the ease of analysing the committee algorithm together with the equivalence. The experimental interest is that this method of speeding up back-propagation may be used with other improvements to reduce training times in some application

Applying neural networks to colour image data compression

A self-organizing neural network is used to achieve color image segmentation and image data compression, with an adaptive codebook for faster training. Neural network architectures are well-suited to high speed processing because they are massively parallel. By adding an external threshold decision, a compression network can build its codebook adaptively and therefore speed the compression process. A 24-bit color image is compressed to 6.39 bit with virtually no visual degradation

A semi-supervised map segmentation of brain tissues

This paper presents a method for semi-supervised MAP (maximum a-posterior probability) segmentation of brain tissues where labelled data are available for either all types of tissues or only a few types of tissues possibly at different levels of quality. The proposed MAP segmentation takes supervised and unsupervised segmentation as its two special cases where, respectively, quality labelled data is available or there is no labelled data at all. Experiments on real MR images have shown that the proposed method improved the segmentation accuracy substantially with only a few labelled data in comparison with both fully supervised method with the same labelled data set and unsupervised method

Syntactic recognition of common cardiac arrhythmias

Outlines a syntactic approach to the recognition of common cardiac arrhythmias within a single ambulatory ECG trace. This methodology essentially involves the annotation of an electrocardiogram trace in terms of the syntax primitives and the subsequent parsing of these annotations into various syntactic forms that describe their appropriate arrhythmia. The syntax primitives, which the authors collectively term arrlets, are a set of curves, which are modelled as a series expansion of orthonormal hermite basis functions. By using as features the parameters of this model, a probabilistic neural network is then employed to detect the occurrences of arrlets within an ECG trace. The approach was evaluated using data from the MIT-BIH arrhythmia databas