Showing papers by "Ioannis Pitas published in 1994"

Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks

Abstract: Two approaches for ultrasonic image processing are examined. First, signal-adaptive maximum likelihood (SAML) filters are proposed for ultrasonic speckle removal. It is shown that in the case of displayed ultrasound (US) image data the maximum likelihood (ML) estimator of the original (noiseless) signal closely resembles the L/sub 2/ mean which has been proven earlier to be the ML estimator of the original signal in US B-mode data. Thus, the design of signal-adaptive L/sub 2/ mean filters is treated for US B-mode data and displayed US image data as well. Secondly, the segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of the learning vector quantizer (L/sub 2/ LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L/sub 2/ mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed L/sub 2/ LVQ NN are studied. L/sub 2/ LVQ is combined with signal-adaptive filtering in order to allow preservation of image edges and details as well as maximum speckle reduction in homogeneous regions. >

Multichannel L filters based on marginal data ordering

Abstract: The extension of single-channel nonlinear filters whose output is a linear combination of the order statistics of the input samples to the multichannel case is presented in the paper. The subordering principle of marginal ordering (M-ordering) is used for multivariate data ordering. Assuming a multichannel signal corrupted by additive white multivariate noise whose components are generally correlated, the coefficients of the multichannel L filter based on marginal ordering are chosen to minimize the output mean-squared-error (MSE) either subject to the constraints of unbiased or location-invariant estimation or without imposing any constraint. Both the case of a constant multichannel signal corrupted by additive white multivariate noise as well as the case of a nonconstant signal is considered. In order to test the performance of the designed multichannel marginal L filters, long-tailed multivariate distributions are required. The derivation and design of such a distribution, namely, the Laplacian (biexponential) distribution that belongs to Morgenstern's family in the 2D case is discussed. It is shown by simulation that the proposed multichannel L filters perform better than other multichannel nonlinear filters such as the vector median, the marginal /spl alpha/-trimmed mean, the marginal-median, the multichannel modified trimmed mean, the multichannel double-window trimmed mean, and the multivariate ranked-order estimator R/sub E/ proposed elsewhere as well as their single-channel counterparts. >

Multichannel Wiener filters in color image restoration

Abstract: Deals with the design of multichannel Wiener filters both in the spatial and frequency domain FIR and IIR Wiener filters are presented Color image restoration based on multi-channel autoregressive (AR) image modeling is examined Detailed discussions on the use of a multichannel Wiener filter in color image restoration incorporating the interchannel correlations and computer simulations are presented also >

Optimization and adaptation of discrete-valued digital filter parameters by simulated annealing

Abstract: A novel method is presented for the optimal choice of discrete-valued digital filter parameters (e.g. digital filter length). The method proposed is a "running" version of the simulated annealing algorithm. Its convergence properties are studied. An application of the proposed method to the adaptation of the median filter length is presented. Simulation results prove the convergence of proposed technique. It is also proven that simulating annealing algorithms give better results than iterative improvement (hill climbing) algorithms. >

Multichannel distance filters

Abstract: Two nonlinear digital filters for multichannel signal processing are presented in this paper. A method for the approximate calculation of output mean and variance for one of these filters is proposed. The results of simulations, showing great advantages of the described filters, are also presented. >

Segmentation-based L-filtering of speckle noise in ultrasonic images

Abstract: In this paper, we introduce segmentation-based L-filters, that is, filtering processes combining segmentationand (nonadaptive) optimum L-filtering, and we use them for the suppression of speckle noise in ultrasonic (US)images. With the aid of a suitable modification of the Learning Vector Quantizer (LVQ) self-organizing neuralnetwork, the image is segmented in regions of approximately homogeneous first-order statistics. For each suchregion a minimum mean-squared error (MMSE) L-filter is designed on the basis of a multiplicative noise modelby using the histogram of grey values as an estimate of the parent distribution of the noisy observations and asuitable estimate of the original signal in the corresponding region. Thus, we obtain a bank of L-filters that arecorresponding to and are operating on different image regions. Simulation results on a simulated US B-mode imageof a tissue mimicking phantom are presented which verify the superiority of the proposed method as compared toa number of conventional filtering strategies in terms of a suitably defined signal-to-noise ratio (SNR) measure anddetection theoretic performance measures.

Robust estimation for radial basis functions

Abstract: This paper presents a new learning algorithm for radial basis functions (RBF) neural network, based on robust statistics. The extention of the learning vector quantizer for second order statistics is one of the classical approaches in estimating the parameters of a RBF model. The paper provides a comparative study for these two algorithms regarding their application in probability density function estimation. The theoretical bias in estimating one-dimensional Gaussian functions are derived. The efficiency of the algorithm is shown in modelling two-dimensional functions. >

A class of order statistics learning vector quantizers

Abstract: A novel class of Learning Vector Quantizers (LVQs) based on multivariate order statistics is proposed in order to overcome the drawback that the estimators for obtaining the reference vectors in LVQ do not have robustness either against erroneous choices for the winner vector or against the outliers that may exist in vector-valued observations. The performance of the proposed variants of LVQ is demonstrated by experiments. In the case of marginal median LVQ, its asymptotic properties are derived as well. >

Discrete multichannel orthogonal transforms

Abstract: This paper presents a novel approach to the Fourier analysis of multichannel time series. Orthogonal matrix functions are introduced and are used in the definition of multichannel Fourier series of continuous-time periodic multichannel functions. Orthogonal transforms are proposed for discrete-time multichannel signals as well. The discrete-time one-dimensional multichannel transforms proposed in this paper are related to two-dimensional single-channel transforms, notably to the discrete Fourier transform and to the discrete Hartley transform. Therefore, fast algorithms for their computation can be easily constructed. Simulations on the use of discrete multichannel transforms on color image compression have also been performed. >

Median learning vector quantizer

Abstract: In this paper we propose a novel class of learning vector quantizers (LVQ) based on multivariate data ordering. Linear LVQ is not the optimal estimator for non-Gaussian multivariate data distributions. Furthermore, it is not robust either in the case of outliers or in the case of erroneous decisions. The novel LVQs use multivariate ordering in order to obtain location estimators that are robust and that provide superior and, in certain cases, optimal performance for non-Gaussian multivariate distributions. A special case of the novel LVQ class is the marginal median LVQ (MM LVQ), which uses the marginal median as multivariate estimator of location.

A fast implementation of two-dimensional weighted median filters

Abstract: This paper deals with the implementation of a fast algorithm for two-dimensional weighted median filtering. Because of the vast amount of data that must be handled, the development of fast algorithms is very important. A fast running algorithm for weighted median filtering, which is based on using a histogram and updating it, is proposed. Experimental results prove the superiority of the proposed algorithm over that of finding the weighted median either by sorting with the Quick Sort or by selecting the r-th order statistic.

Adaptive LMS L-filters for smoothing noisy images

Abstract: Several adaptive LMS L-filters, both constrained and unconstrained ones, are developed for noise suppression in images and being compared in this paper. First, the location-invariant LMS L-filter for a nonconstant signal corrupted by zero-mean additive white noise is derived. Subsequently, the normalized and the sign LMS L-filters are studied. It is shown that both these filters turn to be identical for a certain choice of the adaptation step-size. A modified LMS L-filter with nonhomogeneous step-sizes is also proposed in order to accelerate the rate of convergence of the adaptive L-filter. Finally, a signal-dependent adaptive filter structure is developed to allow a separate treatment of the pixels that are close to the edges from the pixels that belong to homogeneous image regions.

Performance Analysis of a Morphological Voronoi Tessellation Algorithm

Abstract: The goal of the following analysis is to estimate the computational complexity of the Voronoi tessellation of an image performed with an algorithm based on mathematical morphology. The morphological dilation operation is used to implement a set of distance functions (distance transformations) with a variety of structuring elements that approximate distance metrics, such as the Euclidean, city block and chessboard distances. The analysis has been performed for 2-D morphological Voronoi tessellation.

Two-dimensional vector median filters on mesh connected computers

Abstract: This paper presents a parallel algorithm for the calculation of vector median filters on mesh connected computers. A straightforward algorithm to find the vector median requires the computation of the sum of the distances (norms) of each vector to all other vectors in the filter window. By taking into account the fact that adjacent filter windows share common input samples, the computation of the common norms of adjacent windows could be done only once. In the proposed algorithm, a 2-D signal is assumed to be stored in a SIMD mesh connected computer of the same size. According to this scheme, each processing element (PE) computes some norms and the rest ones, that are required for the selection of the vector median, are received from its neighbouring PEs via the local communication channels. >

Application of directional statistics in vector direction estimation

Abstract: Vector direction estimation can be very important in applications like hue color component filtering or motion direction estimation from noisy motion vector fields. Vector representation and manipulation in polar coordinates greatly facilitates the accomplishment of the previous task. Based on angular estimators of location, the authors introduce a number of "circular" filters, i.e. filters for angular input data. These filters include circular mean, median and a-trimmed mean filters. Emphasis is given to a circular median filter for which the approximate output pdf as well as other interesting properties are derived. The effectiveness of angular estimators of location in noise filtering is studied in simulations involving color images. Simulation results clearly indicate that circular filters can be used effectively to remove noise when the estimation of color hue is of primary importance. >

Cellular LMS L-filters for noise suppression in still images and image sequences

Abstract: A novel class of nonlinear adaptive L-filters based on cellular neural networks topology is presented. Like cellular neural systems and cellular automata as well, processing nodes, called cells, communicate with each other directly only through its nearest neighbors exchanging information. Each cell is an adaptive LMS L-filter. The proposed filters share the best features of both adaptive filters and cellular neural network topologies; their adaptive structure tracks image nonstationarities and their local interconnection feature makes it suitable for VLSI implementation. Cellular adaptive LMS L-filters are suited for high-speed parallel adaptive image filtering. Some interesting applications to image and image sequence filtering are demonstrated. >

Multichannel L-filter design based on marginal data ordering

Abstract: We address the design of multichannel L-filters based on marginal data ordering using the mean-squared-error as fidelity criterion. Design procedures subject to the constraints of unbiased or location-invariant estimation or without imposing any constraint are discussed. It is shown by simulations that the proposed multichannel L-filters perform better than other multichannel nonlinear filters such as the vector median, the marginal alpha-trimmed mean, the marginal median, the multichannel modified trimmed mean and the multichannel double-window trimmed mean, the multivariate ranked-order estimators as well as their single-channel counterparts. >