Biomedical subjects

Kenneth E Barner

Publications and source records attributed to Kenneth E Barner.

7 recordsLinked to original sources

PubMed2006

Improved optimization of soft-partition-weighted-sum filters and their application to image restoration.

Soft-partition-weighted-sum (Soft-PWS) filters are a class of spatially adaptive moving-window filters for signal and image restoration. Their performance is shown to be promising. However, optimization of the Soft-PWS filters has received only limited attention. Earlier work focused on a stochastic-gradient method that is computationally prohibitive in many applications. We describe a novel radial basis function interpretation of the Soft-PWS filters and present an efficient optimization procedure. We apply the filters to the problem of noise reduction. The experimental results show that the Soft-PWS filter outperforms the standard partition-weighted-sum filter and the Wiener filter.

Algorithms↗

PubMed2006

The fuzzy transformation and its applications in image processing.

The spatial and rank (SR) orderings of samples play a critical role in most signal processing algorithms. The recently introduced fuzzy ordering theory generalizes traditional, or crisp, SR ordering concepts and defines the fuzzy (spatial) samples, fuzzy order statistics, fuzzy spatial indexes, and fuzzy ranks. Here, we introduce a more general concept, the fuzzy transformation (FZT), which refers to the mapping of the crisp samples, order statistics, and SR ordering indexes to their fuzzy counterparts. We establish the element invariant and order invariant properties of the FZT. These properties indicate that fuzzy spatial samples and fuzzy order statistics constitute the same set and, under commonly satisfied membership function conditions, the sample rank order is preserved by the FZT. The FZT also possesses clustering and symmetry properties, which are established through analysis of the distributions and expectations of fuzzy samples and fuzzy order statistics. These properties indicate that the FZT incorporates sample diversity into the ordering operation, which can be utilized in the generalization of conventional filters. Here, we establish the fuzzy weighted median (FWM), fuzzy lower-upper-middle (FLUM), and fuzzy identity filters as generalizations of their crisp counterparts. The advantage of the fuzzy generalizations is illustrated in the applications of DCT coded image deblocking, impulse removal, and noisy image sharpening.

Algorithms↗

PubMed2006

Optimization of partition-based Weighted Sum filters and their application to image denoising.

Partition-based Weighted Sum (P-WS) filtering is an effective method for processing nonstationary signals, especially those with regularly occurring structures, such as images. P-WS filters were originally formulated as Hard-partition Weighted Sum (HP-WS) filters and were successfully applied to image denoising. This formulation relied on intuitive arguments to generate the filter class. Here we present a statistical analysis that justifies the use of weighted sum filters after observation space partitioning. Unfortunately, the HP-WS filters are nondifferentiable and an analytical solution for their global optimization is therefore difficult to obtain. A two-stage suboptimal training procedure has been reported in the literature, but prior to this research no evaluation on the optimality of this approach has been reported. Here, a Genetic Algorithm (GA) HP-WS optimization procedure is developed that, in simulations, shows that the simpler two-stage training procedure yields near optimal results. Also developed in this paper are Soft-partition Weighted Sum (SP-WS) filters. The SP-WS filters utilize soft, or fuzzy, partitions that yield a differentiable filtering operation, enabling the development of gradient-based optimization procedures. Image denoising simulation results are presented comparing HP-WS and SP-WS filters, their optimization procedures, and wavelet-based image denoising. These results show that P-WS filters, in general, outperform traditional and wavelet-based image filters, and SP-WS filters utilizing soft partitioning not only allow for simple optimization, but also yields improved performance.

Algorithms↗

PubMed2006

Quadratic weighted median filters for edge enhancement of noisy images.

Quadratic Volterra filters are effective in image sharpening applications. The linear combination of polynomial terms, however, yields poor performance in noisy environments. Weighted median (WM) filters, in contrast, are well known for their outlier suppression and detail preservation properties. The WM sample selection methodology is naturally extended to the quadratic sample case, yielding a filter structure referred to as quadratic weighted median (QWM) that exploits the higher order statistics of the observed samples while simultaneously being robust to outliers arising in the higher order statistics of environment noise. Through statistical analysis of higher order samples, it is shown that, although the parent Gaussian distribution is light tailed, the higher order terms exhibit heavy-tailed distributions. The optimal combination of terms contributing to a quadratic system, i.e., cross and square, is approached from a maximum likelihood perspective which yields the WM processing of these terms. The proposed QWM filter structure is analyzed through determination of the output variance and breakdown probability. The studies show that the QWM exhibits lower variance and breakdown probability indicating the robustness of the proposed structure. The performance of the QWM filter is tested on constant regions, edges and real images, and compared to its weighted-sum dual, the quadratic Volterra filter. The simulation results show that the proposed method simultaneously suppresses the noise and enhances image details. Compared with the quadratic Volterra sharpener, the QWM filter exhibits superior qualitative and quantitative performance in noisy image sharpening.

Algorithms↗

PubMed2006

Fuzzy rank LUM filters.

The rank information of samples is widely utilized in nonlinear signal processing algorithms. Recently developed fuzzy transformation theory introduces the concept of fuzzy ranks, which incorporates sample spread (or sample diversity) information into the sample ranking framework. Thus, the fuzzy rank reflects a sample's rank, as well as its similarity to the other sample (namely, joint rank order and spread), and can be utilized to improve the performance of the conventional rank-order-based filters. In this paper, the well-known lower-upper-middle (LUM) filters are generalized utilizing the fuzzy ranks, yielding the class of fuzzy rank LUM (F-LUM) filters. Statistical and deterministic properties of the F-LUM filters are derived, showing that the F-LUM smoothers have similar impulsive noise removal capability to the LUM smoothers, while preserving the image details better. The F-LUM sharpeners are capable of enhancing strong edges while simultaneously preserving small variations. The performance of the F-LUM filters are evaluated for the problems of image impulsive noise removal, sharpening and edge-detection preprocessing. The experimental results show that the F-LUM smoothers can achieve a better tradeoff between noise removal and detail preservation than the LUM smoothers. The F-LUM sharpeners are capable of sharpening the image edges without amplifying the noise or distorting the fine details. The joint smoothing and sharpening operation of the general F-LUM filters also showed superiority in edge detection preprocessing application. In conclusion, the simplicity and versatility of the F-LUM filters and their advantages over the conventional LUM filters are desirable in many practical applications. This also shows that utilizing fuzzy ranks in filter generalization is a promising methodology.

Algorithms↗

PubMed2004

An interference cancellation algorithm for noninvasive extraction of transabdominal fetal electroencephalogram (TaFEEG).

The electroencephalogram is a noninvasive method of demonstrating cerebral function. The fetal electroencephalogram (FEEG) contains important information regarding the status of a fetus. It is believed that disorganization of normal FEEG development may help detect the onset of cerebral palsy and mental retardation syndromes. Unfortunately, noninvasive methods of monitoring FEEG are not currently available. Noninvasively obtained abdominal surface electrical recordings include FEEG components, but are dominated by large interfering components, and, thus, have very low signal to noise ratio. In this paper, we propose a multistep extraction procedure to separate the four main components in transabdominal recordings: 1) maternal ECG; 2) FECG; and 3) FEEG signals as well as 4) interfering baseline wander. The algorithm is tested on simulated and real transabdominal recordings. This study shows that the proposed method successfully extracts the desired FEEG signal.

Abdomen↗

PubMed2004

Optimal halftoning for tactile imaging.

Reading of text and understanding images by touch is an important alternative and additional source of information when sight is absent or lost. Tactile graphics and models such as edge maps, binary output, etc., are the solution for simple access to images for blind persons. This paper introduces an approach to model the human tactile system based on the responses produced by stimuli on microcapsule paper. This system is utilized for the purpose of generating optimum halftone patterns on microcapsule paper that can be utilized for the effective generation of tactile graphics.

Adult↗