Bruno Huysmans

A new fuzzy-based wavelet shrinkage image denoising technique

This paper focuses on fuzzy image denoising techniques. In particular, we investigate the usage of fuzzy set theory in the domain of image enhancement using wavelet thresholding. We propose a simple but efficient new fuzzy wavelet shrinkage method, which can be seen as a fuzzy variant of a recently published probabilistic shrinkage method [1] for reducing adaptive Gaussian noise from digital greyscale images. Experimental results show that the proposed method can efficiently and rapidly remove additive Gaussian noise from digital greyscale images. Numerical and visual observations show that the performance of the proposed method outperforms current fuzzy non-wavelet methods and is comparable with some recent but more complex wavelets methods. We also illustrate the main differences between this version and the probabilistic version and show the main improvements in comparison to it.

Multiresolution Denoising for Optical Coherence Tomography: A Review and Evaluation

Recently emerging non-invasive imaging modality - optical coherence tomography (OCT) - is becoming an increasingly important diagnostic tool in various medical applications. One of its main limitations is the presence of speckle noise which obscures small and low-intensity features. The use of multiresolution techniques has been recently reported by several authors with promising results. These approaches take into account the signal and noise properties in different ways. Approaches that take into account the global orientation properties of OCT images apply accordingly different level of smoothing in different orientation subbands. Other approaches take into account local signal and noise covariances. So far it was unclear how these different approaches compare to each other and to the best available single-resolution despeckling techniques. The clinical relevance of the denoising results also remains to be determined. In this paper we review systematically recent multiresolution OCT speckle filters and we report the results of a comparative experimental study. We use 15 different OCT images extracted from five different three-dimensional volumes, and we also generate a software phantom with real OCT noise. These test images are processed with different filters and the results are evaluated both visually and in terms of different performance measures. The results indicate significant differences in the performance of the analyzed methods. Wavelet techniques perform much better than the single resolution ones and some of the wavelet methods improve remarkably the quality of OCT images.

Denoising of multicomponent images using wavelet least-squares estimators

In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models and Laplacian mixture models. These three prior models are studied within the same framework of least-squares optimization. The presented procedures are compared to Gaussian prior model and single-band denoising procedures. We analyze the suppression of non-correlated as well as correlated white Gaussian noise on multispectral and hyperspectral remote sensing data and Rician distributed noise on multiple images of within-modality magnetic resonance data. It is shown that a superior denoising performance is obtained when (a) the interband covariances are fully accounted for and (b) prior models are used that better approximate the marginal distributions of the wavelet coefficients.

Wavelet domain denoising of multispectral remote sensing imagery adapted to the local spatial and spectral context

We propose a novel Bayesian wavelet domain denoising method for multispectral images, which adapts itself to the local spatial and spectral image context. The proposed estimator is derived under the minimum mean squared error criterion and it takes into account uncertainty about the presence of a signal of interest in a noisy observation. Comparisons with other recent wavelet domain denoisers for multiband data demonstrate the advantages of the new method both in terms of mean squared error and visually.

Texture-based classification of periventricular leukomalacia in preterm ultrasound images

textabstractAltered white brain matter structure in neonatal Ultrasound (US) images has prognostic implications for certain disorders. Commonly, physicians classify pathological white brain matter on a discrete categorical scale based on relevant qualitative characteristics. For certain pathologies, where subtle changes in structure have to be detected, this classification is too stringent. This is the case when characterizing affected white matter in the gliotic variant of Periventricular Leukomalacia (PVL), a brain disorder of very low birth weight preterm infants. The main objective of this study is to investigate quantitatively how texture information extracted from white matter regions in B-mode US images can guide physicians to a more accurate detection. A data set of 140 B-mode US images (70 non-pathological and 70 pathological) was investigated. Pathology was defined either by evolution to cystic PVL or by definite abnormality on acute MRI (ground truth). First, 7 different texture feature sets were extracted: First-Order statistics, Grey Level Co-occurrence matrix features, Run Length matrix features, Sum and Difference histogram features, Statistical features, Texture Energy Measure features and Gabor Filter features. Then, 3 classifiers were compared on these feature sets: a Bayesian Maximum A Posteriori (MAP) probability, a k Nearest Neighbor (kNN), and Fishers Linear Discriminant (FLD) classifier. Finally, a combination of the classifiers as well as texture feature combinations based on a confidence measure, were incorporated into a multi-feature, multi-classifier algorithm. Using our method, we succeeded in identifying the pathological group with an accuracy of 92.5% and sensitivity and specificity scores that exceed those of existing non-texture based methods. Consequently, this method can improve both the prognostic finesse and the guidance of early postnatal management.

Bayesian wavelet-based denoising of multicomponent images

In this paper, we study denoising of multicomponent images. We present a framework of spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the image components. Within this framework, multicomponent prior models for the wavelet coefficients are required that a) fully account for the interband correlations between the image components, and b) approximate well the marginal distributions of the wavelet coefficients. For this, multicomponent heavy tailed models are applied. We analyze three mixture priors: Gaussian scale mixture (GSM) models, Laplacian mixture models and Bernoulli-Gaussian mixture models. As an extension of the Bayesian framework, we propose a framework that also accounts for the correlation between the multicomponent image and an auxiliary noise-free image, in order to improve the SNR of the first. For this, a GSM prior model was applied. Experiments are conducted in the domain of remote sensing in both, simulated and real noisy conditions.