Philippe Bunel

Image analysis by bidimensional empirical mode decomposition

Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture extraction and image filtering, which are widely recognized as a difficult and challenging computer vision problem. We developed an algorithm based on bidimensional empirical mode decomposition (BEMD) to extract features at multiple scales or spatial frequencies. These features, called intrinsic mode functions, are extracted by a sifting process. The bidimensional sifting process is realized using morphological operators to detect regional maxima and thanks to radial basis function for surface interpolation. The performance of the texture extraction algorithms, using BEMD method, is demonstrated in the experiment with both synthetic and natural images.

Bidimensional empirical mode decomposition modified for texture analysis

This study introduces a new approach based on Bidimensional Empirical Mode Decomposition (BEMD) to extract texture features at multiple scales or spatial frequencies. Moreover, it can resolve the intrawave frequency modulation provided the frequency modulation. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textured images. The sifting process is realized using morphological operators to analyze the spatial frequencies and thanks to radial basis functions (RBF) for surface interpolation. We modified the original sifting algorithm to permit a pseudo bandpass decomposition of images by inserting scale criterion. Its effectiveness is demonstrated on synthetic and natural textures. In particular, we show that many different elements in textures can be extracted through the bidimensional empirical mode decomposition, which is fully unsupervised.

Texture analysis based on the bidimensional empirical mode decomposition with gray-level co-occurrence models

We present a texture analysis algorithm based on gray-level cooccurrence (GLC) model and bidimensional empirical mode decomposition (BEMD) of a texture field. The EMD, which has been recently introduced in signal processing by Huang in 1998, is adaptive for nonlinear and nonstationary data analysis. The main contribution of our approach is to apply the empirical mode decomposition to texture extraction and image denoising. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textured images. The sifting process is realized using morphological operators to detect regional extrema and thanks to radial basis functions (RBF) for interpolation. We modified the original sifting process to permit a texture decomposition of images by inserting criteria proposed by second-order statistics from GLCs.

A gray-level transformation-based method for image enhancement

Abstract In this paper, we present a gray-level modification method which allows us to enhance the image contrast as well as to improve the homogeneity of the regions in the image. It is based on an optimal classification of the image gray-levels, followed by a local parametric gray-level transformation applied to the obtained classes. By means of two parameters representing, respectively a homogenization coefficient ( r ) and a desired number ( n ) of classes in the output image, we introduce a new family of monotonic gray-level transformations ranging from the simple linear transformation of the input histogram to the multi-level thresholding function. The proposed method is compared to the usual image enhancement methods.

A multiscale elastic registration scheme for retinal angiograms

The present paper describes a new and efficient method for registration of retinal angiogram. The presence of noise, the variations in the background, and the temporal variation of fluorescence level poses serious problems in obtaining a robust registration of the retinal image. Here, a multiscale registration scheme is proposed which comprises of three steps. The first step of this work proposes an edge preserving smoothing of the vascular tree. This morphological filtering approach is based on opening and closing with a linear rotating structuring element. For complete preservation of the linear shape of the vascular structures, a morphological reconstruction by dilation of the opened image and a reconstruction by erosion of the closed image are applied. It is proposed to compute the registration transform between two successive original frames, from their morphological gradient. Then, the second step consists in computing the morphological gradient of the two filtered images and radiometrically correcting these gradient images. To take into account the intensity variations, our model incorporates two constant multiplicative and additive factors (based on contrast and brightness) estimated employing a simple analysis of the local histograms (based on a sliding window). In the third step, the proposed method computes the registering transform through a coarse-to-fine (or multiscale) hierarchical approach. After computing the dominant registering transform (which implies the translation) between two successive frames, an elastic transform (also called local affine transform) is carried out to achieve a residual correction. The proposed method is tested by experimental studies, performed on macular fluorescein and Indo cyanine green angiographies. It has been sufficiently demonstrated that our proposed registering method is robust, accurate and fully automated, and it is not based on the extraction of the features or landmarks.

Spatio-temporal characterization of vessel segments applied to retinal angiographic images

In this paper, we present a new approach of analysis and recognition of retinal vessel segments for the quantification of their shapes change due to alterations. This approach is based on a spatial followed by a temporal characterization of retinal fluorescein angiographic images. The spatial characteristics (coordinates and classification number i.e. numbering of segments) are associated to bifurcation points (bp), which are matched in temporal image pairs for vessel segments correspondence forming. The matching process uses spatial and temporal characteristics between the bp and their surrounded vessel segments by computing a coefficient of similarity measurement.The recognition of vessel segments will help ophthalmologists in quantifying changes in vessel shape parameters and detect the temporal evolution of some retinal pathologies as the Sickle Cell retinopathy in our case.

A new algorithm to detect the retinal structures

This paper deals with a new algorithm for automatic edge detection of retinal structures on angiographic frames. Usually, the determination of these structures and their accurate locations are based on a manual mapping wich is time consuming, tedious and not error-free. Computer image processing allows to automate the mapping and increases the reliability of extracted parameters. Therefore, we realize a process to automatically detect retinal structures. This process is based on the photometric and geometric properties of these structures. The results shown that the automatic mapping is more accurate and more reliable than manual mapping.