Ali Saad

Simultaneous speckle reduction and contrast enhancement for ultrasound images: Wavelet versus Laplacian pyramid

Speckle can be described as random multiplicative noise. It hampers the perception and extraction of fine details in the image. Speckle reduction techniques are applied to ultrasound images in order to reduce the noise level and improve the visual quality for better diagnoses. It is also used as preliminary treatment before segmentation and classification. Several methods have been proposed for speckle reduction in ultrasound images. Multiscale contrast enhancement has proven to be very efficient for x-ray images. A recent study by Dippel et al. doing a comparison, contrast enhancement of radiographs (x-ray and mammography), between the Laplacian pyramid and the wavelet one proves that the Laplacian pyramid method gives a better result than the wavelet one; the filtering aspect was not taken into account. In ultrasound images a strong contrast variation exists which is different from x-ray and mammography. In this paper a wavelet pyramid with simultaneous speckle reduction and contrast enhancement was applied for the first time on ultrasound images with the area of interest and compared to a Laplacian enhancement pyramid. The optimum choice of wavelet bases for ultrasound images is investigated in this study. In order to realize a fair comparison, the same nonlinear modification in both multiscale schemes is used. The comparison proves that the wavelet pyramid gives a much better result than the Laplacian one for simultaneous speckle reduction and contrast enhancement of ultrasound images.

Visual enhancement of digital ultrasound images: wavelet versus Gauss–Laplace contrast pyramid

AbstractPurpose Noise is the principal factor which hampers the visual quality of ultrasound images, sometimes leading to misdiagnosis. Speckle noise in ultrasound images can be modeled as a random multiplicative process. Speckle reduction techniques were applied to digital ultrasound images to suppress noise and improve visual quality. Rationale Previous reports indicate that wavelet filtering performs best for speckle reduction in digital ultrasound images. Reportes on x-ray images compared wavelet filtering with Laplace-Gauss contrast enhancement (LGCE) showed that the LCGE performed better. As LGCE was never been applied to Ultrasound images, this study compared two filtering approaches for speckle reduction on digital ultrasound images. Methods Two methods were implemented and compared. The first method uses the wavelet soft threshold (WST) approach for enhancement. The second method is based on multiscale Laplacian-Gaussian contrast enhancement (LGCE). LGCE is derived from the combination of a Gaussian pyramid and a Laplacian one. Contrast enhancement is applied on local scale by using varying sizes of median filter. Results The two methods were applied to synthetic and real ultrasound images. A comparison between WST and LGCE methods was performed based on noise level, artifacts and subjective image quality. Conclusion WST visual enhancement provided better results than LGCE for selected ultrasound images.

Hierarchical wavelets projection matching for orientation determination of low contrast electron cryomicroscopic images of icosahedral virus particles

Electron cryomicroscopic images of virus particles, which contain structural signals at high spatial frequencies, have very low contrast. This makes the determination of the particles orientations used for three-dimensional reconstruction very difficult. In this paper we propose a new method, based on wavelet filtering and the weighted-projection matching between projections from an initial model and the raw particle images. The implementation of this method is performed hierarchically by grouping the models projections into classes of similar orientations in order to reduce the processing time. The raw particle image is compared by correlation method, with centers of classes and then with all members of the best class, to find the best match with the projections in wavelet space. The orientation of the projection giving the highest correlation coefficient is assigned to the raw particle. Our results show that a significant improvement is obtained, in terms of accuracy and speed, for the initial orientation estimates in the low contrast virus particle images.

Wavelets filtering for classification of very noisy electron microscopic single particles images- application on structure determination of VP5-VP19C recombinant

BackgroundImages of frozen hydrated [vitrified] virus particles were taken close-to-focus in an electron microscope containing structural signals at high spatial frequencies. These images had very low contrast due to the high levels of noise present in the image. The low contrast made particle selection, classification and orientation determination very difficult. The final purpose of the classification is to improve the signal-to-noise ratio of the particle representing the class, which is usually the average. In this paper, the proposed method is based on wavelet filtering and multi-resolution processing for the classification and reconstruction of this very noisy data. A multivariate statistical analysis (MSA) is used for this classification.ResultsThe MSA classification method is noise dependant. A set of 2600 projections from a 3D map of a herpes simplex virus -to which noise was added- was classified by MSA. The classification shows the power of wavelet filtering in enhancing the quality of class averages (used in 3D reconstruction) compared to Fourier band pass filtering. A 3D reconstruction of a recombinant virus (VP5-VP19C) is presented as an application of multi-resolution processing for classification and reconstruction.ConclusionThe wavelet filtering and multi-resolution processing method proposed in this paper offers a new way for processing very noisy images obtained from electron cryo-microscopes. The multi-resolution and filtering improves the speed and accuracy of classification, which is vital for the 3D reconstruction of biological objects. The VP5-VP19C recombinant virus reconstruction presented here is an example, which demonstrates the power of this method. Without this processing, it is not possible to get the correct 3D map of this virus.

A computer-based image analysis for tear ferning featuring

The present work focuses on the development of a novel computer-based approach for tear ferning (TF) featuring. The original TF images of the recently developed five-point grading scale have been used to assign a grade for any TF image automatically. A vector characteristic (VC) representing each grade was built using the reference images. A weighted combination between features selected from textures analysis using gray level co-occurrence matrix (GLCM), power spectrum (PS) analysis and linear specificity of the image were used to build the VC of each grade. A total of 14 features from texture analysis were used. PS at different frequency points and number of line segments in each image were also used. Five features from GLCM have shown significant differences between the recently developed grading scale images which are: angular second moment at 0° and 45°, contrast, and correlation at 0° and 45° these five features were all included in the characteristic vector. Three specific power frequencies were used in the VC because of the discrimination power. Number of line segments was also chosen because of dissimilarities between images. A VC for each grade of TF reference images was constructed and was found to be significantly different from each others. This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.

Orientation determination by wavelets matching for 3D reconstruction of very noisy electron microscopic virus images

BackgroundIn order to perform a 3D reconstruction of electron microscopic images of viruses, it is necessary to determine the orientation (Euler angels) of the 2D projections of the virus. The projections containing high resolution information are usually very noisy. This paper proposes a new method, based on weighted-projection matching in wavelet space for virus orientation determination. In order to speed the retrieval of the best match between projections from a model and real virus particle, a hierarchical correlation matching method is also proposed.ResultsA data set of 600 HSV-1 capsid particle images in different orientations was used to test the proposed method. An initial model of about 40 Å resolutions was used to generate projections of an HSV-1 capsid. Results show that a significant improvement, in terms of accuracy and speed, is obtained for the initial orientation estimates of noisy herpes virus images. For the bacteriophage (P22), the proposed method gave the correct reconstruction compared to the model, while the classical method failed to resolve the correct orientations of the smooth spherical P22 viruses.ConclusionThis paper introduces a new method for orientation determination of low contrast images and highly noisy virus particles. This method is based on weighted projection matching in wavelet space, which increases the accuracy of the orientations. A hierarchical implementation of this method increases the speed of orientation determination. The estimated number of particles needed for a higher resolution reconstruction increased exponentially. For a 6 Å resolution reconstruction of the HSV virus, 50,000 particles are necessary. The results show that the proposed method reduces the amount of data needed in a reconstruction by at least 50 %. This may result in savings 2 to 3 man-years invested in acquiring images from the microscope and data processing. Furthermore, the proposed method is able to determine orientations for some difficult particles like P22 with accuracy and consistency. Recently a low PH sindbis capsid was determined with the proposed method, where other methods based on the common line fail.

Numerical study of compositional compressible degenerate two-phase flow in saturated-unsaturated heterogeneous porous media

We study the convergence of a combined finite volume-nonconforming finite element scheme on general meshes for a partially miscible two-phase flow model in anisotropic porous media. This model includes capillary effects and exchange between the phases. The diffusion term, which can be anisotropic and heterogeneous, is discretized by piecewise linear nonconforming triangular finite elements. The other terms are discretized by means of a cell-centered finite volume scheme on a dual mesh. The relative permeability of each phase is decentered according to the sign of the velocity at the dual interface. The convergence of the scheme is proved thanks to an estimate on the two pressures which allows to show estimates on the discrete time and compactness results in the case of degenerate relative permeabilities. A key point in the scheme is to use particular averaging formula for the dissolution function arising in the diffusion term. We show also a simulation of hydrogen production in nuclear waste management. Numerical results are obtained by in-house numerical code.

Numerical analysis of a finite volume scheme for two incompressible phase flow with dynamic capillary pressure

Abstract In this paper, we propose and analyze the convergence of a TPFA (Two Points Flux Approximation) finite volume scheme to approximate the two incompressible phase flow with dynamic capillary pressure in porous media. The fully implicit scheme is based on nonstandard approximation on mobilities and capillary pressure on the dual mesh. We derive a discrete variational formulation and we present a new result of convergence in a two or three dimensional porous medium. In comparison with static capillary pressure, the non-equilibrium capillary model requires more powerful techniques; especially the discrete energy estimates are not standard.

3D Visualization of iron oxide nanoparticles in MRI of inflammatory model

Nanomedicine is becoming an extremely promising research area for healthcare. The visualization and quantification of nanoparticles (NPs) inside the organs of interest pose a significant challenge and therefore, novel image processing approaches are required for a better diagnosis. The purpose of this work was to develop a novel approach for better visualization and quantification of iron oxide NPs in three dimension (3D) high-resolution magnetic resonance (MR) images of an inflammatory model. The proposed procedure focuses on the extraction of NPs from the background surrounding it. It is applied on 2D and 3D images and is based on pre-processing and segmentation by automatic threshold to visualize the NPs inside the mouse calf using a control set of images of the same calf before injecting the NPs. The resulting visualization of the 3D distribution of iron oxide NPs inside the inflamed area of the calf has a potential in the advancement of NPs application in nanomedicine therapy and diagnosis.Graphical abstract