Francisco Rodenas

Automatic evaluation of the image quality of a mammographic phantom

In this work a method has been developed to analyse the digital image quality of a mammographic phantom by means of automatic process techniques. The techniques used for the digital image treatment are standard techniques as the image thresholding to detect objects, the regional growing for pixels pooling and the morphological operator application to determine the objects shape and size, etc. This study allows the obtention of information about the phantom characteristics, that due to its small size and lowly contrast can be obtained very difficultly by direct observation. The final aim of this work is to obtain one or more parameters to characterize the reference phantom quality image in an objective way. These parameters will serve to compare images obtained at different mammographic centers and also, to study the temporal evolution of the image quality produced by determined mammographic equipment.

Comparing methods to denoise mammographic images

Digital mammographic image processing often requires a previous application of filters to reduce the noise level of the image while preserving important details. This may improve the quality of digital mammographic images and contribute to an accurate diagnosis. In the literature, one can find a large amount of denoising techniques available for different kinds of images. We have adapted some of the existing denoising algorithms to mammographic images. We compare the effect of different denoising filters acting on digitized mammograms. The considered filters are: a local Wiener filter, a wavelet filter, a filter based on independent component analysis, and finally, a filter based on the diffusion equation. The noise reduction is measured by the mean squared error.

CT Image Reconstruction Based on GPUs

In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections. However, in practice, these methods are not widely used due to the high computational cost of their implementation. Nowadays technology provides the possibility to reduce effectively this drawback. It is the goal of this work to develop a fast GPU-based algorithm to reconstruct high quality images from under sampled and noisy projection data.

Medical image restoration with different types of noise

The images obtained by X-Ray or computed tomography (CT) in adverse conditions may be contaminated with noise that can affect the detection of diseases. A large number of image processing techniques (filters) have been proposed to remove noise. These techniques depend on the type of noise present in the image. In this work, we propose a method designed to reduce the Gaussian, the impulsive and speckle noise and combined noise. This filter, called PGNDF, combines a non-linear diffusive filter with a peer group with fuzzy metric technique. The proposed filter is able to reduce efficiently the image noise without any information about what kind of noise might be present. To evaluate the filter performance, we use mammographic images from the mini-MIAS database which we have damaged by adding Gaussian, impulsive and speckle noises of different magnitudes. As a result, the proposed method obtains a good performance in most of the different types of noise.

Fast parallel algorithm for CT image reconstruction

In X-ray computed tomography (CT) the X rays are used to obtain the projection data needed to generate an image of the inside of an object. The image can be generated with different techniques. Iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections. Their use may be important in portable scanners for their functionality in emergency situations. However, in practice, these methods are not widely used due to the high computational cost of their implementation. In this work we analyze iterative parallel image reconstruction with the Portable Extensive Toolkit for Scientific computation (PETSc).

Iterative reconstruction of CT images with PETSc

In X-ray computed tomography (CT) the X rays are used to obtain the projection data needed to generate an image of the inside of an object. The image can be generated with different techniques. Algebraic methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections. Their use may be important in portable scanners for their functionality in emergency situations. However, in practice, these methods are not widely used due to the high computational cost of their implementation. In this work we analyze and propose the usage of the PETSc library for the optimal usage of a system in the parallel reconstruction of images. Also, the quality comparison of the images reconstructed with both methods, analytical Filtered Back projection (FBP) and iterative LSQR, has been performed.

Slow Growth for Universal Harmonic Functions

Given any continuous increasing function such that , we show that there are harmonic functions on satisfying the inequality for every , which are universal with respect to translations. This answers positively a problem of D. H. Armitage (2005). The proof combines techniques of Dynamical Systems and Operator Theory, and it does not need any result from Harmonic Analysis.

Iterative reconstruction of CT images on GPUs

Although widely used in nuclear medicine (gamma-camera, single photon emission computed tomography (SPECT), positron emission tomography (PET)), iterative reconstruction has not yet penetrated in CT. The main reason for this is that data sets in CT are much larger than in nuclear medicine and iterative reconstruction then becomes computationally very intensive. Graphical Processing Units (GPUs) provide the possibility to reduce effectively the high computational cost of their implementation. It is the goal of this work to develop a GPU-based algorithm to reconstruct high quality images from under sampled and noisy projection data.

Study of digital mammographic equipments by phantom image quality.

Nowadays, the digital radiographic equipments are replacing the traditional film-screen equipments and it is necessary to update the parameters to guarantee the quality of the process. Contrast-detail phantoms are applied to digital radiography to study the threshold contrast-detail sensitivity at operation conditions of the equipment. The phantom that is studied in this work is CDMAM 3.4. One of the most extended indexes to measure the image quality in an objective way is the image quality figure (IQF). The aim of this work is to study the image quality of different images contrast-detail phantom CDMAM 3.4, carrying out the automatic detection of the contrast-detail combination and to establish a parameter which characterize in an objective way the mammographic image quality. This is useful to compare images obtained at different digital mammographic equipments to study the functioning of the equipments that facilitates the evaluation of image contrast and detail resolution

Diffusion equations with negentropy applied to denoise mammographic images

Mammography is a radiographic technique used for the detection of breast lesions. The analysis of the digital image normally requires a previous application of filters as a preprocessing step to reduce the noise level of the image, while preserving important details to carry out a suitable diagnostic. In the literature, there are a large amount of denoising techniques applied to different medical images. In this work we have studied the performance of a diffusive filter with a stopping condition based on the statistical concept of negentropy, applied to denoise mammographic images. The negentropy has been succesfully prove with other denoising methods as independent component analysis by the authors in [1]. We have evaluated the final image quality obtained, measuring a root mean squared error between the noise-free initial image and the final restored image and compared the results obtained by this diffusive filter with those obtained by an adaptative non-linear Wiener filter