Zoubeida Messali

Tomographic image reconstruction using filtered back projection (FBP) and algebraic reconstruction technique (ART)

This paper presents comparative study and experimentation of Algebraic Reconstruction Technique (ART) and Filter Back Projection (FBP). The ART and FBP methods are used to reconstruct the object from the X-ray projection. The process of creating back the object image from the Radon Transform of the object is known as Image Reconstruction. Image reconstruction is a famous and interesting field which comes under computed tomography. Computed Tomography is used for identifying the hidden or inner defects of objects. In this paper Algebraic Reconstruction technique and Filter Back Projection methods are implemented and the experimented results are compared using performance parameters for various test cases. Projections for the image reconstruction are calculated analytically by defining two phantoms: Shepp-Logan phantom head model and the standard medical image of abdomen. The original images are grayscale images of size 128 × 128, 256 × 256, respectively.

Nonparametric Bayesian estimation structures in the wavelet domain of multiple noisy image copies

This paper addresses the recovery of an image from its multiple noisy copies using a nonparametric Bayesian estimator in the wavelet domain. Boubchir et al have proposed a prior statistical model based on the α-stable densities adapted to capture the sparseness of the wavelet detail coefficients. They used the scale mixture of Gaussians theorem as an analytical approximation for α-stable densities, which is not known in general, in order to obtain a closed-form expression of their Bayesian denoiser. Since the proposed estimator has worked well for one copy of corrupted image, we consider its extension to multiple copies in this paper. So, our contribution is to design two fusion structures based on the Bayesian denoiser and the traditional averaging operation, in order to combine all multiple noisy image copies to recover the noise free image. Because of the nonlinearity of the Bayesian denoiser, averaging then Bayesian denoising or Bayesian denoising then averaging will produce different estimators. We will demonstrate the effectiveness of our Bayesian denoiser fusion structures compared to other denoising approaches. Better performance comes at the expense of higher complexity.

PDEs on Graphs for Image Reconstruction on Positron Emission Tomography

A better quality of an image can be achieved through iterative image reconstruction for positron emission tomography (PET) as it employs spatial regularization that minimizes the difference of image intensity among adjacent pixels. In our previous works, we have proposed a simple method to solve PDEs on general images using the framework of PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply morphological-based operators on graphs for processing of 2D PET images. We apply this approach for to remove noise from the raw projections data. The quality measurements and visual inspections show a significant improvement in image quality compared to conventional Algebraic Reconstruction Technique (ART).

Robust fuzzy c-means clustering algorithm using non-parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation

The major drawback of the fuzzy c-means (FCM) algorithm is its sensitivity to noise. The authors propose a new extended FCM algorithm based a non-parametric Bayesian estimation in the wavelet transform domain for segmenting noisy MR brain images. They use the Bayesian estimator to process the noisy wavelet coefficients. Before segmentation based on FCM algorithm, they use an a priori statistical model adapted to the modelisation of the wavelet coefficients of a noisy image. The main objective of this wavelet-based Bayesian statistical estimation is to recover a good quality image, from a noisy image of poor quality. Experimental results on simulated and real magnetic resonance imaging brain images show that their proposed method solves the problem of sensitivity to noise and offers a very good performance that outperforms some FCM-based algorithms.

Filtered-based expectation ߞ Maximization algorithm for Emission computed tomography (ECT) image reconstruction

The goal of Emission computed tomography (ECT) is to reconstruct the distribution of the radioisotopes in the body by measuring the emitted photons. There is a growing realization for the importance of the functional information that is submitted by ECT and there are important advancements taking place, both in the instrumentation for data collection and in the computer methods for reconstruction images from the measured data. These methods are designed to solve the inverse problem known as “image reconstruction from set of projections.” A general overview of: Filtered Back-Projection (FBP) and (Expectation Maximization) EM algorithms of reconstruction in ECT are presented in this paper. The simulated results are compared using quality measurements for various test cases and conclusion is achieved. Through these simulated results, we have demonstrated that the EM algorithm with post-filter provides the best image quality and the small values of the quality measurements.

New denoising algorithm of multiple noisy image copies based on alpha-stable distributions

This paper deals with the recovery of an image from noisy observations when multiple noisy copies of the image are available. Two configurations based on wavelet shrinkage algorithm and averaging technique are proposed. The performance of these methods is an improvement upon other methods proposed in the literature and are algorithmically simple for large computational saving. The proposed structures take a number of noisy copies of the image onto consideration in the computation of the threshold.

A Comparative Study of Analytical,Iterative and Bayesian Reconstruction Algorithms in Computed Tomography(CT)

Images of the inside of the human body can be obtained using tomographic acquisition and processing techniques. In particular, these techniques are commonly used to obtain X_ray images of the human body. The reconstructed images are obtained given a set of their projections, acquired using reconstruction techniques. A general overview of analytical and iterative methods of reconstruction in computed tomography (CT) is presented in this paper, with a special focus on Bayesian algorithms. The simulated results are compared using quality measurements for various test cases and conclusion is achieved. Through these simulated results, we have demonstrated that the Bayesian approach provides the best image quality and the small values of the quality measurements.