El-Hachemi Guerrout

Medical Image Segmentation Using Particle Swarm Optimization

Segmentation of medical images is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There are several methods to perform segmentation. Hidden Markov Random Fields (HMRF) constitutes an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we focus on Particles Swarm Optimization (PSO) method to solve this optimization problem. The quality of segmentation is evaluated on grounds truths images using the Kappa index. The results show the supremacy of the HMRF-PSO method compared to K-means and threshold based techniques.

Satellite and Aerial Image Mosaicing - A Comparative Insight

Image registration or image stitching is a central operation in many useful and important tasks in image processing like maps construction, scanning large documents and panoramic photos creation. In particular image mosaicing is used to assemble several overlapping images in order to constitute the global frame. We will focus on a feature-point matching method to perform the mosaicing. The SIFT algorithm is used to extract the feature points in both images. The mosaicing result is obtained after transforming the sensed or target image to align to the reference image. Performing a mosaicing operation is not sufficient to claim reaching the goal. Objective metrics must be used to evaluate the resulting mosaic. In this paper we present a complete mosaicing system named EsiReg and give a brief comparative insight on results of stitching satellite and aerial images using well known performance metrics.

Evaluation of Volumetric Medical Images Segmentation Using Hidden Markov Random Field Model

Medical image segmentation is a crucial step in the process of image analysis. An automatic aid in interpretation of huge amount of data can be of great value to specialists that hold final decision. Hidden Markov Random Field (HMRF) Model and Gibbs distributions provide powerful tools for image modeling. In this paper, we use a HMRF model to perform segmentation of volumetric medical images handling inter-image similarity. This modelling leads to the minimization of an energy function. This problem is computationally intractable. Therefore, optimizations techniques are used to compute a solution. We will use and compare promising relatively recent methods based on graph cuts with older well known methods that are Simulated Annealing and ICM.

Hidden Markov random field model and Broyden–Fletcher–Goldfarb–Shanno algorithm for brain image segmentation

Abstract Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden–Fletcher–Goldfarb–Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (BrainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.

Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation

The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grounds truths images using the Kappa index called also Dice Coefficient (DC). The results show the supremacy of the methods used compared to others methods.

Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation

Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good segmentation. Brain image segmentation results are evaluated on ground-truth images, using the Dice coefficient.

Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation

Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear Conjugate Gradient algorithm (CG) for image segmentation, in combination with the Hidden Markov Random Field modelization. Since derivatives are not available for this expression, finite differences are used in the CG algorithm to approximate the first derivative. The approach is evaluated using a number of publicly available images, where ground truth is known. The Dice Coefficient is used as an objective criterion to measure the quality of segmentation. The results show that the proposed CG approach compares favorably with other variants of Hidden Markov Random Field segmentation algorithms.