Bayesian entropy estimation applied to non-gaussian robust image segmentation

We introduce a new approach for robust image segmentation combining two strategies within a Bayesian framework. The first one is to use a Markov random field (MRF) which allows to introduce prior information with the purpose of image edges preservation. The second strategy comes from the fact that the probability density function (pdf) of the likelihood function is non-Gaussian or unknown, so it should be approximated by an estimated version, which is obtained by using the classical non-parametric or kernel density estimation. This lead us to the definition of a new maximum a posteriori (MAP) estimator based on the minimization of the entropy of the estimated pdf of the likelihood function and the MRF at the same time, named MAP entropy estimator (MAPEE). Some experiments were made for different kind of images degraded with impulsive noise (salt & pepper) and the segmentation results are very satisfactory and promising

Tracking Lumbar Vertebrae in Digital Videofluoroscopic Video Automatically

Low back pain becomes one of the significant problem in the industrialized world. Efficient and effective spinal motion analysis is required to understand low back pain and to aid the diagnosis. Videofluoroscopy provides a cost effective way for such analysis. However, common approaches are tedious and time consuming due to the low quality of the images. Physicians have to extract the vertebrae manually in most cases and thus continuous motion analysis is hardly achieved. In this paper, we propose a system which can perform automatic vertebrae segmentation and tracking. Operators need to define exact location of landmarks in the first frame only. The proposed system will continuously learn the texture pattern along the edge and the dynamics of the vertebrae in the remaining frames. The system can estimate the location of the vertebrae based on the learnt texture and dynamics throughout the sequence. Experimental results show that the proposed system can segment vertebrae from videofluoroscopic images automatically and accurately. © Springer-Verlag 2004.postprintThe International Workshop on Medical Imaging and Augmented Reality (MIAR 2004), Beijing, China, 19-20 August 2004. In Lecture Notes in Computer Science, 2004, v. 3150, p. 154-16

A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm

Biofilm is a formation of microbial material on tooth substrata. Several methods to quantify dental biofilm coverage have recently been reported in the literature, but at best they provide a semi-automated approach to quantification with significant input from a human grader that comes with the graders bias of what are foreground, background, biofilm, and tooth. Additionally, human assessment indices limit the resolution of the quantification scale; most commercial scales use five levels of quantification for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current state-of-the-art techniques in automatic plaque quantification fail to make their way into practical applications owing to their inability to incorporate human input to handle misclassifications. This paper proposes a new interactive method for biofilm quantification in Quantitative light-induced fluorescence (QLF) images of canine teeth that is independent of the perceptual bias of the grader. The method partitions a QLF image into segments of uniform texture and intensity called superpixels; every superpixel is statistically modeled as a realization of a single 2D Gaussian Markov random field (GMRF) whose parameters are estimated; the superpixel is then assigned to one of three classes (background, biofilm, tooth substratum) based on the training set of data. The quantification results show a high degree of consistency and precision. At the same time, the proposed method gives pathologists full control to post-process the automatic quantification by flipping misclassified superpixels to a different state (background, tooth, biofilm) with a single click, providing greater usability than simply marking the boundaries of biofilm and tooth as done by current state-of-the-art methods.Comment: 10 pages, 7 figures, Journal of Biomedical and Health Informatics 2014. keywords: {Biomedical imaging;Calibration;Dentistry;Estimation;Image segmentation;Manuals;Teeth}, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758338&isnumber=636350

New approach of entropy estimation for robust image segmentation

In this work we introduce a new approach for robust image segmentation. The idea is to combine two strategies within a Bayesian framework. The first one is to use a Márkov Random Field (MRF), which allows to introduce prior information with the purpose of preserve the edges in the image. The second strategy comes from the fact that the probability density function (pdf) of the likelihood function is non Gaussian or unknown, so it should be approximated by an estimated version, and for this, it is used the classical non-parametric or kernel density estimation. This two strategies together lead us to the definition of a new maximum a posteriori (MAP) estimator based on the minimization of the entropy of the estimated pdf of the likelihood function and the MRF at the same time, named MAP entropy estimator (MAPEE). Some experiments were made for different kind of images degraded with impulsive noise and the segmentation results are very satisfactory and promising

Chaîne d'analyse et de synthèse de textures 3D basée sur une décomposition en pyramides orientées

Cette étude vise à l'extension en 3D de la méthode proposée par Portilla et Simoncelli en analyse-synthèse de textures. L'article présente une méthode fondée sur une décomposition 3D multi-résolution dans le domaine de Fourier pour la caractérisation des textures. Le but de ce travail est d'établir un lien entre les textures 3D et les paramètres extraits lors de la phase d'analyse. Les paramètres, validés par une étape de synthèse, sont alors exploitables à des fins de classification ou de segmentation

Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach

Segmentation is an essential process in image because of its wild application such as image analysis, medical image analysis, pattern reorganization, etc. Color and texture are most significant low-level features in an image. Normally, color-textured image segmentation consists of two steps: (i) extracting the feature and (ii) clustering the feature vector. This paper presents the hybrid approach for color texture segmentation using Haralick features extracted from the Integrated Color and Intensity Co-occurrence Matrix (ICICM). Then, Extended- Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image. Experimental results show that the proposed hybrid approach could obtain better cluster quality and segmentation results compared to state-of-art image segmentation algorithms

Classification d'image satellitaire superspectrale en zone rurale et périurbaine

Les projets futurs d'observation satellitaire superspectrale à des résolutions d'une dizaine de mètres fourniront des images de zones rurales et périurbaines. L'information spectrale précise du terrain permet une classification d'un tel paysage. Afin de régulariser une classification par maximum de vraisemblance, nous considérons une modélisation de l'image par champs de Markov. Nous utilisons une expression de son énergie selon le modèle de Potts, que nous avons rendu adaptatif aux classes, pour compenser la sensibilité des structures fines (routes) à une régularisation. De plus, l'étude de la texture fournit des critères supplémentaires caractéristiques de certaines classes (forêt, paysage urbain). Une image et une vérité terrain ont permis de mettre en évidence les méthodes, les critères et le paramétrage adéquats pour optimiser une classification

MPEG-7 Based Image Retrieval on the World Wide Web

Due to the rapid growth of the number of digital media elements like image, video, audio, graphics on Internet, there is an increasing demand for effective search and retrieval techniques. Recently, many search engines have made image search as an option like Google, AlltheWeb, AltaVista, Freenet. In addition to this, Ditto, Picsearch, can search only the images on Internet. There are also other domain specific search engines available for graphics and clip art, audio, video, educational images, artwork, stock photos, science and nature [www.faganfinder.com/img]. These entire search engines are directory based. They crawls the entire Internet and index all the images in certain categories. They do not display the images in any particular order with respect to the time and context. With the availability of MPEG-7, a standard for describing multimedia content, it is now possible to store the images with its metadata in a structured format. This helps in searching and retrieving the images. The MPEG-7 standard uses XML to describe the content of multimedia information objects. These objects will have metadata information in the form of MPEG-7 or any other similar format associated with them. It can be used in different ways to search the objects. In this paper we propose a system, which can do content based image retrieval on the World Wide Web. It displays the result in user-defined order

MRF-based image segmentation using Ant Colony System

In this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality

Gray-level Texture Characterization Based on a New Adaptive

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context