Image processing for porous media characterization

International audienceIn digital image processing, skeletonization is a valuable technique for the characterization of complex 3D porous media, such as bone, stone and soils. 3D thinning algorithms are usually used to extract one-voxel wide skeleton from 3D porous objects while preserving the topological information. Models based on simplified skeletons have been shown to be efficient in retrieving morphological information from large scale disordered objects at a local level. In this paper, we present a series of 3D skeleton-based image processing techniques for evaluating the micro-architecture of large scale disordered porous media. The proposed hybrid skeleton method combines curve and surface thinning methods with the help of an enhanced shape classification algorithm. Results on two different porous objects demonstrate the ability of the hybrid skeleton method to provide significant topological and morphological information

A Feature Point Based Image Registration Using Genetic Algorithms

Image registration has been widely applied in many fields such as remote sensing, medical image analysis, cartography, computer vision and pattern recognition. The key of image registration is to find the proper transformation of one image to another image so that each point of one image is spatially aligned with its corresponding point of the other. In this paper, we present a rigid feature point based image registration method integrating two techniques. The first is one in which we propose to extract the feature points by using efficiency of the multi-resolution representation data of the nonsubsampled contourlet transform. The second technique exploits the robustness of Genetic algorithms as an optimization method to find the best transformation parameters. The results show the effectiveness of this approach for registering the magnetic resonance images

Improved VSF Algorithm for Smooth Surface Reconstruction from Sparse Medical Data

This paper presents a Modified Variational Splines Fitting (MVSF) algorithm for surface reconstruction using thin plate splines on scattered patches or points of originally smooth surfaces. In particular, a more accurate derivation of the discrete equations for the energy corresponding to the thin plate model is introduced. The results obtained on simulated data show that the proposed algorithm converges faster than the original VSF algorithm. Additionally, we discuss an approach for choosing the algorithm’s parameters using a cross validation technique. Results obtained with the modified algorithm are compared to those using a Frequency Fourier-based 3D Harmonic modelling (3DHM) algorithm and show that the proposed algorithm gives an improved performance under the small sample size condition. The developed model has been successfully applied for real biomedical data; in particular for the reconstruction of left ventricle of human heart

Chan-Vese based method to segment mouse brain MRI images: application to cerebral malformation analysis in trisomy 21

International audienceIn this paper, a semi automatic active contour method based on Chan-Vese model is proposed for the segmentation of mouse brain MR images. First, a 2 ½ D strategy is applied on the axial images to segment the 3D volume of interest. The method takes into account the special shape of the object to segment. Moreover, the user defines the limits where to search these contours and also provides an initial contour. This semi automatic method makes that human intervention is limited and the tedious manual handling is greatly reduced. Results have shown that the brain volumes estimated by the method are identical to expert manually estimated volumes. Last but not least, the new method was used in the analysis of the cerebral malformations linked to Trisomy 21: no significant difference of the brain volumes between Tri-somy 21 mice and the control ones were found

Comparaison locale des surfaces 3D (application à l imagerie médicale)

Les travaux présentés dans cette thèse concernent la comparaison locale de surfaces médicales. Ils ont été conduits pour comparer précisément deux séquences de surfaces du ventricule gauche du cœur reconstruites à partir de deux modalités d imagerie médicale. Les reconstructions de la première modalité (médecine nucléaire) ont servi de référence pour valider de nouvelles reconstructions obtenues par la seconde (échographie). La contribution théorique des travaux porte sur deux domaines du traitement de signal et des images : le recalage rigide et la reconstruction de surfaces fermées. Les travaux menés sur le recalage de surfaces se limitent au recalage rigide, en particulier à l algorithme Iterative Closest Point (ICP) , puisqu on s attache à mettre en évidence les écarts entre des reconstructions différentes de surfaces identiques. Une variante de l algorithme ICP est proposée pour améliorer l appariement de paires de points de correspondance dans les deux ensembles de données à recaler. La recherche vectorielle des distances minimum entre les paires de points est remplacée par une recherche matricielle, qui assure les propriétés d injection nécessaire pour estimer les paramètres de rotation. Les performances de la nouvelle méthode sont étudiées en présence de différents types de bruit superposés aux données. La reconstruction de surfaces 3D incomplètes est utilisée pour comparer et visualiser des données qui présentent des échantillonnages irréguliers ou des résolutions différentes. Une nouvelle forme discrète du modèle plaque mince de la méthode de reconstruction basée sur un ajustement par splines variationnelles (VSF) améliore la performance de cette méthode et prend en compte des conditions de périodicité sur les surfaces reconstruites. Les résultats obtenus sont comparés avec ceux donnés par une analyse fréquentielle de Fourier. Ces travaux ont été complétés par la mise en œuvre d une nouvelle méthode locale basée sur les splines quasi-interpolant.Les méthodes proposées ont été utilisées pour comparer localement différentes surfaces médicales : des séquences de surfaces du ventricule gauche du cœur, des reconstructions de l enveloppe pulmonaire avec un atlas de référence, et des surfaces d escarres identiques reconstruites à plusieurs résolutions par des techniques différentes.ORLEANS-BU Sciences (452342104) / SudocSudocFranceF

Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren–Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models’ performance were not possible due to the lack of a unique definition of each of the three outcomes

Performance of Radiological and Biochemical Biomarkers in Predicting Radio-Symptomatic Knee Osteoarthritis Progression

Imaging biomarkers permit improved approaches to identify the most at-risk patients encountering knee osteoarthritis (KOA) progression. This study aimed to investigate the utility of trabecular bone texture (TBT) extracted from plain radiographs, associated with a set of clinical, biochemical, and radiographic data, as a predictor of long-term radiographic KOA progression. We used data from the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium dataset. The reference model made use of baseline TBT parameters adjusted for clinical covariates and radiological scores. Several models based on a combination of baseline and 24-month TBT variations (TBT∆TBT) were developed using logistic regression and compared to those based on baseline-only TBT parameters. All models were adjusted for baseline clinical covariates, radiological scores, and biochemical descriptors. The best overall performances for the prediction of radio-symptomatic, radiographic, and symptomatic progression were achieved using TBT∆TBT parameters solely, with area under the ROC curve values of 0.658 (95% CI: 0.612–0.705), 0.752 (95% CI: 0.700–0.804), and 0.698 (95% CI: 0.641–0.756), respectively. Adding biochemical markers did not significantly improve the performance of the TBT∆TBT-based model. Additionally, when TBT values were taken from the entire subchondral bone rather than just the medial, lateral, or central compartments, better results were obtained

Atlas-assisted segmentation of cerebral structures of mice

International audienceThe segmentation of different cerebral structures of mice is becoming more and more important due to the growing interest in finding small animal models of human diseases. In this work, variational atlases are constructed by manual segmentation of various MRI brain images of reference and trisomy 21 mice. These atlases are then registered to assist the segmentation of fine cerebral structures such as the cerebellum and the hippocampus. A modified Chan-Vese segmentation method is used for the detection of these structures. Global as well as local comparison of the reconstructed surfaces and volumes are hence conducted to better understand the gene involved in the morphological malformations associated to trisomy

Image processing for the non destructive characterization of porous media. Application to limestones and trabecular bones

International audienceDifferent image processing techniques have recently been investigated for the characterization of complex porous media, such as bones, stones and soils. Among these techniques, 3D thinning algorithms are generally used to extract a one-voxel-thick skeleton from 3D porous objects while preserving the topological information. Models based on simplified skeletons have been shown to be efficient in retrieving morphological information from large scale disordered objects not only at a global level but also at a local level. In this paper, we present a series of 3D skeleton-based image processing techniques for evaluating the micro-architecture of large scale disordered porous media. The proposed skeleton method combines curve and surface thinning methods with the help of an enhanced shape classification algorithm. Results on two different porous objects demonstrate the ability of the proposed method to provide significant topological and morphological information