Eric Petit

A gray-level transformation-based method for image enhancement

Abstract In this paper, we present a gray-level modification method which allows us to enhance the image contrast as well as to improve the homogeneity of the regions in the image. It is based on an optimal classification of the image gray-levels, followed by a local parametric gray-level transformation applied to the obtained classes. By means of two parameters representing, respectively a homogenization coefficient ( r ) and a desired number ( n ) of classes in the output image, we introduce a new family of monotonic gray-level transformations ranging from the simple linear transformation of the input histogram to the multi-level thresholding function. The proposed method is compared to the usual image enhancement methods.

Imagerie de l’adénocarcinome du pancréas

Resume Le cancer du pancreas reste la quatrieme cause de mortalite par cancer. La chirurgie reste le seul traitement permettant une guerison. Un diagnostic et un bilan d’extension precis sont imperatifs pour une prise en charge adaptee des patients ayant un cancer du pancreas. Cet article detaille l’apport de chacune des techniques d’imagerie moderne au diagnostic et au bilan d’extension de l’adenocarcinome pancreatique. Le role central de la TDM s’est renforce avec l’apparition des scanners multicoupes.

Imagerie des épaississements de la paroi vésiculaire

Thickening of the gallbladder wall may result from a large spectrum of pathological conditions, intrinsic as well as extrinsic to the biliary tract, and may have different appearances. Accurate diagnosis is usually established after a correlation of imaging findings, laboratory data and clinical history. US remains the initial imaging modality for the evaluation of acute right upper quadrant pain. CT and MRI are complementary to US and have an increasing role in assessing a thickened-wall gallbladder.

Cerebrospinal fluid volume analysis for hydrocephalus diagnosis and clinical research

In this paper we analyze volumes of the cerebrospinal fluid spaces for the diagnosis of hydrocephalus, which are served as reference values for future studies. We first present an automatic method to estimate those volumes from a new three-dimensional whole body magnetic resonance imaging sequence. This enables us to statistically analyze the fluid volumes, and to show that the ratio of subarachnoid volume to ventricular one is a proportionality constant for healthy adults (=10.73), while in range [0.63, 4.61] for hydrocephalus patients. This indicates that a robust distinction between pathological and healthy cases can be achieved by using this ratio as an index.

Multivariate statistical modeling of images in sparse multiscale transforms domain

In this paper, we propose a multivariate statistical model to characterize the inter- and intra-scale dependencies between image coefficients in the oriented and non-oriented sparse multiscale transforms domain. Our proposed model, namely the Multivariate Bessel K Form, is based on multivariate extension of Bessel K Form distribution. To establish this model in practice, we propose an analytical form of PDF and then estimate its hyperparameters. Also, we compared it to the other models proposed in literature such as the Anisotropic Multivariate Generalized Gaussian and the Jeffrey models, in order to demonstrate its capabilities to capture the inter- and intra-scale dependencies between image detail coefficients.

A new method for texture classification based on wavelet transform

The wavelet transform provides several important characteristics which can be used in a texture classification problem. We present a new algorithm based on conventional pyramid-structured wavelet transform. The iteration of the basic decomposition is submitted to a variance criterion which is applied on the current lowpass subimage. This criterion helps to decide the level where the decomposition have to be stopped. This level depends on the texture properties. It is used as a feature to assign textures into different classes. Form factors computed from wavelet decomposition are then used to perfect the classification process in each class. The performance of our method is pointed out on various images and compared with Lawss (1980) texture classification results.

3D mapping of cerebrospinal fluid local volume changes in patients with hydrocephalus treated by surgery: preliminary study

AbstractObjectiveTo develop automated deformation modelling for the assessment of cerebrospinal fluid (CSF) local volume changes in patients with hydrocephalus treated by surgery.MethodsVentricular and subarachnoid CSF volume changes were mapped by calculating the Jacobian determinant of the deformation fields obtained after non-linear registration of pre- and postoperative images. A total of 31 consecutive patients, 15 with communicating hydrocephalus (CH) and 16 with non-communicating hydrocephalus (NCH), were investigated before and after surgery using a 3D SPACE (sampling perfection with application optimised contrast using different flip-angle evolution) sequence. Two readers assessed CSF volume changes using 3D colour-encoded maps. The Evans index and postoperative volume changes of the lateral ventricles and sylvian fissures were quantified and statistically compared.ResultsBefore surgery, sylvian fissure and brain ventricle volume differed significantly between CH and NCH (P = 0.001 and P = 0.025, respectively). After surgery, 3D colour-encoded maps allowed for the visual recognition of the CSF volume changes in all patients. The amounts of ventricle volume loss of CH and NCH patients were not significantly different (P = 0.30), whereas readjustment of the sylvian fissure volume was conflicting in CH and NCH patients (P < 0.001). The Evans index correlated with ventricle volume in NCH patients.Conclusion3D mapping of CSF volume changes is feasible providing a quantitative follow-up of patients with hydrocephalus.Key Points• MRI can provide helpful information about cerebrospinal fluid volumes. • 3D CSF mapping allows quantitative follow-up in communicating and non-communicating hydrocephalus. • Following intervention, fissures and cisterns readjust in both forms of hydrocephalus. • These findings support the hypothesis of suprasylvian block in communicating hydrocephalus. • 3D mapping may improve shunt dysfunction detection and guide valve pressure settings.

Hybrid framework based on evidence theory for blood cell image segmentation

The segmentation of microscopic images is an important issue in biomedical image processing. Many works can be found in the literature; however, there is not a gold standard method that is able to provide good results for all kinds of microscopic images. Then, authors propose methods for a given kind of microscopic images. This paper deals with new segmentation framework based on evidence theory, called ESA (Evidential Segmentation Algorithm) to segment blood cell images. The proposed algorithm allows solving the segmentation problem of blood cell images. Herein, our goal is to extract the components of a given cell image by using evidence theory, that allows more flexibility to classify the pixels. The obtained results showed the efficiency of the proposed algorithm compared to other competing methods.

EM algorithm-based hyperparameters estimator for bayesian image denoising using BKF prior

This paper is devoted to a novel hyperparameters estimator for bayesian denoising of images using the Bessel K Forms prior which we recently developed. More precisely, this approach is based on the EM algorithm. The simulation results show that this estimator offers good performances and is slightly better compared to the cumulant-based estimator suggested in. A comparative study is carried to show the effectiveness of our bayesian denoiser based on EM algorithm compared to other denoisers developed in both classical and bayesian contexts. Our study has been effected on natural and medical images for gaussian and poisson noise removal.

Segmentation and reconstruction of cerebral vessels from 3D rotational angiography for AVM embolization planning.

Diagnosis and computer-guided therapy of cerebral Arterio-Venous Malformations (AVM) require an accurate understanding of the cerebral vascular network both from structural and biomechanical point of view. We propose to obtain such information by analyzing three Dimensional Rotational Angiography (3DRA) images. In this paper, we describe a two-step process allowing 1) the 3D automatic segmentation of cerebral vessels from 3DRA images using a region-growing based algorithm and 2) the reconstruction of the segmented vessels using the 3D constrained Delaunay Triangulation method. The proposed algorithm was successfully applied to reconstruct cerebral blood vessels from ten datasets of 3DRA images. This software allows the neuroradiologist to separately analyze cerebral vessels for pre-operative interventions planning and therapeutic decision making.