15 research outputs found
Recommended from our members
Active contour approach for accurate quantitative airway analysis
Chronic airway disease causes structural changes in the lungs including peribronchial thickening and airway dilatation. Multi-detector computed tomography (CT) yields detailed near-isotropic images of the lungs, and thus the potential to obtain quantitative measurements of lumen diameter and airway wall thickness. Such measurements would allow standardized assessment, and physicians to diagnose and locate airway abnormalities, adapt treatment, and monitor progress over time. However, due to the sheer number of airways per patient, systematic analysis is infeasible in routine clinical practice without automation. We have developed an automated and real-time method based on active contours to estimate both airway lumen and wall dimensions; the method does not require manual contour initialization but only a starting point on the targeted airway. While the lumen contour segmentation is purely region-based, the estimation of the outer diameter considers the inner wall segmentation as well as local intensity variation, in order anticipate the presence of nearby arteries and exclude them. These properties make the method more robust than the Full-Width Half Maximum (FWHM) approach. Results are demonstrated on a phantom dataset with known dimensions and on a human dataset where the automated measurements are compared against two human operators. The average error on the phantom measurements was 0.10mm and 0.14mm for inner and outer diameters, showing sub-voxel accuracy. Similarly, the mean variation from the average manual measurement was 0.14mm and 0.18mm for inner and outer diameters respectively
Spatially-Variant Directional Mathematical Morphology Operators Based on a Diffused Average Squared Gradient Field
International audienceThis paper proposes an approach for mathematical morphology operators whose structuring element can locally adapt its orientation across the pixels of the image. The orientation at each pixel is extracted by means of a diffusion process of the average squared gradient field. The resulting vector field, the average squared gradient vector flow, extends the orientation information from the edges of the objects to the homogeneous areas of the image. The provided orientation field is then used to perform a spatially variant filtering with a linear structuring element. Results of erosion, dilation, opening and closing spatially-variant on binary images prove the validity of this theoretical sound and novel approach
Attribute Controlled Reconstruction and Adaptive Mathematical Morphology
ISBN : 978-3-642-38293-2International audienceIn this paper we present a reconstruction method controlled by the evolution of attributes. The process begins from a marker, propagated over increasing quasi-flat zones. The evolution of several increasing and non-increasing attributes is studied in order to select the appropriate region. Additionally, the combination of attributes can be used in a straightforward way. To demonstrate the performance of our method, three applications are presented. Firstly, our method successfully segments connected objects in range images. Secondly, input-adaptive structuring elements (SE) are defined computing the controlled propagation for each pixel on a pilot image. Finally, input-adaptive SE are used to assess shape features on the image. Our approach is multi-scale and auto-dual. Compared with other methods, it is based on a given attribute but does not require a size parameter in order to determine appropriate regions. It is useful to extract objects of a given shape. Additionally, our reconstruction is a connected operator since quasi-flat zones do not create new contours on the image
Amoeba Techniques for Shape and Texture Analysis
Morphological amoebas are image-adaptive structuring elements for
morphological and other local image filters introduced by Lerallut et al. Their
construction is based on combining spatial distance with contrast information
into an image-dependent metric. Amoeba filters show interesting parallels to
image filtering methods based on partial differential equations (PDEs), which
can be confirmed by asymptotic equivalence results. In computing amoebas, graph
structures are generated that hold information about local image texture. This
paper reviews and summarises the work of the author and his coauthors on
morphological amoebas, particularly their relations to PDE filters and texture
analysis. It presents some extensions and points out directions for future
investigation on the subject.Comment: 38 pages, 19 figures v2: minor corrections and rephrasing, Section 5
(pre-smoothing) extende
Volume Visualization of the Heart Using MRI 4D Cardiac Images
This paper deals with a system for volume visualization of the heart using multiphase-multislice cardiac MRI data. The proposed system is based on a generalized 4D form of a fuzzy object extraction algorithm in order to distinguish voxels belonging to cardiac object from noisy points and surrounding tissues. The system is initialized by interactive selection of a pixel placed inside the cardiac muscle in a slice. In some cases, complementary information might be necessary for segmentation because of the similarity in grey level information between the heart and surrounding tissues. We applied an active contour model and a contour propagation technique to a variance image for a rough segmentation of epicardium. The fuzzy approach in combination with the use of a deformable model for isolation enables us to segment cardiac object without modifying voxel grey levels and preserving anatomical details. Fuzzy object extraction involves problems of enormous combinatorial complexity, but t his ca n be reduced by dynamic programming leading to practical algorithms for cardiac data sets. We have implemented these algorithms and tested their efficiency in preserving heart data during preprocessing. Different anatomical presentations of the heart have been used for this purpose, consisting of only a small number of slices per volume
Caracterisation et classification des images médicales en vue d'une compression optimale
Cet article propose une nouvelle méthodologie dont le but est la détermination de l'algorithme de compression d'images optimal, par un système de décision basé sur une caractérisation et classification des images médicales en fonction de leurs propriétés texturales. Ce système de décision est réalisé grâce à une "pyramide discriminante", basée sur des analyses factorielles discriminantes successives
Phase based level set segmentation of ultrasound images
Ultrasound images segmentation is a difficult problem due to speckle noise, low contrast and local changes of intensity. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant. Here, we use level set propagation to capture the left ventricle boundaries. This uses a new speed term based on local phase and local orientation derived from the monogenic signal, which makes the algorithm robust to attenuation artefact. Furthermore, we use Cauchy kernels, instead of the commonly used log-Gabor, as pair of quadrature filters for the feature extraction. Preliminary results show that the proposed method can robustly handle noise, and captures well the low contrast boundaries