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