Benoît Naegel

Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology

In the last 20 years, 3D angiographic imaging has proven its usefulness in the context of various clinical applications. However, angiographic images are generally difficult to analyse due to their size and the complexity of the data that they represent, as well as the fact that useful information is easily corrupted by noise and artifacts. Therefore, there is an ongoing necessity to provide tools facilitating their visualisation and analysis, while vessel segmentation from such images remains a challenging task. This article presents new vessel segmentation and filtering techniques, relying on recent advances in mathematical morphology. In particular, methodological results related to spatially variant mathematical morphology and connected filtering are stated, and included in an angiographic data processing framework. These filtering and segmentation methods are evaluated on real and synthetic 3D angiographic data.

Grey-level hit-or-miss transforms-Part I: Unified theory

The hit-or-miss transform (HMT) is a fundamental operation on binary images, widely used since 40 years. As it is not increasing, its extension to grey-level images is not straightforward, and very few authors have considered it. Moreover, despite its potential usefulness, very few applications of the grey-level HMT have been proposed until now. Part I of this paper, developed hereafter, is devoted to the description of a theory leading to a unification of the main definitions of the grey-level HMT, mainly proposed by Ronse and Soille, respectively (part II will deal with the applicative potential of the grey-level HMT, which will be illustrated by its use for vessel segmentation from 3D angiographic data). In this first part, we review the previous approaches to the grey-level HMT, especially the supremal one of Ronse, and the integral one of Soille; the latter was defined only for flat structuring elements (SEs), but it can be generalized to non-flat ones. We present a unified theory of the grey-level HMT, which is decomposed into two steps. First a fitting associates to each point the set of grey-levels for which the SEs can be fitted to the image; as in Soilles approach, this fitting step can be constrained. Next, a valuation associates a final grey-level value to each point; we propose three valuations: supremal (as in Ronse), integral (as in Soille) and binary.

Grey-level hit-or-miss transforms-part II: Application to angiographic image processing

The hit-or-miss transform (HMT) is a fundamental operation on binary images, widely used since 40 years. As it is not increasing, its extension to grey-level images is not straightforward, and very few authors have considered it. Moreover, despite its potential usefulness, very few applications of the grey-level HMT have been proposed until now. Part I of this paper [B. Naegel, N. Passat, C. Ronse, Grey-level hit-or-miss transforms-part I: unified theory. Pattern Recogn., in press doi:10.1016/j.patcog.2006.06.004] was devoted to the description of a theory enabling to unify the main definitions of the grey-level HMT, mainly proposed by Ronse and Soille, respectively. Part II of this paper, developed hereafter, deals with the applicative potential of the grey-level HMT, illustrated by its use for vessel segmentation from 3D angiographic data. Different HMT-based segmentation methods are then described and analysed, leading to concrete analysis techniques for brain and liver vessels, but also providing algorithmic strategies which could further be used for many other kinds of image processing applications.

Interactive segmentation based on component-trees

Component-trees associate to a discrete grey-level image a descriptive data structure induced by the inclusion relation between the binary components obtained at successive level-sets. This article presents an original interactive segmentation methodology based on component-trees. It consists of the extraction of a subset of the image component-tree, enabling the generation of a binary object which fits at best (with respect to the grey-level structure of the image) a given binary target selected beforehand in the image. A proof of the algorithmic efficiency of this methodological scheme is proposed. Concrete application examples on magnetic resonance imaging (MRI) data emphasise its actual computational efficiency and its usefulness for interactive segmentation of real images.

Component-Trees and Multi-value Images: A Comparative Study

In this article, we discuss the way to derive connected operators based on the component-tree concept and devoted to multi-value images. In order to do so, we first extend the grey-level definition of the component-tree to the multi-value case. Then, we compare some possible strategies for colour image processing based on component-trees in two application fields: colour image filtering and colour document binarisation.

A document binarization method based on connected operators

An original binarization method based on connected operators is proposed in this paper. Connected operators enable to filter and/or segment an image by preserving its contours. The proposed binarization method enables to extract relevant document objects by means of the component-tree structure. This strategy was compared to other binarization methods and showed good behavior in various contexts.

An extension of component-trees to partial orders

Component-trees provide efficient ways to define filtering-based procedures on grey-level images. We propose an extension of the notion of component-trees to the case of ¿non grey-level¿ images (i.e. images taking their values in partially-ordered sets) including in particular - but not exclusively - colour images. Experiments performed on such images emphasise the interest of the approach.

Segmentation of Complex Images Based on Component-Trees: Methodological Tools

Component-trees can be used for the design of image processing methods, and in particular segmentation ones. However, despite their ability to consider various kinds of knowledge and their tractable computation, methodological deadlocks often forbid to efficiently involve them in real applications. In this article, we explore new solutions to some of these deadlocks, and more especially those related to (i ) complexity of the structures of interest and (ii ) multiple knowledge handling. The usefulness of the proposed strategies is illustrated by preliminary results related to vessel segmentation from 3-D angiographic data.

Connected Filtering Based on Multivalued Component-Trees

In recent papers, a new notion of component-graph was introduced. It extends the classical notion of component-tree initially proposed in mathematical morphology to model the structure of gray-level images. Component-graphs can indeed model the structure of any-gray-level or multivalued-images. We now extend the anti extensive filtering scheme based on component-trees, to make it tractable in the framework of component-graphs. More precisely, we provide solutions for building a component-graph, reducing it based on selection criteria, and reconstructing a filtered image from a reduced component-graph. In this paper, we first consider the cases where component-graphs still have a tree structure; they are then called multivalued component-trees. The relevance and usefulness of such multivalued component-trees are illustrated by applicative examples on hierarchically classified remote sensing images.

SNR enhancement of highly-accelerated real-time cardiac MRI acquisitions based on non-local means algorithm

Real-time cardiac MRI appears as a promising technique to evaluate the mechanical function of the heart. However, ultra-fast MRI acquisitions come with an important signal-to-noise ratio (SNR) penalty, which drastically reduces the image quality. Hence, a real-time denoising approach would be desirable for SNR amelioration. In the clinical context of cardiac dysfunction assessment, long acquisitions are required and for most patients the acquisition takes place with free breathing. Hence, it is necessary to compensate respiratory motion in real-time. In this article, a real-time and interactive method for sequential registration and denoising of real-time MR cardiac images is presented. The method has been experimented on 60 fast MRI acquisitions in five healthy volunteers and five patients. These experiments assessed the feasibility of the method in a real-time context.