Olena Tankyevych

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.

Directed Connected Operators: Asymmetric Hierarchies for Image Filtering and Segmentation

Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relations. The induced image representation models are no longer partition hierarchies (i.e., trees), but directed acyclic graphs that generalize standard morphological tree structures such as component trees, binary partition trees or hierarchical watersheds. We describe how to efficiently build and handle these richer data structures, and we illustrate the versatility of the proposed framework in image filtering and image segmentation.

Direction-adaptive grey-level morphology. application to 3D vascular brain imaging

Segmentation and analysis of blood vessels is an important issue in medical imaging. In 3D cerebral angiographic data, the vascular signal is however hard to accurately detect and can, in particular, be disconnected. In this article, we present a procedure utilising both linear, Hessian-based and morphological methods for blood vessel edge enhancement and reconnection. More specifically, multi-scale second-order derivative analysis is performed to detect candidate vessels as well as their orientation. This information is then fed to a spatially-variant morphological filter for reconnection and reconstruction. The result is a fast and effective vessel-reconnecting method.

Spatially-Variant Morpho-Hessian Filter: Efficient Implementation and Application

Elongated objects are more difficult to filter than more isotropic ones because they locally comprise fewer pixels. For thin linear objects, this problem is compounded because there is only a restricted set of directions that can be used for filtering, and finding this local direction is not a simple problem. In addition, disconnections can easily appear due to noise. In this paper we tackle both issues by combining a linear filter for direction finding and a morphological one for filtering. More specifically, we use the eigen-analysis of the Hessian for detecting thin, linear objects, and a spatially-variant opening or closing for their enhancement and reconnection. We discuss the theory of spatially-variant morphological filters and present an efficient algorithm. The resulting spatially-variant morphological filter is shown to successfully enhance directions in 2D and 3D examples illustrated with a brain blood vessel segmentation problem.

Angiographic Image Analysis

In the last 20 years, progress in 3D medical imaging (such as MRI and CT) has led to the development of modalities devoted to visualise vascular structures. These angiographic images progressively proved their usefulness in the context of various clinical applications. However, such data are generally complex to analyse due to their size and low amount of relevant (vascular) information versus noise, artifacts and other anatomical structures. Therefore, there is an ongoing necessity to provide tools facilitating image visualisation and analysis. In this chapter, we first focus on vascular image analysis. In particular, we present a survey on both standard and recent vessel segmentation methodologies. We then discuss the existing ways to model anatomical knowledge via the computation of vascular atlases. Such atlases can notably be embedded in computer-aided radiology tools.

Semi-connections and Hierarchies

Connectivity is the basis of several methodological concepts in mathematical morphology. In graph-based approaches, the notion of connectivity can be derived from the notion of adjacency. In this preliminary work, we investigate the effects of relaxing the symmetry property of adjacency. In particular, we observe the consequences on the induced connected components, that are no longer organised as partitions but as covers, and on the hierarchies that are obtained from such components. These hierarchies can extend data structures such as component-trees and partition-trees, and the associated filtering and segmentation paradigms, leading to improved image processing tools.

Morphology-based cerebrovascular atlas

Cerebrovascular atlases can be used to improve medical tasks requiring the analysis of 3D angiographic data. The generation of such atlases remains however a complex and infrequently considered issue. The existing approaches rely on information exclusively related to the vessels. We alternatively investigate a new way, consisting of using both vascular and morphological information (i.e., cerebral structures) to improve the accuracy and relevance of the obtained vascular atlases. Experiments emphasize improvements in the main steps of the atlas generation process impacted by the use of morphological information. An example of cerebrovascular atlas obtained from a dataset of 56 MRAs acquired from different acquisition devices is finally provided.

Ultrasound image texture characterization with Gabor wavelets for cardiac hypertrophy differentiation

Cardiac hypertrophy is routinely examined using ultrasound (US) imaging. The myocardial tissue undergoes modifications specific to every disease expressed in the image by changes in texture difficult to be perceived by the naked eye. Here, we study the possibility of the automatic detection and quantification of different causes of hypertrophy by texture analysis methods. In this work, the cardiac tissue texture is characterized using decimated Gabor filters. Then, the first- and second-order statistical features are determined from the filtered images. The most significant features are selected by Principal Component Analysis then classified by Linear Discriminant Analysis in the supervised manner giving promising results for automatic cardiac tissue characterization with Gabor filters.

Symbolic representation of brain vascular network with Arteriovenous Malformations from 3DRA images

Vascular imaging is crucial in the treatment of many diseases. In the case of cerebral ArterioVenous Malformation (AVM), where the vascular network can be deeply altered, an accurate knowledge of its topology is required. For this purpose, after a vessels segmentation and skeletization applied on 3D rotational angiographic images (3DRA), we build a symbolic tree representation of the vascular network thanks to topological descriptors, such as end points, junctions and branches. This leads to an efficient tool to assist the neuroradiologist to understand the feeding and the draining of the AVM and to apprehend its complex architecture in order to determine the best therapeutic strategy before and during embolization interventions.

Thin structure filtering framework with non-local means, Gaussian derivatives and spatially-variant mathematical morphology

Thin structure filtering is an important preprocessing task for the analysis of 2D and 3D bio-medical images in various contexts. We propose a filtering framework that relies on three approaches that are distinct and infrequently used together: linear, non-linear and non-local. This strategy, based on recent progress both in algorithmic/computational and methodological points of view, provides results that benefit from the advantages of each approach, while reducing their respective weaknesses. Its relevance is demonstrated by validations on 2D and 3D images.