Isabelle Bloch

YNIMG Journal 2023 Journal Article

Super-resolution in brain positron emission tomography using a real-time motion capture system

• Yanis Chemli
• Marc-André Tétrault
• Thibault Marin
• Marc D. Normandin
• Isabelle Bloch
• Georges El Fakhri
• Jinsong Ouyang
• Yoann Petibon

Super-resolution (SR) is a methodology that seeks to improve image resolution by exploiting the increased spatial sampling information obtained from multiple acquisitions of the same target with accurately known sub-resolution shifts. This work aims to develop and evaluate an SR estimation framework for brain positron emission tomography (PET), taking advantage of a high-resolution infra-red tracking camera to measure shifts precisely and continuously. Moving phantoms and non-human primate (NHP) experiments were performed on a GE Discovery MI PET/CT scanner (GE Healthcare) using an NDI Polaris Vega (Northern Digital Inc), an external optical motion tracking device. To enable SR, a robust temporal and spatial calibration of the two devices was developed as well as a list-mode Ordered Subset Expectation Maximization PET reconstruction algorithm, incorporating the high-resolution tracking data from the Polaris Vega to correct motion for measured line of responses on an event-by-event basis. For both phantoms and NHP studies, the SR reconstruction method yielded PET images with visibly increased spatial resolution compared to standard static acquisitions, allowing improved visualization of small structures. Quantitative analysis in terms of SSIM, CNR and line profiles were conducted and validated our observations. The results demonstrate that SR can be achieved in brain PET by measuring target motion in real-time using a high-resolution infrared tracking camera.

AIJ Journal 2018 Journal Article

Belief revision, minimal change and relaxation: A general framework based on satisfaction systems, and applications to description logics

• Marc Aiguier
• Jamal Atif
• Isabelle Bloch
• Céline Hudelot

Belief revision of knowledge bases represented by a set of sentences in a given logic has been extensively studied but for specific logics, mainly propositional, and also recently Horn and description logics. Here, we propose to generalize this operation from a model-theoretic point of view, by defining revision in the abstract model theory of satisfaction systems. In this framework, we generalize to any satisfaction system the characterization of the AGM postulates given by Katsuno and Mendelzon for propositional logic in terms of minimal change among interpretations. In this generalization, the constraint on syntax independence is partially relaxed. Moreover, we study how to define revision, satisfying these weakened AGM postulates, from relaxation notions that have been first introduced in description logics to define dissimilarity measures between concepts, and the consequence of which is to relax the set of models of the old belief until it becomes consistent with the new pieces of knowledge. We show how the proposed general framework can be instantiated in different logics such as propositional, first-order, description and Horn logics. In particular for description logics, we introduce several concrete relaxation operators tailored for the description logic ALC and its fragments EL and ELU, discuss their properties and provide some illustrative examples.

ECAI Conference 2016 Conference Paper

Efficient Semantic Tableau Generation for Abduction in Propositional Logic

• Yifan Yang
• Ricardo de Aldama
• Jamal Atif
• Isabelle Bloch

ECAI Conference 2014 Conference Paper

Concept Dissimilarity Based on Tree Edit Distances and Morphological Dilations

• Felix Distel
• Jamal Atif
• Isabelle Bloch

A number of similarity measures for comparing description logic concepts have been proposed. Criteria have been developed to evaluate a measure's fitness for an application. These criteria include on the one hand those that ensure compatibility with the semantics, such as equivalence soundness, and on the other hand the properties of a metric, such as the triangle inequality. In this work we present two classes of dissimilarity measures that are at the same time equivalence sound and satisfy the triangle inequality: a simple dissimilarity measure, based on description trees for the lightweight description logic EL; and an instantiation of a general framework, presented in our previous work, using dilation operators from mathematical morphology, and which exploits the link between Hausdorff distance and dilations using balls of the ground distance as structuring elements.

KR Conference 2014 Short Paper

Concept Dissimilarity with Triangle Inequality

• Felix Distel
• Jamal Atif
• Isabelle Bloch

and (Lehmann and Turhan 2012) list amongst others the properties of a metric, in particular the triangle inequality, as well as soundness with respect to equivalence and subsumption. The triangle inequality has been somewhat controversial and in some applications such as (Janowicz and Wilkes 2009) it is not needed. In other applications such as metricbased conceptual clustering and distance-based optimization methods it is crucial (Fayyad et al. 1996). Unfortunately, even the measures presented in (Lehmann and Turhan 2012) and (d’Amato, Staab, and Fanizzi 2008) with their otherwise good theoretical properties do not satisfy the triangle inequality. Our results aim to provide knowledge engineers from these fields with an adequate measure. In this work, we give a general framework that can be used to construct concept dissimilarity measures with good theoretical properties, including the triangle inequality. The framework is based on concept relaxations, operators that can be used to successively make concepts more general. A directed distance between two concepts C and D can then be defined as the number of times D needs to be relaxed before it subsumes C. We show that the maximum of the two directed distances yields a good dissimilarity measure. Finally, we demonstrate ways to instantiate the framework. Several researchers have developed properties that ensure compatibility of a concept similarity or dissimilarity measure with the formal semantics of Description Logics. While these authors have highlighted the relevance of the triangle inequality, none of their proposed dissimilarity measures satisfy it. In this work we present a theoretical framework for dissimilarity measures with this property. Our approach is based on concept relaxations, operators that perform stepwise generalizations on concepts. We prove that from any relaxation we can derive a dissimilarity measure that satisfies a number or properties that are important when comparing concepts.

ECAI Conference 2010 Conference Paper

Integrating Bipolar Fuzzy Mathematical Morphology in Description Logics for Spatial Reasoning

• Céline Hudelot
• Jamal Atif
• Isabelle Bloch

Bipolarity is an important feature of spatial information, involved in the expression of preferences and constraints about spatial positioning or in pairs of opposite spatial relations such as left and right. Another important feature is imprecision which has to be taken into account to model vagueness, inherent to many spatial relations (as for instance vague expressions such as close to, to the right of), and to gain in robustness in the representations. In previous works, we have shown that fuzzy sets and fuzzy mathematical morphology are appropriate frameworks, on the one hand, to represent bipolarity and imprecision of spatial relations and, on the other hand, to combine qualitative and quantitative reasoning in description logics extended with fuzzy concrete domains. The purpose of this paper is to integrate the bipolarity feature in the latter logical framework based on bipolar and fuzzy mathematical morphology and description logics with fuzzy concrete domains. Two important issues are addressed in this paper: the modeling of the bipolarity of spatial relations at the terminological level and the integration of bipolar notions in fuzzy description logics. At last, we illustrate the potential of the proposed formalism for spatial reasoning on a simple example in brain imaging.

ECAI Conference 2008 Conference Paper

Sequential spatial reasoning in images based on pre-attention mechanisms and fuzzy attribute graphs

• Geoffroy Fouquier
• Jamal Atif
• Isabelle Bloch

Spatial relations play a crucial role in model-based image recognition and interpretation due to their stability compared to many other image appearance characteristics, and graphs are well adapted to represent such information. Sequential methods for knowledgebased recognition of structures require to define in which order the structures have to be recognized, which can be expressed as the optimization of a path in the representation graph. We propose to integrate pre-attention mechanisms in the optimization criterion, in the form of a saliency map, by reasoning on the saliency of spatial area defined by spatial relations. Such mechanisms extract knowledge from an image without object recognition in advance and do not require any a priori knowledge on the image. Therefore, pre-attentional mechanisms provide useful knowledge for object segmentation and recognition. The derived algorithms are applied on brain image understanding.

ECAI Conference 2008 Conference Paper

Structure segmentation and recognition in images guided by structural constraint propagation

• Olivier Nempont
• Jamal Atif
• Elsa D. Angelini
• Isabelle Bloch

In some application domains, such as medical imaging, the objects that compose the scene are known as well as some of their properties and their spatial arrangement. We can take advantage of this knowledge to perform the segmentation and recognition of structures in medical images. We propose here to formalize this problem as a constraint network and we perform the segmentation and recognition by iterative domain reductions, the domains being sets of regions. For computational purposes we represent the domains by their upper and lower bounds and we iteratively reduce the domains by updating their bounds. We show some preliminary results on normal and pathological brain images.

IJCAI Conference 2007 Conference Paper

• Jamal Atif
• C
• eacute; line Hudelot
• Geoffroy Fouquier
• Isabelle Bloch
• Elsa Angelini

In several domains of spatial reasoning, such as medical image interpretation, spatial relations between structures play a crucial role since they are less prone to variability than intrinsic properties of structures. Moreover, they constitute an important part of available knowledge. We show in this paper how this knowledge can be appropriately represented by graphs and fuzzy models of spatial relations, which are integrated in a reasoning process to guide the recognition of individual structures in images. However pathological cases may deviate substantially from generic knowledge. We propose a method to adapt the knowledge representation to take into account the influence of the pathologies on the spatial organization of a set of structures, based on learning procedures. We also propose to adapt the reasoning process, using graph based propagation and updating.

ECAI Conference 2006 Conference Paper

Mediation in the Framework of Morpho-Logic

• Isabelle Bloch
• Ramón Pino Pérez
• Carlos Uzcátegui

In this paper we introduce a median operator between two sets of interpretations (worlds) in a finite propositional language. Our definition is based on morphological operations and Hausdorff distance. It provides a result which lies “halfway” between both sets and accounts for the “extension” or “shape” of the sets. We prove several interesting properties of this operator and compare it with fusion operators. This new operator allows performing mediation between two sets of beliefs, preferences, demands, in an original way, showing an interesting behavior that was not possible to achieve using existing operators.

KR Conference 2004 Conference Paper

Explanations as an unifying tool for knowledge dynamics

• Isabelle Bloch
• Ramon Pino-Perez
• Carlos Uzcategui

AIJ Journal 2003 Journal Article

Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition—Application to 3D brain imaging

• Isabelle Bloch
• Thierry Géraud
• Henri Maître

We present a novel approach to model-based pattern recognition where structural information and spatial relationships have a most important role. It is illustrated in the domain of 3D brain structure recognition using an anatomical atlas. Our approach performs segmentation and recognition of the scene simultaneously. The solution of the recognition task is progressive, processing successively different objects, and using different pieces of knowledge about the object and about relationships between objects. Therefore, the core of the approach is the knowledge representation part, and constitutes the main contribution of this paper. We make use of a spatial representation of each piece of information, as a spatial fuzzy set representing a constraint to be satisfied by the searched object, thanks in particular to fuzzy mathematical morphology operations. Fusion of these constraints allows us to select, segment and recognize the desired object.

YNIMG Journal 2001 Journal Article

F.E.M. tetrahedral mesh of head tissues from M.R.I. under geometrical and topological constraints for applications in E.E.G. and M.E.G.

• Jeremie Pescatore
• Isabelle Bloch
• Sylvain Baillet
• L. Garnero