François Chung, Ph.D.

Tag: medical imaging

CVIU 2013 - Academic journal

CVIU 2013 – Academic journal article

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Publication

François Chung, Hervé Delingette; Regional appearance modeling based on the clustering of intensity profiles; In: Computer Vision and Image Understanding (CVIU), 117 (6), pp. 705-717, 2013.

Abstract

Model-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This paper focuses on the formulation of a novel appearance prior that can cope with large variability between subjects, for instance due to the presence of pathologies. Instead of relying on Principal Component Analysis (PCA) such as in Statistical Appearance Models (SAMs), our approach relies on a multimodal intensity profile atlas from which a point may be assigned to several profile modes consisting of a mean profile and its covariance matrix. These profile modes are first estimated without any intra-subject registration through a boosted Expectation-Maximization (EM) classification based on spectral clustering. Then, they are projected on a reference mesh whose role is to store the appearance information in a common geometric representation. We show that this prior leads to better performance than the classical monomodal PCA approach while relying on fewer profile modes.

Keywords

  • appearance modeling
  • medical imaging
  • model-based image segmentation
  • unsupervised clustering

References

Publication

Published version (ScienceDirect)
Preprint version (PDF)
Bibliographic reference (BibTeX)

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3D Anatomical Human (INRIA project)
Ph.D. Thesis 2011 (doctoral thesis)

GIMIAS framework

GIMIAS framework

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UPF project @Barcelona, Spain (2012). GIMIAS (Graphical Interface for Medical Image Analysis and Simulation) is a workflow-oriented environment for solving biomedical image computing and simulation problems, which is extensible through the development of problem-specific plugins. In addition, GIMIAS provides an open source framework for the development of research and clinical software prototypes while allowing business-friendly technology transfer.

GIMIAS is particularly tailored to integrate tools from medical imaging, computational modeling, numerical methods and computer graphics to provide scientific developers and researchers with a software framework allowing them to build a wide variety of tools. The aim of GIMIAS is to combine tools from different areas of knowledge, thus providing a framework for multi-disciplinary research, clinical study and commercial product development.

Some of the main features of GIMIAS include:

  • multi-modal image processing;
  • personalized model creation;
  • numerical simulation;
  • visualization of simulation results.

As a Scientific Software Engineer within the GIMIAS team, my work consists in developing, optimizing, testing and installing software solutions for orthopedic applications. More precisely, I am in charge of the software development for the EU FP7-funded MySpine project and for both Catalonia ACC1Ó-funded 3D-FemOs and VERTEX projects. MySpine aims to create a clinical predictive tool to provide the clinicians with patient-specific biomechanical analysis. 3D-FemOs and VERTEX aim to improve both the diagnosis of osteoporosis and the prevention of hip (3D-FemOs) and vertebral (VERTEX) fractures.

References

Related article

VPH 2012 (conference proceeding)

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GIMIAS – Graphical Interface for Medical Image Analysis and Simulation
MySpine

Mines 2018 – Magazine

Mines 2012 – Magazine article

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Publication

François Chung; De l'imagerie médicale à la médecine du futur; Mines Revue des Ingénieurs, 458, pp. 53-56, 2012.

Abstract

Depuis ses débuts, l'imagerie médicale a pour objectif de fournir aux radiologues des images médicales afin de les aider dans leur diagnostic. Avec l'avancée des techniques d'acquisition, les radiologues se retrouvent à analyser des images de plus en plus complexes et dans des quantités de plus en plus importantes. Du côté de la recherche, cela se traduit par une collaboration entre physique médicale, radiologie et imagerie médicale. Les physiciens ont pour objectif d'améliorer la qualité et la résolution des images médicales. Ces améliorations permettent d'aider les radiologues dans leur diagnostic et à la communauté de l'imagerie médicale de pouvoir extraire des informations plus précises. Cette collaboration permet non seulement d'avancer dans les sciences médicales (ex. étude de l'anatomie et physiologie), mais également dans les applications cliniques (ex. détection de maladies et planification de thérapie).

References

Publication

Published version (PDF)
Bibliographic reference (BibTeX)
Online version (Ingénieurs Belges)

L'Ing. 2012 - Magazine

L’Ing. 2012 – Magazine article

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Publication

François Chung; L'imagerie médicale: Un domaine d'ingénieurie et de recherche au service de la société; L'Ing., 17, pp. 10-12, 2012.

Abstract

Depuis ses débuts, l'imagerie médicale a pour objectif de fournir aux radiologues des images médicales afin de les aider dans leur diagnostic. Avec l'avancée des techniques d'acquisition, les radiologues se retrouvent à analyser des images de plus en plus complexes et dans des quantités de plus en plus importantes. Du côté de la recherche, cela se traduit par une collaboration entre physique médicale, radiologie et imagerie médicale. Les physiciens ont pour objectif d'améliorer la qualité et la résolution des images médicales. Ces améliorations permettent d'aider les radiologues dans leur diagnostic et à la communauté de l'imagerie médicale de pouvoir extraire des informations plus précises. Cette collaboration permet non seulement d'avancer dans les sciences médicales (ex. étude de l'anatomie et physiologie), mais également dans les applications cliniques (ex. détection de maladies et planification de thérapie).

References

Publication

Published version (PDF)
Bibliographic reference (BibTeX)

LAP 2011 - Book

LAP 2011 – Book

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Publication

François Chung; Regional appearance modeling for model-based image segmentation: Methodological approaches to improve the accuracy of model-based image segmentation; Lambert Academic Publishing (LAP), Saarbrücken, 2011; ISBN: 978-3844322095.

Abstract

This thesis presents a novel appearance prior for model-based image segmentation. This appearance prior, denoted as Multimodal Prior Appearance Model (MPAM), is built upon an Expectation–Maximization (EM) clustering of intensity profiles with model order selection to automatically select the number of profile classes. Unlike classical approaches based on Principal Component Analysis (PCA), the clustering is considered as regional because intensity profiles are classified for each mesh and not for each vertex. Comparative results on liver profiles from Computed Tomography (CT) images show that MPAM outperforms PCA-based appearance models. Finally, methods for the analysis of lower limb structures from Magnetic Resonance (MR) images are presented. A first part deals with the creation of subject-specific models for kinematic simulations of the lower limbs. In a second part, the performance of statistical models is compared in the context of lower limb bone segmentation when only a small number of datasets is available for training.

References

Publication

Book (Amazon)
Book (MoreBooks)
Bibliographic reference (BibTeX)

Related articles

3D Anatomical Human (INRIA project)
Ph.D. Thesis 2011 (doctoral thesis)

Learn more

LAP – Lambert Academic Publishing

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