Abstract:

Automatic building of morphometric anatomical atlases from volumetric medical images

In this thesis, we address the new problem of automated building of morphometric anatomical atlases from volumetric medical images (X-ray CT or Magnetic Resonance Imaging).

We describe a method composed of five steps. First, we automatically extract feature lines from the volumetric images. We use "crest lines" which are a simplified representation of the studied structure and which are anatomically significant. Then, we have developed a non-rigid matching algorithm to register sets of lines extracted from different patients in order to find the common lines which form the atlas. We then compute their average positions and a very reduced set of statistical parameters describing their variation.

This method has allowed us to build an atlas of crest lines of the skull from 6 CT-Scans and of the brain from 10 MRI images.

The statistical parameters can be used for several medical applications: reference database with automatic labeling, normalized registration, computer assisted therapy planning, morphometric analysis, computer aided diagnosis and therapy planning. We study, in particular, a maxillary disease and the deformations of cerebral lateral ventricles.

We also present some applications of the tools developed for the atlas building to the growth of a child's head, registration between MRI brain images of different patients and the study of the deformation of the left cardiac ventricle during the cardiac cycle.

How to get the thesis (in French)?

The PostScript file of the Ph.D thesis is available. As, the file is huge (about 120Mb!!!), it has been separated into 4 sub-files that were compressed with the command "gzip".

  • these.1.ps.gz (2.1 Mbytes): pages 1-52.

  • these.2.ps.gz (3.1 Mbytes): pages 53-112.

  • these.3.ps.gz (5.2 Mbytes): pages 113-172.

  • these.4.ps.gz (7.9 Mkbytes): pages 172-266.

    Table of contents (between parenthesis, the page number):

    1. Introduction (1)
      1. Présentation du problème (3)
      2. Description générale de la méthode (23)
    2. Les différentes étapes de la méthode (33)
      1. Extraction des caractéristiques (35)
      2. Mise en correspondance des caractéristiques (71)
      3. Extraction des ensembles de caractéristiques communes (119)
      4. Moyenne des caractéristiques communes (137)
      5. Analyse statistique des caractéristiques communes (169)
    3. Résultats et perspectives (187)
      1. Quelques applications médicales (189)
        1. Introduction (189)
        2. Une application craniofaciale : étude d'une déformation maxillaire (190)
        3. Une application neurologique : étude des déformations ventriculaires (197)
        4. Étude et simulation de la croissance de la tête d'un enfant (209)
        5. Mise en correspondance avec un atlas du cerveau (215)
        6. Une application en cardiologie : étude du mouvement cardiaque (218)
        7. Une perspective d'application en paléontologie : comparaison d'un crâne préhistorique et contemporain (227)
      2. Conclusion et perspectives (231)
    4. Bibliographie (239)