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Heart deformation pattern analysis through shape modelling

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Abstract

In this paper, we present an approach to the description of time-varying anatomical structures. The main goal is to compactly but faithfully describe the whole heart cycle in such a way to allow for deformation pattern characterization and assessment. Using such an encoding, a reference database can be built, thus permitting similarity searches or data mining procedures.

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Authors and Affiliations

  1. Institute of Information Science and Technologies (ISTI), Italian National Research Council (CNR), Pisa, Italy

    D. Moroni, S. Colantonio & O. Salvetti

  2. Department of Mathematics, University of Pisa, Pisa, Italy

    M. Salvetti

Authors
  1. D. Moroni
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  2. S. Colantonio
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  3. O. Salvetti
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  4. M. Salvetti
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Corresponding author

Correspondence to D. Moroni.

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The article is published in the original.

Davide Moroni (Magenta, 1977), M.Sc. in Mathematics honours degree from the University of Pisa in 2001, dipl. at the Scuola Normale Superiore of Pisa in 2002, Ph.D. in Mathematics at the University of Rome “La Sapienza” in 2006, is a research fellow at the Institute of Information Science and Technologies of the Italian National Research Council, in Pisa. His main interests include geometric modelling, computational topology, image processing and medical imaging. At present he is involved in a number of European research projects working in discrete geometry and scene analysis.

Ovidio Salvetti. Director of research at the Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa. Working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling, and intelligent processes in computer vision. Coauthor of four books and monographs and more than 300 technical and scientific articles, with ten patents regarding systems and software tools for image processing. Has served as a scientific coordinator of several national and European research and industrial projects, in collaboration with Italian and foreign research groups, in the fields of computer vision and high-performance computing for diagnostic imaging, Member of the editorial boards of the international journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. Currently the CNR contact person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on Vision and Image Understanding and a member of IEEE and of the steering committee of a number of EU projects. Head of the ISTI Signals and Images Laboratory.

Sara Colantonio, M.Sc. degree with honors in computer science, University of Pisa, 2004; Ph.D. student in information engineering at the Department of Information Engineering, Pisa University; temporary researcher at the Institute of Information Science and Technologies, National Research Council, Pisa. Received a grant from Finmeccanica for studies in the field of image categorization with applications in medicine and quality control. Her main interests include neural networks, machine learning, industrial diagnostics, and medical imaging. Coauthor of more than 30 scientific papers. Currently involved in a number of European research projects regarding image mining, information technology, and medical decision support systems.

Mario Salvetti. Full professor at the Department of Mathematics, University of Pisa, Italy, of which he is chairman since 2003. His fields of research include Artin and braid groups, Coxeter groups, group cohomology and hyperplane arrangements. Since 2006, he is a research associate at the Institute of Information Science and Technologies of the Italian National Research Council, in Pisa, where he is collaborating in a number of research projects regarding discrete geometry.

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Moroni, D., Colantonio, S., Salvetti, O. et al. Heart deformation pattern analysis through shape modelling. Pattern Recognit. Image Anal. 19, 262–270 (2009). https://doi.org/10.1134/S1054661809020084

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