Handbook of Biomedical Imaging

Methodologies and Clinical Research

  • Nikos Paragios
  • James Duncan
  • Nicholas Ayache

Table of contents

  1. Front Matter
    Pages i-vii
  2. Methodologies

    1. Front Matter
      Pages 1-1
    2. I. Bloch
      Pages 25-44
    3. G. Charpiat, M. Hofmann, B. Schölkopf
      Pages 63-81
    4. Y. Bai, X. Han, J. L. Prince
      Pages 83-104
    5. T. F. Cootes, M. G. Roberts, K. O. Babalola, C. J. Taylor
      Pages 105-122
  3. Statistical & Physiological Models

    1. Front Matter
      Pages 123-123
    2. C. Davatzikos, R. Verma, D. Shen
      Pages 125-145
    3. D. M. McQueen, T. O’Donnell, B. E. Griffith, C. S. Peskin
      Pages 183-197
    4. A. G. Radaelli, H. Bogunović, M. C. Villa Uriol, J. R. Cebral, A. F. Frangi
      Pages 199-217
  4. Biomedical Perception

    1. Front Matter
      Pages 219-219
    2. M. Bach Cuadra, V. Duay, J.-Ph. Thiran
      Pages 221-244
    3. C. Florin, N. Paragios, J. Williams
      Pages 263-275
    4. D. Rueckert, P. Aljabar
      Pages 277-294
    5. F. Maes, D. Loeckx, D. Vandermeulen, P. Suetens
      Pages 295-308
    6. P. Yang, C. Delorenzo, X. Papademetris, J. S. Duncan
      Pages 309-329
    7. A. Sotiras, Y. Ou, N. Paragios, C. Davatzikos
      Pages 331-359
  5. Clinical Biomarkers

    1. Front Matter
      Pages 361-361
    2. I. A. Kakadiaris, U. Kurkure, A. Bandekar, S. O’Malley, M. Naghavi
      Pages 363-374
    3. G. Langs, P. Peloschek, H. Bischof, F. Kainberger
      Pages 375-389
    4. N. Navab, B. Glocker, O. Kutter, S. M. Kirchhoff, M. Reiser
      Pages 391-401
    5. R. Beichel, C. Bauer, A. Bornik, E. Sorantin, H. Bischof
      Pages 403-412
  6. Emerging Modalities & Domains

    1. Front Matter
      Pages 413-413
    2. J. B. Poline, P. Ciuciu, A. Roche, B. Thirion
      Pages 415-435
    3. R. Neji, N. Azzabou, G. Fleury, N. Paragios
      Pages 437-454
    4. M. Descoteaux, R. Deriche
      Pages 455-473
    5. A. Kuijper, B. Heise, Y. Zhou, L. He, H. Wolinski, S. Kohlwein
      Pages 475-487
    6. M. Baiker, J. Dijkstra, J. Milles, C. W. G. M. Löwik, B. P. F. Lelieveldt
      Pages 489-500
    7. S. Golemati, J. Stoitsis, K. S. Nikita
      Pages 501-511

About this book


Biomedical image analysis has become a major aspect of engineering sciences, and radiology in particular has become a dominant player in the field. Recent developments have made it possible to use biomedical imaging to view the human body from an anatomical or physiological perspective in a non-invasive fashion. Computer-aided diagnosis consists of developing algorithms and intelligent software components that can automatically process images and spot potential irregularities in the health chain.

This book explains the process of computer assisted biomedical image analysis diagnosis through mathematical modeling and inference of image-based bio-markers. It covers five crucial thematic areas: methodologies, statistical and physiological models, biomedical perception, clinical biomarkers, and emerging modalities and domains.

The dominant state-of-the-art methodologies for content extraction and interpretation of medical images include fuzzy methods, level set methods, kernel methods, and geometric deformable models. The models and techniques discussed are used in the diagnosis, planning, control and follow-up of medical procedures. Throughout the book, challenges and limitations are explored along with new research directions.

This complete volume is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors. This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses.


Algorithms Atlas-based Automated diagnosis Bioinformatics Biomedical perception Curve propogation Fuzzy methods Health chain Image analysis Intelligent software Medical image processing Medical image segmentation Medical imaging Patient-specific models Radiology Statistical computing

Editors and affiliations

  • Nikos Paragios
    • 1
  • James Duncan
    • 2
  • Nicholas Ayache
    • 3
  1. 1.Department of Applied MathematicsÉcole Centrale de Paris / INRIA Saclay, Île De FranceChatenay-MalabryFrance
  2. 2.Department of Biomedical Engineering, Diagnostic Radiology and Electrical EngineeringYale UniversityNew HavenUSA
  3. 3.INRIA Sophia Antipolis - MéditerranéeSophia Antipolis CedexFrance

Bibliographic information