Pattern Recognition and Signal Analysis in Medical Imaging

Medical imaging is one of the heaviest funded biomedical engineering research areas. The second edition of Pattern Recognition and Signal Analysis in Medical Imaging brings sharp focus to the development of integrated systems for use in the clinical sector, enabling both imaging and the automatic assessment of the resultant data.

Since the first edition, there has been tremendous development of new, powerful technologies for detecting, storing, transmitting, analyzing, and displaying medical images. Computer-aided analytical techniques, coupled with a continuing need to derive more information from medical images, has led to a growing application of digital processing techniques in cancer detection as well as elsewhere in medicine.

This book is an essential tool for students and professionals, compiling and explaining proven and cutting-edge methods in pattern recognition for medical imaging.


Table of Content:

  1. Introduction
  2. Feature Selection and Extraction
  3. Subband Coding and Wavelet Transform
  4. The Wavelet Transform in Medical Imaging
  5. Genetic Algorithms
  6. Statistical and Syntactic Pattern Recognition
  7. Foundations of Neural Networks
  8. Transformation and Signal-Separation Neural Networks
  9. Neuro-Fuzzy Classification
  10. Specialized Neural Networks Relevant to Bioimaging
  11. Spatio-Temporal Models in Functional and Perfusion Imaging
  12. Analysis of Dynamic Susceptibility Contrast MRI Time-Series Based on Unsupervised Clustering Methods
  13. Computer-Aided Diagnosis for Diagnostically Challenging Breast Lesions in DCE-MRI
Volker J Schmid
Professor for Bayesian Imaging and Spatial Statistics

Volker J Schmid ist Professor für Statistik mit Schwerpunkt Bayesianische Statistik für räumliche und Bilddaten.