Bibliothèque de L'IMIST/CNRST

Medical Imaging has become one of the most important visualization and interpretation methods in biology and medecine over the past decade. This time has witnessed a tremendous development of new, powerful instruments for detecting, storing, transmitting, analyzing, and displaying medical images. This has led to a huge growth in the application of digital processing techniques for solving medical problems. The most challenging aspect of medical imaging lies in the development of integrated systems for the use of the clinical sector. Design, implementation, and validation of complex medical systems requires a tight interdisciplinary collaboration between physicians and engineers because poor image quality leads to problematic feature extraction, analysis, and recognition in medical application. Therefore, much of the research done today is geared towards improvement of imperfect image material. This important book by academic authority Anke Meyer-Baese compiles and organizes a complete range of proven and cutting-edge new methods, which are playing a leading role in the improvement of image quality, analysis and interpretation in modern medical imaging. These methods offer fresh tools of hope for physicians investigating a vast number of medical problems for which classical methods prove insufficient. Each chapter in Pattern Recognition for Medical Imaging provides a chapter summary and bibliographic remarks for in-depth study. Each presented classification approach is elucidated by a flow-diagram and highlighted with practical medical imaging applications. This is an essential tool for the serious student and professional working with Medical Imaging. *Essential tool for serious students and professionals working with Medical Imaging.

Foundations of Medical Imaging; Feature Selection and Extraction; Theory of Subband Decomposition and Wavelets; The Wavelet Transform in Medical Imaging; Genetic Algorithms; Statistical Pattern Recognition; Syntactic Pattern Recognition; Neural Networks; Theory; Neural Networks: Applications; Fuzzy Logic: Theory and Clustering Algorithms; Computer Aided Diagnosis Systems.

Includes bibliographical references (p. 367-382) and index.

Description based on print version record.