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Pattern Recognition in Medical Imaging
 
 

Pattern Recognition in Medical Imaging [Hardcover]

Anke Meyer-Baese (Author)
5.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

0124932908 978-0124932906 November 14, 2003 1
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

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Book Description

Essential tool for students and professionals that compiles, organizes and thoroughly explains proven and new cutting-edge methods in pattern recognition for medical imaging

About the Author

By Anke Meyer-Baese

Product Details

  • Hardcover: 448 pages
  • Publisher: Academic Press; 1 edition (November 14, 2003)
  • Language: English
  • ISBN-10: 0124932908
  • ISBN-13: 978-0124932906
  • Product Dimensions: 9 x 6.1 x 1.1 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #1,989,558 in Books (See Top 100 in Books)

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5.0 out of 5 stars This is what you need to have!, February 14, 2005
This review is from: Pattern Recognition in Medical Imaging (Hardcover)
This is what I have always been looking for: A. Meyer-Baese presents an excellent book covering both a general introduction and a comprehensive survey on pattern recognition for biomedical purposes. I think the author provides a nice balance between theory and practice. The textbook can serve as an outstanding introduction of exceptional quality for everybody who wants to find precise, yet understandable discussions on how cutting-edge medical image analysis works, including methods for feature selection and extraction, wavelet analysis, neural networks and applications.

In summary, no matter whether you are a student or an expert interested in the field of biomedical pattern and image recognition: This book is what you need and a definite `must have'!
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Inside This Book (learn more)
First Sentence:
The visualization and analysis of small particles are important tasks in many biomedical sciences, biology, and medicine. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
multiscale signal decomposition, new pattern vector, modified hopfield neural network, resulting iterative procedure, texture energy measures, microcalcification detection, original mammogram, radial basis neural networks, pixel labeling, winning prototype, optimal fuzzy partition, spiculated lesions, cluster substructure, clustered microcalcifications, fractal texture, digital mammograms, abnormal class, syntactic pattern recognition, class separability, jth neuron, distance dij, fuzzy clustering algorithms, mammographic image, water phantom, tumor center
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Columbia University, Design Considerations, University of Munich
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