Screen LibraryPattern 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 as...
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| Published: |
Academic Press,
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| Publisher Address: | Oxford, UK : |
| Publication Dates: | 2014. |
| Literature type: | eBook |
| Language: | English |
| Edition: | Second edition. |
| Subjects: | |
| Online Access: |
http://www.sciencedirect.com/science/book/9780124095458 |
| Summary: |
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 deriv. |
| Carrier Form: | 1 online resource (466 pages) |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9780124166158 (electronic bk.) 0124166156 (electronic bk.) 1306548454 (electronic bk.) 9781306548458 (electronic bk.) 0124095453 9780124095458 |
| Index Number: | RC78 |
| CLC: | R445 |
| Contents: |
Half title; Title Page; Copyright; Dedication; Contents; Foreword; Preface; Acknowledgments; List of Symbols; 1 Introduction; 1.1 Model for Medical Image Processing; 1.2 Medical Image Analysis; 1.2.1 Imaging with Ionizing Radiation; 1.2.2 Magnetic Resonance Imaging; 1.2.3 Ultrasound and Acoustic Imaging; 1.3 Computer-Aided Diagnosis (CAD) Systems; 1.3.1 CAD Workstation; 2 Feature Selection and Extraction; 2.1 Introduction; 2.2 Role of Feature Selection and Extraction; 2.3 Preliminary Notations for Feature Selection and Extraction; 2.4 Feature Extraction Methods. 2.4.1 Nontransformed Signal Characteristics2.4.1.1 Moments; 2.4.1.2 Parametric Modeling; 2.4.2 Transformed Signal Characteristics; 2.4.2.1 Principal Component Analysis (PCA); 2.4.2.2 Discrete Fourier Transform; 2.4.2.3 Discrete Cosine and Sine Transform; 2.4.3 Advanced Techniques for Nontransformed Signal Characteristics and Transformed Signal Characteristics; 2.4.3.1 Krawtchouk Moments; 2.4.3.2 Zernike Moments; 2.4.3.3 Zernike Velocity Moments; 2.4.3.4 Writhe Number; 2.4.3.5 Minkowski Functionals; 2.4.4 Structural Descriptors; 2.4.5 Graph Descriptors; 2.4.6 Texture. 2.4.6.1 First-Order Statistics Features2.4.6.2 Second-Order Statistics Features; 2.4.6.3 Laws' Texture Energy Measures; 2.5 Gaussian Markov Random Fields; 2.5.1 Markov Random Field; 2.5.2 Ising Model; 2.5.3 Gaussian Markov Random Fields; 2.5.4 Latent GMRF; 2.5.5 Inferring from (Gaussian) Markov Random Fields; 2.6 Markov Chain Monte Carlo; 2.6.1 Metropolis-Hastings algorithms; 2.6.2 Gibbs Sampler; 2.6.3 Computational efficiency; 2.7 Feature Selection Methods; 2.7.1 Exhaustive Search; 2.7.2 Branch and Bound Algorithm; 2.7.3 Max-Min Feature Selection. 2.7.4 Sequential Forward and Sequential Backward Selection2.7.5 Fisher's Linear Discriminant; 2.8 Exercises; 3 Subband Coding and Wavelet Transform; 3.1 Introduction; 3.2 The Theory of Subband Coding; 3.2.1 Decimation and Interpolation; 3.2.2 Two-Channel Filter Banks; 3.2.3 The Laplacian Pyramid for Signal Decomposition; 3.3 The Wavelet Transform; 3.3.1 Time-Frequency Representation; 3.3.2 The Continuous Wavelet Transform; 3.4 The Discrete Wavelet Transformation; 3.5 Multiscale Signal Decomposition; 3.5.1 Multiscale-Analysis Spaces; 3.5.2 A Very Simple Wavelet: The Haar Wavelet. 3.5.3 Analogy Between Filter Banks and Wavelet Bases3.5.4 Multiscale Signal Decomposition and Reconstruction; 3.5.5 Wavelet Transformation at a Finite Resolution; 3.6 Overview: Types of Wavelet Transforms; 3.7 Exercises; 4 The Wavelet Transform in Medical Imaging; 4.1 Introduction; 4.2 The Two-Dimensional Discrete Wavelet Transform; 4.3 Biorthogonal Wavelets and Filter Banks; 4.4 Applications; 4.4.1 Multiscale Edge Detection; 4.4.2 Wavelet-Based Denoising and Contrast Enhancement; 4.4.3 Denoising by Thresholding; 4.4.4 Nonlinear Contrast Enhancement; 4.4.5 Image Fusion; 4.5 Exercises. |