| Titre : | Image Processing with MATLAB : applications in medicine and biology | | Type de document : | texte imprimé | | Auteurs : | Omer Demirkaya, Auteur ; Musa Hakan Asyali, Auteur ; Prasanna K. Sahoo, Auteur | | Editeur : | Boca Raton; London; New York : CRC Press/Taylor & Françis Group | | Année de publication : | 209 | | Collection : | Electrical Engineering | | Importance : | 441 p. | | Présentation : | couv. ill. en coul., ill. | | Format : | 24 cm. | | ISBN/ISSN/EAN : | 978-0-84939-246-7 | | Langues : | Anglais (eng) | | Catégories : | AUTOMATISME
| | Index. décimale : | 25-05 Application du traitement numérique du signal | | Résumé : | Image Processing with MATLAB®: Applications in Medicine and Biology explains complex, theory-laden topics in image processing through examples and MATLAB® algorithms. It describes classical as well emerging areas in image processing and analysis.
Providing many unique MATLAB codes and functions throughout, the book covers the theory of probability and statistics, two-dimensional fast Fourier transform, nonlinear diffusion filtering, and partial differential equation (PDE)-based image denoising techniques. It presents intensity-based image segmentation methods, including thresholding techniques as well as K-means and fuzzy C-means clustering techniques. The authors also explore Markov random field (MRF)-based image segmentation, boundary and curvature analysis methods, and parametric and geometric deformable models. The final chapters focus on three specific applications of image processing and analysis.
Reducing the need for the trial-and-error way of solving problems, this book helps readers understand advanced concepts by applying algorithms to real-world problems in medicine and biology. | | Note de contenu : | Contents
Chapter 1. Medical Imaging Systems
Chapter 2. Fundamental Tools for Image Processing and Analysis
Chapter 3. Probability Theory for Stochastic Modeling of Images
Chapter 4. Two-Dimensional Fourier Transform
Chapter 5. Nonlinear Diffusion Filtering
Chapter 6. Intensity-Based Image Segmentation
Chapter 7. Image Segmentation by Markov Random Field Modeling
Chapter 8. Deformable Models
Chapter 9. Image Analysis
Application 1: Quantification of Green Fluorescent Protein eXpression in Live Cells: ProXcell
Application 2: Calculation of Performance Parameters of Gamma Cameras and SPECT Systems
Application 3: Analysis of Islet Cells Using Automated Color Image Analysis
Appendix A: Notation
Appendix B: Working with DICOM Images
Appendix C: Medical Image Processing Toolbox
Appendix D: Description of Image Data |
Image Processing with MATLAB : applications in medicine and biology [texte imprimé] / Omer Demirkaya, Auteur ; Musa Hakan Asyali, Auteur ; Prasanna K. Sahoo, Auteur . - Boca Raton; London; New York : CRC Press/Taylor & Françis Group, 209 . - 441 p. : couv. ill. en coul., ill. ; 24 cm.. - ( Electrical Engineering) . ISBN : 978-0-84939-246-7 Langues : Anglais ( eng) | Catégories : | AUTOMATISME
| | Index. décimale : | 25-05 Application du traitement numérique du signal | | Résumé : | Image Processing with MATLAB®: Applications in Medicine and Biology explains complex, theory-laden topics in image processing through examples and MATLAB® algorithms. It describes classical as well emerging areas in image processing and analysis.
Providing many unique MATLAB codes and functions throughout, the book covers the theory of probability and statistics, two-dimensional fast Fourier transform, nonlinear diffusion filtering, and partial differential equation (PDE)-based image denoising techniques. It presents intensity-based image segmentation methods, including thresholding techniques as well as K-means and fuzzy C-means clustering techniques. The authors also explore Markov random field (MRF)-based image segmentation, boundary and curvature analysis methods, and parametric and geometric deformable models. The final chapters focus on three specific applications of image processing and analysis.
Reducing the need for the trial-and-error way of solving problems, this book helps readers understand advanced concepts by applying algorithms to real-world problems in medicine and biology. | | Note de contenu : | Contents
Chapter 1. Medical Imaging Systems
Chapter 2. Fundamental Tools for Image Processing and Analysis
Chapter 3. Probability Theory for Stochastic Modeling of Images
Chapter 4. Two-Dimensional Fourier Transform
Chapter 5. Nonlinear Diffusion Filtering
Chapter 6. Intensity-Based Image Segmentation
Chapter 7. Image Segmentation by Markov Random Field Modeling
Chapter 8. Deformable Models
Chapter 9. Image Analysis
Application 1: Quantification of Green Fluorescent Protein eXpression in Live Cells: ProXcell
Application 2: Calculation of Performance Parameters of Gamma Cameras and SPECT Systems
Application 3: Analysis of Islet Cells Using Automated Color Image Analysis
Appendix A: Notation
Appendix B: Working with DICOM Images
Appendix C: Medical Image Processing Toolbox
Appendix D: Description of Image Data |
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Image Processing with MATLAB / Omer Demirkaya
