Image Processing with MATLAB

Image Processing with MATLAB: Applications in Medicine and Biology

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Features

  • Provides MATLAB algorithms to help scientists and researchers quickly identify the most effective solution method for a particular problem at hand
  • Enables a diverse set of readers to comprehend the material by striking the right balance between theory and practice
  • Emphasizes three-dimensional processing and analysis as well as statistical and stochastic modeling
  • Covers the new areas of nonlinear diffusion filtering and PDE-based image filtering
  • Addresses relatively advanced topics, such as the segmentation method based on MRF modeling
  • Highlights applications with images from medicine and biology
  • Includes real-world examples and exercises in every chapter

Summary

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.

A solutions manual is available for instructoes wishing to convert this reference to classroom use.

Table of Contents

Medical Imaging Systems
Fundamental Tools for Image Processing and Analysis
Probability Theory for Stochastic Modeling of Images
Two-Dimensional Fourier Transform
Nonlinear Diffusion Filtering
Intensity-Based Image Segmentation
Image Segmentation by Markov Random Field Modeling
Deformable Models
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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