Synopses & Reviews
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.
Focusing on the biological and medical applications of image processing, illustrated with real-world examples, this book provides an understanding of image processing techniques that goes beyond theory and fundamentals. The authors discuss advanced subjects, such as mixture modeling and Markov random field modeling based image segmentation, as well as emerging topics including anisotropic diffusion filtering. This comprehensive text provides MATLAB(R) codes, exercises, homework problems, and case studies, as well as a wide range of algorithms that can be used separately or in combination with a variety of applications.