Biomechanics: Concepts and Computation (Cambridge Texts in Biomedical Engineering) Review

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(More customer reviews)The product arrived in the time that it said it would. The book was brand new and much more affordable than the ones at the book store found on my campus. I saved a ton of money by buying this online.

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This is the first textbook that integrates both general and specific topics, theoretical background and biomedical engineering applications, as well as analytical and numerical approaches. This quantitative approach integrates the classical concepts of mechanics and computational modelling techniques, in a logical progression through a wide range of fundamental biomechanics principles. Online MATLAB-based software along with examples and problems using biomedical applications will motivate undergraduate biomedical engineering students to practice and test their skills. The book covers topics such as kinematics, equilibrium, stresses and strains, and also focuses on large deformations and rotations and non-linear constitutive equations, including visco-elastic behaviour and the behaviour of long slender fibre-like structures. This is the definitive textbook for students.

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Biosignal and Medical Image Processing, Second Edition (Signal Processing and Communications) Review

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(More customer reviews)This book has too many typos, especially in important equations. That is very confusing to students who have no idea about signal processing.
And, I don't like the paper used to print this book. I prefer regular printing papers rather than the "mirror" paper that makes my eyeballs tired.

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A Practical Guide to Signal Processing MethodologyJust as a cardiologist can benefit from an oscilloscope-type display of the ECG without a deep understanding of electronics, an engineer can benefit from advanced signal processing tools without always understanding the details of the underlying mathematics. Through the use of extensive MATLAB examples and problems, Biosignal and Medical Image Processing, Second Edition provides readers with the necessary knowledge to successfully evaluate and apply a wide range of signal and image processing tools.The book begins with an extensive introductory section and a review of basic concepts before delving into more complex areas. Topics discussed include classical spectral analysis, basic digital filtering, advanced spectral methods, spectral analysis for time-variant spectrums, continuous and discrete wavelets, optimal and adaptive filters, and principal and independent component analysis. In addition, image processing is discussed in several chapters with examples taken from medical imaging. Finally, new to this second edition are two chapters on classification that review linear discriminators, support vector machines, cluster techniques, and adaptive neural nets. Comprehensive yet easy to understand, this revised edition of a popular volume seamlessly blends theory with practical application. Most of the concepts are presented first by providing a general understanding, and second by describing how the tools can be implemented using the MATLAB software package.Through the concise explanations presented in this volume, readers gain an understanding of signal and image processing that enables them to apply advanced techniques to applications without the need for a complex understanding of the underlying mathematics.A solutions manual is available for instructors wishing to convert this reference to classroom use.

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Biosignal and Medical Image Processing (Signal Processing and Communications) Review

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(More customer reviews)This book assumes that you have both a prior knowledge of Matlab and of signal processing concepts. It spends the first three chapters going over measurement and transducer systems, basic signals and systems, and classical methods of spectral analysis. Even though these chapters are meant to be a quick review, there are some Matlab implementations of basic algorithms in each chapter. Chapter four introduces digital filters and shows Matlab implementations of both IIR and FIR filters. A special treat of chapter four is that some time is spent introducing the reader to the Matab signal processing toolkit. Now that the basics of digital filtering have been introduced, more advanced signal processing techniques are tackled. These include modern methods of spectral analysis and also time-frequency analysis using such methods as the Wigner-Ville distribution. Again, in all cases, the equations are concise, the prose is very accessible, and all concepts are demonstrated using Matlab programs. There is a separate chapter devoted to the wavelet transform and to its use in filter banks, denoising, and feature detection. Quite frankly, I found this chapter far more accessible than entire books that have been devoted to the subject, especially if you are interested in getting to the heart of the matter and using wavelets to perform a task. Two more chapters round out the section of the book on general signal processing techniques- one is about adaptive filters and another on principal component analysis. The final four chapters of the book concern themselves with image processing and Matlab, and the Matlab image processing toolkit in particular. You should already be familiar with the basic concepts behind image processing, and as with the signal processing portion of this book, the point is to have a single text with all of the relevant signal processing techniques briefly described along with Matlab code for the purpose of biosignal processing. However, even if you are not a biomedical engineer, and I am not, you should find this book helpful in the general sense of producing implementations of signal processing concepts. This book would also be helpful for biometric professionals since it goes into great detail on how to turn biological features into measurements that can be processed and compared.

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Relying heavily on MATLAB problems and examples, as well as simulated data, this text/reference surveys a vast array of signal and image processing tools for biomedical applications, providing a working knowledge of the technologies addressed while showcasing valuable implementation procedures, common pitfalls, and essential application concepts. The first and only textbook to supply a hands-on tutorial in biomedical signal and image processing, it offers a unique and proven approach to signal processing instruction, unlike any other competing source on the topic. The text is accompanied by a CD with support data files and software including all MATLAB examples and figures found in the text.

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Image Processing with MATLAB: Applications in Medicine and Biology (MATLAB Examples) Review

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(More customer reviews)>>Update on 02/11/2010
I spent a few more weeks on this book recently with a bit more emphasis on the programming side. I find the programming part of this book is quite readable and it gives me much insight into the image processing topic. I hereby raise my rate to four stars.
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>>Original review in Oct.2009
I borrowed this book from the university library mainly because it provided with Matlab examples. This book turned out to be somewhat disappointing:
pros:
1. example Matlab codes, provided in context
2. that is all
cons:
Editing is a nightmare. I don't know why the authors can't spend some more hours proofreading the pre-print they have already spent hundreds of times more hours on.
1. Typos and mistakes are literally on every page.
2. Lots of illustrations other than figures generated by Matlab look like being drawn using paint.exe in Windows. Imaging the quality.
3. Equations are edited in a weird improportionate fashion that the symbols are ugly. Dear lord please use Tex while editing equations. I bet even the equation editor integrated with M$ office 2007 can do better than this.I think it is only worth 1 star at most, as a science and technology book. Since my first intention of borrowing this book is the matlab code, so 2 stars.

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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.

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