Statistical And Computational Methods In Brain Image Analysis

The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.

The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.

Computational neuroanatomy is an emerging field that utilizes various non-invasive brain imaging modalities, such as MRI and DTI, in quantifying the spatiotemporal dynamics of the human brain structures in both normal and clinical populations. This discipline emerged about twenty years ago and has made substantial progress in the past decade. The main goals of this book are to provide an overview of various mathematical, statistical and computational methodologies used in the field to a wide range of researchers and students, and to address important yet technically challenging topics in further detail.

Ch. 1. Modeling for medical image analysis : framework and applications / Marek Kretowski and Johanne Bézy-Wendling -- ch. 2. Biomechanical models for image analysis and simulation / M. Sermesant, H. Delingette and N. Ayache -- ch. 3. Techniques in fractal analysis and their applications in brain MRI / Khan M. Iftekharuddin -- ch. 4. Techniques in infrared microspectroscopy and advanced computational methods for colon cancer diagnosis / S. Mordechai ... [et al.] -- ch. 5. Advances in computerized image analysis methods on breast ultrasound / Anant Madabhushi and Dimitris N. Metaxas -- ch. 6. Techniques in blind deblurring of spiral computed tomography images and their applications / Ming Jiang and Jing Wang -- ch. 7. Model-based 3D encoding/2D decoding of medical imaging data / G. Menegaz -- ch. 8. Interpolation techniques in multimodality image registration and their application / Jeffrey Tsao, Jim Xiuquan Ji and Zhi-Pei Liang -- ch. 9. Automatic construction of cardiac statistical shape models : applications in SPECT and MR imaging / Sebastián Ordás and Alejandro F. Frangi -- ch. 10. Techniques for mutual information-based brain image. Registration and their applications / Hua-Mei Chen and Pramod K. Varshney -- ch. 11. Iterative algebraic algorithms for image reconstruction / Ming Jiang

With strong numerical and computational focus, this book serves as an essential resource on the methods for functional neuroimaging analysis, diffusion weighted image analysis, and longitudinal VBM analysis. It includes four MRI image modalities analysis methods. The first covers the PWI methods, which is the basis for understanding cerebral flow in human brain. The second part, the book’s core, covers fMRI methods in three specific domains: first level analysis, second level analysis, and effective connectivity study. The third part covers the analysis of Diffusion weighted image, i.e. DTI, QBI and DSI image analysis. Finally, the book covers (longitudinal) VBM methods and its application to Alzheimer’s disease study.

The study of brain function is one of the most fascinating pursuits of m- ern science. Functional neuroimaging is an important component of much of the current research in cognitive, clinical, and social psychology. The exci- ment of studying the brain is recognized in both the popular press and the scienti?c community. In the pages of mainstream publications, including The New York Times and Wired, readers can learn about cutting-edge research into topics such as understanding how customers react to products and - vertisements (“If your brain has a ‘buy button,’ what pushes it?”, The New York Times,October19,2004),howviewersrespondtocampaignads(“Using M. R. I. ’s to see politics on the brain,” The New York Times, April 20, 2004; “This is your brain on Hillary: Political neuroscience hits new low,” Wired, November 12,2007),howmen and womenreactto sexualstimulation (“Brain scans arouse researchers,”Wired, April 19, 2004), distinguishing lies from the truth (“Duped,” The New Yorker, July 2, 2007; “Woman convicted of child abuse hopes fMRI can prove her innocence,” Wired, November 5, 2007), and even what separates “cool” people from “nerds” (“If you secretly like Michael Bolton, we’ll know,” Wired, October 2004). Reports on pathologies such as autism, in which neuroimaging plays a large role, are also common (for - stance, a Time magazine cover story from May 6, 2002, entitled “Inside the world of autism”).

This volume contains original submissions on the development and application of molecular imaging computing. The editors invited authors to submit high-quality contributions on a wide range of topics including, but not limited to: • Image Synthesis & Reconstruction of Emission Tomography (PET, SPECT) and other Molecular Imaging Modalities • Molecular Imaging Enhancement • Data Analysis of Clinical & Pre-clinical Molecular Imaging • Multi-Modal Image Processing (PET/CT, PET/MR, SPECT/CT, etc.) • Machine Learning and Data Mining in Molecular Imaging. Molecular imaging is an evolving clinical and research discipline enabling the visualization, characterization and quantification of biological processes taking place at the cellular and subcellular levels within intact living subjects. Computational methods play an important role in the development of molecular imaging, from image synthesis to data analysis and from clinical diagnosis to therapy individualization. This work will bring readers from academia and industry up to date on the most recent developments in this field.

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: - The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs - Applications of statistics on manifolds and shape spaces in medical image computing - Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. - A complete reference covering both the foundations and state-of-the-art methods - Edited and authored by leading researchers in the field - Contains theory, examples, applications, and algorithms - Gives an overview of current research challenges and future applications