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Author: Qiang Yuan
Publisher: National Library of Canada = Bibliothèque nationale du Canada
ISBN: 9780612210561
Category : Coding theory
Languages : en
Pages : 65
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Book Description
In this thesis, we study adaptive, statistical, context based error modelling techniques for predictive coding of medical images. The proposed modelling techniques are developed from the general purpose lossless image coding algorithm - CALIC, one of the state of art lossless image coders, and are aimed to reduce the model cost of a universal coder by utilizing a priori knowledge of input images. This context-based, adaptive modelling of prediction errors is optimized for the MR head image class, and the model cost can be disregarded since the model can be precomputed and saved for a particular type of images. The proposed approach improves the average compression code length by 0.32bpp over the universal CALIC through (1) Optimizing prediction function; (2) Type-customized context modelling of prediction errors; (3) Reducing the cost of learning source statistics from scratch by modelling initial conditional probabilities of prediction errors. We use linear regression to calculate the optimal least square coefficients of prediction function based on a set of training images. This lowers the average entropy by more than 0.1bpp. By modelling context optimization and using precomputed error model, error distributions from a set of training images, we obtain another average gain of 0.07bpp. We achieve 0.08bpp improvement by modelling initial conditional probabilities of prediction errors.
![Adaptive, Statistical, Context Modelling for Predictive Coding of Medical Images [microform] PDF](https://books.google.com/books/content/images/frontcover/I2r-wQEACAAJ?fife=w80-h100)
Author: Qiang Yuan
Publisher: National Library of Canada = Bibliothèque nationale du Canada
ISBN: 9780612210561
Category : Coding theory
Languages : en
Pages : 65
Get Book Here
Book Description
In this thesis, we study adaptive, statistical, context based error modelling techniques for predictive coding of medical images. The proposed modelling techniques are developed from the general purpose lossless image coding algorithm - CALIC, one of the state of art lossless image coders, and are aimed to reduce the model cost of a universal coder by utilizing a priori knowledge of input images. This context-based, adaptive modelling of prediction errors is optimized for the MR head image class, and the model cost can be disregarded since the model can be precomputed and saved for a particular type of images. The proposed approach improves the average compression code length by 0.32bpp over the universal CALIC through (1) Optimizing prediction function; (2) Type-customized context modelling of prediction errors; (3) Reducing the cost of learning source statistics from scratch by modelling initial conditional probabilities of prediction errors. We use linear regression to calculate the optimal least square coefficients of prediction function based on a set of training images. This lowers the average entropy by more than 0.1bpp. By modelling context optimization and using precomputed error model, error distributions from a set of training images, we obtain another average gain of 0.07bpp. We achieve 0.08bpp improvement by modelling initial conditional probabilities of prediction errors.
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Languages : en
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Book Description
Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 595
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Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Author: Shuo Li
Publisher: Springer Science & Business Media
ISBN: 3319038133
Category : Technology & Engineering
Languages : en
Pages : 441
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Book Description
This book contains thirteen contributions from invited experts of international recognition addressing important issues in shape analysis in medical image analysis, including techniques for image segmentation, registration, modelling and classification and applications in biology, as well as in cardiac, brain, spine, chest, lung and clinical practice. This volume treats topics such as for example, anatomic and functional shape representation and matching; shape-based medical image segmentation; shape registration; statistical shape analysis; shape deformation; shape-based abnormity detection; shape tracking and longitudinal shape analysis; machine learning for shape modeling and analysis; shape-based computer-aided-diagnosis; shape-based medical navigation; benchmark and validation of shape representation, analysis and modeling algorithms. This work will be of interest to researchers, students and manufacturers in the fields of artificial intelligence, bioengineering, biomechanics, computational mechanics, computational vision, computer sciences, human motion, mathematics, medical imaging, medicine, pattern recognition and physics.
Author: Gerhard Tutz
Publisher: Springer
ISBN: 3319281585
Category : Mathematics
Languages : en
Pages : 252
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Book Description
This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.
Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617
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Book Description
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author: Ke-Lin Du
Publisher: Springer Science & Business Media
ISBN: 1447155718
Category : Technology & Engineering
Languages : en
Pages : 834
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Book Description
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Author: Robert B. Gramacy
Publisher: CRC Press
ISBN: 1000766209
Category : Mathematics
Languages : en
Pages : 560
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Book Description
Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.
Author: Alan J. Izenman
Publisher: Springer Science & Business Media
ISBN: 0387781897
Category : Mathematics
Languages : en
Pages : 757
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Book Description
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Author: Joyce L. Epstein
Publisher: Corwin Press
ISBN: 1483320014
Category : Education
Languages : en
Pages : 508
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Book Description
Strengthen programs of family and community engagement to promote equity and increase student success! When schools, families, and communities collaborate and share responsibility for students′ education, more students succeed in school. Based on 30 years of research and fieldwork, the fourth edition of the bestseller School, Family, and Community Partnerships: Your Handbook for Action, presents tools and guidelines to help develop more effective and more equitable programs of family and community engagement. Written by a team of well-known experts, it provides a theory and framework of six types of involvement for action; up-to-date research on school, family, and community collaboration; and new materials for professional development and on-going technical assistance. Readers also will find: Examples of best practices on the six types of involvement from preschools, and elementary, middle, and high schools Checklists, templates, and evaluations to plan goal-linked partnership programs and assess progress CD-ROM with slides and notes for two presentations: A new awareness session to orient colleagues on the major components of a research-based partnership program, and a full One-Day Team Training Workshop to prepare school teams to develop their partnership programs. As a foundational text, this handbook demonstrates a proven approach to implement and sustain inclusive, goal-linked programs of partnership. It shows how a good partnership program is an essential component of good school organization and school improvement for student success. This book will help every district and all schools strengthen and continually improve their programs of family and community engagement.
