Post by ncwebcenter on Jul 28, 2008 13:11:51 GMT -5
CALL FOR CHAPTERS Proposal Submission Deadline: July 30, 2008
BIOMEDICAL IMAGE ANALYSIS AND MACHINE LEARNING TECHNOLOGIES: APPLICATIONS AND TECHNIQUES
A book edited by Fabio Gonzalez and Eduardo Romero
National University of Colombia
www.bioingenium.unal.edu.co/book/webbook.htm
INTRODUCTION
Medical Images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Different techniques have been applied to these automated tasks, which cover very different domains including signal processing, statistics, machine learning (ML) as well as variable combinations of these tasks.
ML techniques attempt to find patterns in data that allow for the
building of descriptive or predictive models. One of the main
advantages of ML methods is that they are able to automatically find elusive, complex relationships between data that are otherwise found by an extensive knowledge of the problem. In the biomedical image context, ML techniques have been traditionally applied to pattern recognition tasks associated with image understanding and interpretation. Nevertheless, ML
methods have become ubiquitous in tasks such as image navigation, image search, image retrieval, and image transformation:, a set of applications which may be gathered together into the image analysis area.
THE OVERALL OBJECTIVE OF THE BOOK
This book aims to provide a general panorama of the current boundary between the biomedical complexity coming from the medical image context and the multiple ML techniques which have been used for solving many of these problems. In recent years, there has been a rapid development in the ML research field, both in the development of new algorithms and to their application to problems in different fields. The biomedical
image field has not been the exception. This book will gather representative works that exhibit how ML is applied to the solution of very different problems related to different biomedical image areas. The main expected impact of the book is to highlight the great research potential of this interdisciplinary area, and to provide insights on new potential applications of ML techniques to the solution of important problems in biomedical image applications.
THE TARGET AUDIENCE
This book is addressed to students, scientists and practitioners in the field of biomedical images, both in academy and industry. Also it will provide useful material for ML researchers looking for interesting application problems.
Recommended topics include, but are not limited to, the following
machine learning applications:
- Model-based biomedical image analysis
- Biomedical object and pattern recognition
- Quantification in biomedical objects
- Biomedical image understanding and interpretation
- High-level biomedical image representation
- Computational perception models
- Content-based biomedical image retrieval
- Biomedical image classification and categorization
- Mining from large collections of biomedical images
- Biomedical image compression
- Biomedical image navigation
- Computer-assisted diagnosis in biomedical images
- Biomedical image reconstruction
- Biomedical Image models for visualization
- Biomedical image registration with prior knowledge
- Biomedical Image enhancement and restoration
- Content-specific biomedical image enhancement
- User preference-based biomedical image enhancement
- Biomedical image transformations and feature vectors for analysis
- Biomedical image region-based segmentation methods
- Performance evaluation of ML methods in biomedical applications
SUBMISSION PROCEDURE
Researchers and practitioners are invited to submit on or before July 30, 2008, a 2-3 page chapter proposal clearly explaining the mission and concerns of the proposed chapter, together with a tentative title and chapter organization. Proposals will be accepted based on pertinence criteria and topic balancing needs. Authors of accepted proposals will be notified by August 15, 2008 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted no later than November 30, 2008. All submitted chapters will be reviewed
on a double-blind review basis. This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference) and "Medical Information Science Reference" imprints. For additional information regarding the publisher, please visit www.igi-global.com.
Inquiries and submissions can be forwarded electronically (Word
document) to:
f*gonzalezo@unal.edu.co and edromero@unal.edu.co
BIOMEDICAL IMAGE ANALYSIS AND MACHINE LEARNING TECHNOLOGIES: APPLICATIONS AND TECHNIQUES
A book edited by Fabio Gonzalez and Eduardo Romero
National University of Colombia
www.bioingenium.unal.edu.co/book/webbook.htm
INTRODUCTION
Medical Images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Different techniques have been applied to these automated tasks, which cover very different domains including signal processing, statistics, machine learning (ML) as well as variable combinations of these tasks.
ML techniques attempt to find patterns in data that allow for the
building of descriptive or predictive models. One of the main
advantages of ML methods is that they are able to automatically find elusive, complex relationships between data that are otherwise found by an extensive knowledge of the problem. In the biomedical image context, ML techniques have been traditionally applied to pattern recognition tasks associated with image understanding and interpretation. Nevertheless, ML
methods have become ubiquitous in tasks such as image navigation, image search, image retrieval, and image transformation:, a set of applications which may be gathered together into the image analysis area.
THE OVERALL OBJECTIVE OF THE BOOK
This book aims to provide a general panorama of the current boundary between the biomedical complexity coming from the medical image context and the multiple ML techniques which have been used for solving many of these problems. In recent years, there has been a rapid development in the ML research field, both in the development of new algorithms and to their application to problems in different fields. The biomedical
image field has not been the exception. This book will gather representative works that exhibit how ML is applied to the solution of very different problems related to different biomedical image areas. The main expected impact of the book is to highlight the great research potential of this interdisciplinary area, and to provide insights on new potential applications of ML techniques to the solution of important problems in biomedical image applications.
THE TARGET AUDIENCE
This book is addressed to students, scientists and practitioners in the field of biomedical images, both in academy and industry. Also it will provide useful material for ML researchers looking for interesting application problems.
Recommended topics include, but are not limited to, the following
machine learning applications:
- Model-based biomedical image analysis
- Biomedical object and pattern recognition
- Quantification in biomedical objects
- Biomedical image understanding and interpretation
- High-level biomedical image representation
- Computational perception models
- Content-based biomedical image retrieval
- Biomedical image classification and categorization
- Mining from large collections of biomedical images
- Biomedical image compression
- Biomedical image navigation
- Computer-assisted diagnosis in biomedical images
- Biomedical image reconstruction
- Biomedical Image models for visualization
- Biomedical image registration with prior knowledge
- Biomedical Image enhancement and restoration
- Content-specific biomedical image enhancement
- User preference-based biomedical image enhancement
- Biomedical image transformations and feature vectors for analysis
- Biomedical image region-based segmentation methods
- Performance evaluation of ML methods in biomedical applications
SUBMISSION PROCEDURE
Researchers and practitioners are invited to submit on or before July 30, 2008, a 2-3 page chapter proposal clearly explaining the mission and concerns of the proposed chapter, together with a tentative title and chapter organization. Proposals will be accepted based on pertinence criteria and topic balancing needs. Authors of accepted proposals will be notified by August 15, 2008 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted no later than November 30, 2008. All submitted chapters will be reviewed
on a double-blind review basis. This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference) and "Medical Information Science Reference" imprints. For additional information regarding the publisher, please visit www.igi-global.com.
Inquiries and submissions can be forwarded electronically (Word
document) to:
f*gonzalezo@unal.edu.co and edromero@unal.edu.co