Computational and Mathematical Methods in Medicine

Integrative Approaches in Computational Biomedical Imaging


Publishing date
19 Oct 2012
Status
Published
Submission deadline
01 Jun 2012

Lead Editor

1State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China

2B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA

3Department of Mathematics, University of Florida, 458 Little Hall, Gainesville, FL 32611-8105, USA


Integrative Approaches in Computational Biomedical Imaging

Description

With the increasingly wider availability of biomedical imaging modalities, namely, magnetic resonance imaging (MRI), ultrasonic imaging, X-ray imaging, CT scan, PET, SPECT, and optical imaging, there have been many successful applications in clinical medical imaging and laboratory biological imaging. The ultimate goal of biomedical imaging is to understand the function of organisms and, more importantly, the mechanisms underlying disease. Given the wealth of data, computational biomedical imaging clearly matters: it determines how much information can be reliably extracted and, therefore, whether they can be used to do the things, like diagnosis. It requires advanced algorithms and computational tools.

Nowadays, integrative approaches play an important role in computational biomedical imaging. In order to obtain sensible outcomes from imaging data, there are several issues which need to be properly addressed, including the representation of the problem domain, the proper models addressing two features that are inherent to imaged biological systems: complexity and uncertainty, the modeling of the solution properties which must be used for extracting meaningful solutions, and the optimization methods that integrate the imaging data and the models. Prospective authors are invited to submit their research contributions related to the following themes of this special issue, that is, which modalities can be used to obtain the information necessary for an integrative model for a particular application? How can the information from these modalities be integrated with each other and with any prior? How can integrated models be validated? Potential topics include, but are not limited to:

  • Shape representation and analysis
  • Image registration and fusion
  • Functional and molecular imaging
  • Statistical and mathematical models and simulation
  • Image reconstruction
  • Computer-aided detection/diagnosis (e.g., for lung cancer, prostate cancer, breast cancer, colon cancer, liver cancer, acute disease, chronic disease, and osteoporosis)

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/cmmm/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2012
  • - Article ID 162892
  • - Editorial

Integrative Approaches in Computational Biomedical Imaging

Huafeng Liu | Pengcheng Shi | Yunmei Chen
  • Special Issue
  • - Volume 2012
  • - Article ID 198108
  • - Research Article

Numerical Study for Blood Flow in Pulmonary Arteries after Repair of Tetralogy of Fallot

Ming-Jyh Chern | Ming-Ting Wu | Sheau-Wei Her
  • Special Issue
  • - Volume 2012
  • - Article ID 481923
  • - Research Article

A Generalized Gamma Mixture Model for Ultrasonic Tissue Characterization

Gonzalo Vegas-Sanchez-Ferrero | Santiago Aja-Fernandez | ... | Marcos Martin-Fernandez
  • Special Issue
  • - Volume 2012
  • - Article ID 394374
  • - Research Article

Extended Finite Element Method with Simplified Spherical Harmonics Approximation for the Forward Model of Optical Molecular Imaging

Wei Li | Huangjian Yi | ... | Jimin Liang
  • Special Issue
  • - Volume 2012
  • - Article ID 306765
  • - Research Article

A Workflow for Patient-Individualized Virtual Angiogram Generation Based on CFD Simulation

Jürgen Endres | Markus Kowarschik | ... | Arnd Dörfler
  • Special Issue
  • - Volume 2012
  • - Article ID 646045
  • - Research Article

Linear Program Relaxation of Sparse Nonnegative Recovery in Compressive Sensing Microarrays

Linxia Qin | Naihua Xiu | ... | Yu Li
  • Special Issue
  • - Volume 2012
  • - Article ID 876545
  • - Research Article

Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer

Chao Li | Shuheng Zhang | ... | Su Zhang
  • Special Issue
  • - Volume 2012
  • - Article ID 475745
  • - Research Article

A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data

Wei He | Yiyuan Cheng | ... | Feng Liu
  • Special Issue
  • - Volume 2012
  • - Article ID 128431
  • - Research Article

Quantitative Measurements in 3-Dimensional Datasets of Mouse Lymph Nodes Resolve Organ-Wide Functional Dependencies

Jürgen Mayer | Jim Swoger | ... | James Sharpe
  • Special Issue
  • - Volume 2012
  • - Article ID 961967
  • - Research Article

Reliable and Efficient Approach of BOLD Signal with Dual Kalman Filtering

Cong Liu | Zhenghui Hu
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate28%
Submission to final decision86 days
Acceptance to publication45 days
CiteScore1.840
Impact Factor1.563
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