Integrative Approaches in Computational Biomedical Imaging
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: