Special Issue "Machine Learning for Medical Imaging"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 December 2009)
Dr. Kenji Suzuki
Associate Professor of Electrical and Computer Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL 60616, USA
Phone: +1 312 567 5232
Interests: machine learning in medical imaging, computation intelligence in medical imaging, computer-aided detection and diagnosis, medical image processing and analysis
- Computer-aided detection/diagnosis (e.g., for lung cancer, breast cancer, colon cancer, liver cancer, acute disease, chronic disease, osteoporosis)
- Machine learning (e.g., with support vector machines, statistical methods, manifold-space-based methods, artificial neural networks) applications to medical images with 2D, 3D and 4D data.
- Multi-modality fusion (e.g., PET/CT, projection X-ray/CT, X-ray/ultrasound)
- Medical image analysis (e.g., pattern recognition, classification, segmentation) of lesions, lesion stage, organs, anatomy, status of disease and medical data
- Image reconstruction (e.g., expectation maximization (EM) algorithm, statistical methods) for medical images (e.g., CT, PET, MRI, X-ray)
- Biological image analysis (e.g., biological response monitoring, biomarker tracking/detection)
- Image fusion of multiple modalities, multiple phases and multiple angles
- Image retrieval (e.g., lesion similarity, context-based)
- Gene data analysis (e.g., genotype/phenotype classification/identification)
- Molecular/pathologic image analysis
- Dynamic, functional, physiologic, and anatomic imaging.
- computer-aided diagnosis
- artificial neural networks
- support vector machines
- manifold, classification
- pattern recognition
- image reconstruction
- medical image analysis
- statistical pattern recognition
- image fusion
- image retrieval
- biological imaging
- multiple modalities