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
Department of Radiology, Graduate Program in Medical Physics, and Cancer Research Center, Division of the Biological Sciences, The University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL 60637, USA
Interests: interdisciplinary research in medicine and computer science; computer-aided diagnosis; machine learning; image processing; pattern recognition
- 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