Machine Learning for Medical Imaging 2012
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (31 August 2012) | Viewed by 20657
Special Issue Editor
Interests: deep learning; machine learning; computer-aided diagnosis; medical imaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Medical imaging is an indispensable tool of patients’ healthcare in modern medicine. Machine learning plays an essential role in the medical imaging field, including medical image analysis, computer-aided diagnosis, organ/lesion segmentation, image fusion, image-guided therapy, image annotation and image retrieval, because objects such as lesions and anatomy in medical images cannot be represented accurately by simple equations; thus, tasks in medical imaging essentially require “learning from examples.” Because of its essential needs, machine learning for medical imaging is one of the most promising, rapidly growing fields. As medical imaging has been advancing with the introduction of new imaging modalities and methodologies such as cone-beam/multi-slice CT, positron-emission tomography (PET)-CT, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), electrical impedance tomography and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Areas of interest in this special issue are all aspects of machine-learning research in the medical imaging field, including, but not limited to:
- 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, decision tree learning, Bayesian networks, sparse dictionary learning, genetic algorithms) 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, registration) 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, cell/marker tracking/detection)
- image fusion of multiple modalities, multiple phases, multiple sequences and multiple angles
- image retrieval (e.g., lesion similarity, context-based) and data mining
- gene data analysis (e.g., genotype/phenotype classification/identification)
- molecular/pathologic image analysis (e.g., PET, digital pathology)
- dynamic, functional, physiologic, and anatomic imaging.
Dr. Kenji Suzuki
Guest Editor
Keywords
- computer-aided diagnosis
- support vector machines
- artificial neural networks
- manifold
- decision tree learning
- Bayesian networks
- sparse dictionary learning
- genetic algorithms
- classificatio
- pattern recognition
- image reconstruction
- registration
- medical image analysis
- statistical pattern recognition
- segmentation
- image fusion
- image retrieval
- biological imaging
- multiple modalities
- gene
- X-ray
- CT
- MRI
- PET
- Ultrasound
- digital pathology
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