Special Issue "Machine Learning for Medical Imaging"

Guest Editor
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
Website: http://suzukilab.uchicago.edu/
E-Mail:
Interests: interdisciplinary research in medicine and computer science; computer-aided diagnosis; machine learning; image processing; pattern recognition

Summary: Medical imaging is an indispensable tool of patients’ healthcare in modern medicine. Machine leaning 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 modeled accurately by simple equations; thus, tasks in medical imaging require learning from examples. Because of its essential needs, machine learning for medical imaging is one of the most promising, 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 for medical imaging/images 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) 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.

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs).

• computer-aided diagnosis
• artificial neural networks
• support vector machines
• manifold, classification
• pattern recognition
• image reconstruction
• medical image analysis
• statistical pattern recognition
• segmentation
• image fusion
• image retrieval
• biological imaging
• multiple modalities
• gene
• X-ray
• CT
• MRI
• PET
• ultrasound