Special Issue "Machine Learning for Medical Imaging 2012"

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A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 August 2012)

Special Issue Editor

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: suzuki@uchicago.edu
Interests: interdisciplinary research in medicine and computer science; computer-aided diagnosis; machine learning; image processing; pattern recognition

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

Published Papers (2 papers)

by , ,  and
Algorithms 2013, 6(3), 512-531; doi:10.3390/a6030512
Received: 12 November 2012; in revised form: 14 August 2013 / Accepted: 15 August 2013 / Published: 21 August 2013
Show/Hide Abstract | PDF Full-text (725 KB)

by
Algorithms 2012, 5(3), 318-329; doi:10.3390/a5030318
Received: 2 June 2012; in revised form: 10 July 2012 / Accepted: 20 July 2012 / Published: 10 August 2012
Show/Hide Abstract | PDF Full-text (582 KB) | HTML Full-text | XML Full-text

Last update: 6 March 2014

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