Special Issue "Machine Learning for Medical Imaging"

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

Deadline for manuscript submissions: closed (31 December 2009)

Special Issue Editor

Guest Editor
Dr. Kenji Suzuki
Associate Professor of Electrical and Computer Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL 60616, USA
Website: http://suzukilab.uchicago.edu/
E-Mail: kenjisuzuki00@gmail.com
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

Special Issue Information

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.

Keywords

  • 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

Published Papers (11 papers)

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Displaying article 1-11
p. 136-160
by
Algorithms 2013, 6(1), 136-160; doi:10.3390/a6010136
Received: 28 November 2012; in revised form: 18 February 2013 / Accepted: 19 February 2013 / Published: 12 March 2013
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 636-653
by
Algorithms 2012, 5(4), 636-653; doi:10.3390/a5040636
Received: 31 July 2012; in revised form: 5 November 2012 / Accepted: 3 December 2012 / Published: 13 December 2012
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 125-144
by ,  and
Algorithms 2010, 3(2), 125-144; doi:10.3390/a3020125
Received: 1 February 2010; in revised form: 16 February 2010 / Accepted: 22 March 2010 / Published: 31 March 2010
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 44-62
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Algorithms 2010, 3(1), 44-62; doi:10.3390/a3010044
Received: 28 October 2009; in revised form: 14 January 2010 / Accepted: 14 January 2010 / Published: 19 January 2010
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
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p. 21-43
by , , ,  and
Algorithms 2010, 3(1), 21-43; doi:10.3390/a3010021
Received: 9 November 2009; in revised form: 14 December 2009 / Accepted: 23 December 2009 / Published: 5 January 2010
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
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p. 1-20
by , ,  and
Algorithms 2010, 3(1), 1-20; doi:10.3390/a3010001
Received: 28 September 2009; Accepted: 6 October 2009 / Published: 4 January 2010
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 1503-1525
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Algorithms 2009, 2(4), 1503-1525; doi:10.3390/a2041503
Received: 12 October 2009; in revised form: 20 November 2009 / Accepted: 25 November 2009 / Published: 1 December 2009
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 1473-1502
by , ,  and
Algorithms 2009, 2(4), 1473-1502; doi:10.3390/a2041473
Received: 27 September 2009; in revised form: 27 October 2009 / Accepted: 11 November 2009 / Published: 30 November 2009
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 1350-1367
by , , , , ,  and
Algorithms 2009, 2(4), 1350-1367; doi:10.3390/a2041350
Received: 1 August 2009; in revised form: 22 September 2009 / Accepted: 3 November 2009 / Published: 16 November 2009
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 925-952
by , ,  and
Algorithms 2009, 2(3), 925-952; doi:10.3390/a2030925
Received: 27 March 2009; in revised form: 6 June 2009 / Accepted: 2 July 2009 / Published: 10 July 2009
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(This article belongs to the Special Issue Machine Learning for Medical Imaging)
p. 828-849
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Algorithms 2009, 2(2), 828-849; doi:10.3390/a2020828
Received: 29 April 2009; in revised form: 28 May 2009 / Accepted: 28 May 2009 / Published: 4 June 2009
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Last update: 20 February 2014

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