International Journal of Biomedical Imaging

Machine Learning in Medical Imaging


Publishing date
01 Sep 2011
Status
Published
Submission deadline
01 Mar 2011

Lead Editor

1Department of Radiology, University of Chicago, Chicago, IL, USA

2Philips Research North America, Briarcliff Manor, NY, USA

3IBM Almaden Research Center, San Jose, CA, USA

4Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA


Machine Learning in Medical Imaging

Description

Medical imaging is indispensable for patients' healthcare. Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, medical image analysis, organ/lesion segmentation, image registration, and image-guided therapy. Because of large variations and complexity, it is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require learning from examples for accurate representation of data and knowledge. Because of its essential needs, machine learning in 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/multislice CT, positron-emission tomography (PET)/CT, tomosynthesis, and diffusion-weighted MRI, new machine-learning algorithms/applications are demanded in the medical imaging field.

The main aim of this special issue is to help advance scientific research within the broad field of medical imaging and machine learning. This special issue is planned in conjunction with the International Workshop on Machine Learning in Medical Imaging (MLMI 2010), which is the first workshop on this topic, being held with International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010). This special issue focuses on major trends and challenges in this area, and it presents work aimed to identify new techniques and their use in medical imaging. We are looking for original, high-quality submissions on innovative research and development in all aspects of machine learning in medical imaging including, but not limited to:

  • Computer-aided detection/diagnosis (e.g., for lung cancer, prostate cancer, breast cancer, colon cancer, liver cancer, acute disease, chronic disease, osteoporosis)
  • Machine learning (e.g., with support vector machines, kernel methods, statistical methods, probabilistic modeling, manifold-space-based methods, artificial neural networks) applications to medical images with 2D, 3D, and 4D data
  • Medical image analysis (e.g., pattern recognition, classification, segmentation, registration) of anatomic structures and lesion
  • Multimodality fusion (e.g., MRI/PET, PET/CT, X-ray/ultrasound) for diagnosis, image analysis, and image-guided intervention
  • Image reconstruction (e.g., expectation maximization and statistical methods)
  • Image retrieval (e.g., context-based retrieval, lesion similarity)
  • Cellular image analysis (e.g., genotype, phenotype, classification, cell tracking)
  • Biological image analysis (e.g., biological response monitoring, biomarker detection and tracking)
  • Image fusion (e.g., multiple modalities, multiple phases, multiple angles)

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/ijbi/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2012
  • - Article ID 123727
  • - Editorial

Machine Learning in Medical Imaging

Kenji Suzuki | Pingkun Yan | ... | Dinggang Shen
  • Special Issue
  • - Volume 2012
  • - Article ID 792079
  • - Review Article

Pixel-Based Machine Learning in Medical Imaging

Kenji Suzuki
  • Special Issue
  • - Volume 2011
  • - Article ID 870252
  • - Research Article

A Bayesian Hyperparameter Inference for Radon-Transformed Image Reconstruction

Hayaru Shouno | Madomi Yamasaki | Masato Okada
  • Special Issue
  • - Volume 2011
  • - Article ID 846312
  • - Research Article

Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Paul Wighton | Tim K. Lee | ... | M. Stella Atkins
  • Special Issue
  • - Volume 2011
  • - Article ID 601672
  • - Research Article

A Statistical Texture Model of the Liver Based on Generalized N-Dimensional Principal Component Analysis (GND-PCA) and 3D Shape Normalization

Xu Qiao | Yen-Wei Chen
  • Special Issue
  • - Volume 2011
  • - Article ID 658930
  • - Research Article

Sparse Representation of Deformable 3D Organs with Spherical Harmonics and Structured Dictionary

Dan Wang | Ahmed H. Tewfik | ... | Yunhe Shen
  • Special Issue
  • - Volume 2011
  • - Article ID 136034
  • - Research Article

Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation

Shadi AlZubi | Naveed Islam | Maysam Abbod
  • Special Issue
  • - Volume 2011
  • - Article ID 241396
  • - Research Article

Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

Xian-Hua Han | Yen-Wei Chen
  • Special Issue
  • - Volume 2011
  • - Article ID 972648
  • - Research Article

Automatic Segmentation of Dermoscopic Images by Iterative Classification

Maciel Zortea | Stein Olav Skrøvseth | ... | Fred Godtliebsen
  • Special Issue
  • - Volume 2011
  • - Article ID 350838
  • - Research Article

Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

Vincent Michel | Evelyn Eger | ... | Bertrand Thirion
International Journal of Biomedical Imaging
 Journal metrics
See full report
Acceptance rate13%
Submission to final decision128 days
Acceptance to publication23 days
CiteScore10.200
Journal Citation Indicator1.310
Impact Factor7.6
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