Pixel-Based Machine Learning in Medical Imaging
Author
Source
International Journal of Biomedical Imaging
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-02-28
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require “learning from examples.” One of the most popular uses of ML is classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates.
Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of features calculated from segmented objects as input information; thus, feature calculation or segmentation is not required.
Because the PML can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers (i.e., feature-based MLs).
In this paper, PMLs are surveyed to make clear (a) classes of PMLs, (b) similarities and differences within (among) different PMLs and those between PMLs and feature-based MLs, (c) advantages and limitations of PMLs, and (d) their applications in medical imaging.
American Psychological Association (APA)
Suzuki, Kenji. 2012. Pixel-Based Machine Learning in Medical Imaging. International Journal of Biomedical Imaging،Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-498461
Modern Language Association (MLA)
Suzuki, Kenji. Pixel-Based Machine Learning in Medical Imaging. International Journal of Biomedical Imaging No. 2012 (2012), pp.1-18.
https://search.emarefa.net/detail/BIM-498461
American Medical Association (AMA)
Suzuki, Kenji. Pixel-Based Machine Learning in Medical Imaging. International Journal of Biomedical Imaging. 2012. Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-498461
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-498461