Real-Time Noise Classification of Medical Image via Online Machine Learning Algorithm
Journal Title: Advances in Image and Video Processing - Year 2017, Vol 5, Issue 6
Abstract
Medical image is generally deteriorated by noise due to signal acquisition, signal processing, and other reasons. Noise classification of medical image is able to enhance post-processing tasks like medical image segmentation, registration, and analysis. Due to real-time requirements of medical image analysis for clinical applications, noise classification of medical image is desired to be fast for meeting real-time requirements. On the other hand, online learning algorithms have been studied for processing online data in real-time mode, which can produce rapid learning model based on adjustments of new incoming data. In this paper, we investigate perceptron algorithm - a classical online learning method for noise classification in parallel magnetic resonance imaging (pMRI). Noise generated in pMRI is quickly classified and online classification model is updated in real-time simultaneously. Experimental results demonstrate that noise and brain tissues existing MR images is able to be classified dynamically with the perceptron algorithm.
Authors and Affiliations
Yuchou Chang
Keywords
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- EP ID EP266435
- DOI 10.14738/aivp.56.4084
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