Mutual information-based binarisation of multiple images of an object: an application in medical imaging

Gal, Yaniv, Mehnert, Andrew, Rose, Stephen and Crozier, Stuart (2013) Mutual information-based binarisation of multiple images of an object: an application in medical imaging. IET Computer Vision, 7 3: 163-169. doi:10.1049/iet-cvi.2012.0135

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Author Gal, Yaniv
Mehnert, Andrew
Rose, Stephen
Crozier, Stuart
Title Mutual information-based binarisation of multiple images of an object: an application in medical imaging
Journal name IET Computer Vision   Check publisher's open access policy
ISSN 1751-9632
1751-9640
Publication date 2013-06-01
Year available 2013
Sub-type Article (original research)
DOI 10.1049/iet-cvi.2012.0135
Open Access Status File (Author Post-print)
Volume 7
Issue 3
Start page 163
End page 169
Total pages 7
Place of publication Stevenage, Herts, United Kingdom
Publisher The Institution of Engineering and Technology
Language eng
Abstract A new method for image thresholding of two or more images that are acquired in different modalities or acquisition protocols is proposed. The method is based on measures from information theory and has no underlying free parameters nor does it require training or calibration. The method is based on finding an optimal set of global thresholds, one for each image, by maximising the mutual information above the thresholds while minimising the mutual information below the thresholds. Although some assumptions on the nature of images are made, no assumptions are made by the method on the intensity distributions or on the shape of the image histograms. The effectiveness of the method is demonstrated both on synthetic images and medical images from clinical practice. It is then compared against three other thresholding methods
Formatted abstract
A new method for image thresholding of two or more images that are acquired in different modalities or acquisition protocols is proposed. The method is based on measures from information theory and has no underlying free parameters nor does it require training or calibration. The method is based on finding an optimal set of global thresholds, one for each image, by maximising the mutual information above the thresholds while minimising the mutual information below the thresholds. Although some assumptions on the nature of images are made, no assumptions are made by the method on the intensity distributions or on the shape of the image histograms. The effectiveness of the method is demonstrated both on synthetic images and medical images from clinical practice. It is then compared against three other thresholding methods
Keyword Multispectral images
Entropy
Registration
Histogram
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 631567
Institutional Status UQ

 
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