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

Yaniv Gal ; Andrew Mehnert (Institutionen för signaler och system, Digitala bildsystem och bildanalys) ; Stephen Rose ; Stuart Crozier
IET Computer Vision (1751-9632). Vol. 7 (2013), 3, p. 163-169.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: neurophysiology; medical image processing; positron emission tomography; biomedical MRI; image segmentation; brain



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Denna post skapades 2013-06-17. Senast ändrad 2016-08-22.
CPL Pubid: 178705

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Digitala bildsystem och bildanalys (1900-2013)

Ämnesområden

Livsvetenskaper
Medicinsk bildbehandling

Chalmers infrastruktur