Open AccessMutual information‐based binarisation of multiple images of an object: an application in medical imaging
Author(s) -
Gal Yaniv,
Mehnert Andrew,
Rose Stephen,
Crozier Stuart
Publication year - 2013
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0135
Subject(s) - mutual information , artificial intelligence , thresholding , histogram , computer science , computer vision , pattern recognition (psychology) , image (mathematics) , medical imaging , set (abstract data type) , balanced histogram thresholding , object (grammar) , histogram matching , programming language
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