Name
Title | ||
|---|---|---|
Mutual information-based binarisation of multiple images of an object: an application in medical imaging |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1049/iet-cvi.2012.0135 | IET Computer Vision |
Keywords | Field | DocType |
image enhancement,image segmentation,medical image processing,clinical practice,global thresholds,image histograms,image thresholding,information theory,medical imaging,multiple images,mutual information-based binarisation,optimal set,synthetic images,histogram,mutual information,entropy,multispectral images,neurophysiology | Information theory,Computer vision,Histogram,Pattern recognition,Medical imaging,Multispectral image,Image segmentation,Artificial intelligence,Mutual information,Thresholding,Mathematics,Calibration | Journal |
Volume | Issue | ISSN |
7 | 3 | 1751-9632 |
Citations | PageRank | References |
2 | 0.45 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gal, Y. | 1 | 3 | 1.14 |
| Andrew Mehnert | 2 | 140 | 14.07 |
| Rose, S. | 3 | 2 | 0.45 |
| Stuart Crozier | 4 | 130 | 14.02 |