MI image registration using prior knowledge

05/24/2007
by   W. Jacquet, et al.
0

Subtraction of aligned images is a means to assess changes in a wide variety of clinical applications. In this paper we explore the information theoretical origin of Mutual Information (MI), which is based on Shannon's entropy.However, the interpretation of standard MI registration as a communication channel suggests that MI is too restrictive a criterion. In this paper the concept of Mutual Information (MI) is extended to (Normalized) Focussed Mutual Information (FMI) to incorporate prior knowledge to overcome some shortcomings of MI. We use this to develop new methodologies to successfully address specific registration problems, the follow-up of dental restorations, cephalometry, and the monitoring of implants.

READ FULL TEXT

page 10

page 11

page 13

page 14

research
09/24/2019

Analysis of Generalized Entropies in Mutual Information Medical Image Registration

Mutual information (MI) is the standard method used in image registratio...
research
10/02/2012

Distributed High Dimensional Information Theoretical Image Registration via Random Projections

Information theoretical measures, such as entropy, mutual information, a...
research
12/01/2016

A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information

CLARITY is a method for converting biological tissues into translucent a...
research
11/06/2016

Validation of Tsallis Entropy In Inter-Modality Neuroimage Registration

Medical image registration plays an important role in determining topogr...
research
01/10/2017

Toward a Calculus of Redundancy: The feedback arrow of expectations in knowledge-based systems

Whereas the generation of Shannon-type information is coupled to the sec...
research
05/14/2020

The Information Mutual Information Ratio for Counting Image Features and Their Matches

Feature extraction and description is an important topic of computer vis...
research
05/24/2022

First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization

How can we train an assistive human-machine interface (e.g., an electrom...

Please sign up or login with your details

Forgot password? Click here to reset