Improving accuracy and efficiency of registration by mutual information using Sturges’ Histogram Rule

Legg, Philip; Rosin, Paul; Marshall, David; Morgan, James

Abstract

Mutual Information is a common technique for image registration in the medical domain, in particular where images of different modalities are to be registered. In this paper, we wish to demonstrate the benefits of applying a common method known in statistics as Sturges’ Rule for selecting histogram bin size when computing Entropy as a part of the existing Mutual Information algorithm. Although Sturges’ Rule is well known in the field of statistics it has received little attention in the Computer Vision community. By augmenting Mutual Information with Sturges’ Rule, we show that this offers an improvement to both the runtime of the algorithm and also the accuracy of the registration. Our results are demonstrated on images of the eye, in particular, Fundus images and SLO (Scanning Laser Ophthalmoscopy) images.

• Presentation Conference Type: Conference Paper (unpublished)
• Acceptance Date: Jun 1, 2007
• Publication Date: Jun 1, 2007
• Publicly Available Date: Jun 8, 2019
• Peer Reviewed: Peer Reviewed
• Pages: 26-30
• Keywords: improving accuracy, efficiency, registration, mutual information, Sturges' Histogram Rule
• Public URL: https://uwe-repository.worktribe.com/output/1027290
• Additional Information: Title of Conference or Conference Proceedings : Medical Image Understanding and Analysis