Mutual Information Based Rigid Medical Imageregistration Using Normalized Tsallis Entropyand Type II Fuzzy Index

Abstract

We investigated the registration of medical images based on the Normalized Tsallis entropy using mutual information measure. A prerequisite for successful registration is unambiguous maximum of mutual information. We discuss the framework of our algorithm with Normalized Tsallis entropy as the core. Further we propose a type II fuzzy based technique to select the optimal Tsallis parameter q which provides the best alignment. Consequently, specific instances of image registration involving rigid affine transformations were studied. Registration was applied to clinically acquired mammogram. The accuracy was compared with several other techniques. Our algorithm shows promising results. Further, the Need for Pre-registration in mammogram is discussed in detail. Our algorithm can be effective enough to replace Shannon and Tsallis entropy based affine registration.

A CNN Based Hybrid Approach Towards Automatic Image Registration

“…Efficiency of these methods are situation UDK 528.711 and image-specific due to involvement of various parameters like spatial and spectral resolution, sensor characteristics etc. (Viola, Wells 1997;Mohanalin et al 2009). …”

“…Soft computing techniques such as neural networks, genetic algorithms, and fuzzy logic followed by probabilistic concepts such as random field variations, have been extensively applied in this context (Liu, Wu 2012;Gouveia et al 2012;jack, Roux 1995;Chow et al 2004;jankó et al 2006). Literature has also revealed many mutual information as well as intensity-based approaches (Klein et al 2007;Viola, Wells 1997;Cvejic et al 2006;Mohanalin et al 2009). N-dimensional classifiers as well as random field concepts and different transformation techniques (SIFT, Wavelet etc.)…”

A CNN based Hybrid approach towards automatic image registration

“…Soft computing techniques, such as neural networks, genetic algorithms, and fuzzy logic followed by probabilistic concepts such as random fi eld variations, have been extensively applied in this context (Liu and Wu, 2012;Gouveia et al, 2012;Jacq and Roux, 1995;Chow et al, 2004). Literature has also revealed many mutual information as well as intensity-based approaches (Klein, 2007;Viola, 1997;Pluim et al, 2000;Knops et al, 2004;Cvejic et al, 2006;Mohanalin and Kalra, 2009). Different entropy variations such as Tsallis, Renyis, and Shanon have been exploited for optimizing features matching.…”