EEE ( Electronics Thesis and Seminar Project ) On Medical Image registration

Mutual Information based Rigid Medical Image registration using Normalized Tsallis entropy and Type II fuzzy index.

Department Of Electronics

Project Intro: Image registration is the determination of geometrical transformation that aligns points of one view of an object with the corresponding points in another view of that object. i.e. output is a geometrical transformation which is simply a mathematical mapping from one points to points in second. The Paper is organized as follows. Section 2 Deals with the literature review of recent work in rigid image registration. Section 3 explains basics of our algorithm, and details of Normalized entropy. A new method to find optimal Tsallis parameter using Type II fuzzy set is proposed. The proposed algorithm is checked for mammograms with known simulated deformation and results with several validation techniques were shown in section 5. Section 6 contains the conclusion.

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.

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Index Keywords: Tsallis entropy, Shannon entropy,Normalized Tsallis entropy, Joint intensity distribution,image registration, Powell optimization, Mammogram.

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