3D image registration of the human brain

Author(s)

Fernanda Oliveira Favretto

• Document type: Master's Dissertation
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Instituto de Computação
• Defense date: 2009-03-20

Examining board members

Alexandre Xavier Falcão; Bruno Motta de Carvalho; Hélio Pedrini

• Advisor: Alexandre Xavier Falcão

Abstract

Image Registration is the process that aligns two or more images in a common reference system of spacial coordinates [31]. It is an important problem with several applications in Medical Imaging, enabling, for instance, the analysis of changes in anatomy along time by the registration of images from the same modality, and the study of combined anatomic and physiologic data by the registration of images from different modalities. The objective of this work is the development of a registration method for 3D images of the human brain, and the motivation is a comparative study of pre and post-surgical images from epilepsy patients. A recent study [80] has observed that some pacients, who did not cease the seizures after surgery, presented variations in their brain tissues. The registration of pre and post-surgical images enables the analysis of these tissue's variations. We developed a rigid registration method that aligns 3D images in a fast, automatic and accurate way. The method is based on the matching between watershed lines extrated from a source image and a morphological gradient image from the target image. The search for the parameters of rotation and translation that compose the mapping function is done by a techinique proposed in this work, named Multi-Scale Gradient Descent - a variant of the tradicional method Gradient Descent - which enables gradient's vectors with scaled magnitudes, avoiding undesirable local minima and fastly converging to the desired optimum. The method was evaluated on 3D T1-weighted Magnetic Ressonance Images of the human brain. The experiments used 2 data bases: a control data base, composed by 200 pairs of images, in which the method took approximately 45s and acceptable results; and a data base of patients, composed by pre- and post-surgical images, demonstrating the effectiveness of the method for real data. We have also developed visualization techiniques for the registred images: the checkerboard image, that alternates the target and registered source in a checkerboard pattern, allowing the user to inspect the correctness, coherence and continuity of the registration; and the colorized image, that combines the target and registered source images in a single colorized volume, such that the alterations of the tissues can be identified by the red and green colors. Therefore, the main contributions of this work are: a 3D registration methodology, that involves an effective combination of feature selection, similarity measure and search strategy; a search strategy, MSGD, that seems to be promissing for other optimization problems; and a visualization techinique that uses a colorized volume to combine the registered images. (AU)