A Combined Intensity and Gradient-Based Similarity Criterion for Interindividual SPECT Brain Scan Registration
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A Combined Intensity and Gradient-Based Similarity Criterion for Interindividual SPECT Brain Scan Registration Roger Lundqvist Centre for Image Analysis, Uppsala University, L¨agerhyddv¨agen 3, SE-751 05 Uppsala, Sweden Email: [email protected]
Ewert Bengtsson Centre for Image Analysis, Uppsala University, Uppsala, Sweden Email: [email protected]
Lennart Thurfjell Applied Medical Imaging AB, J¨arpv¨agen 1, SE-756 53, Uppsala, Sweden Email: [email protected] Received 27 November 2001 and in revised form 25 October 2002 An evaluation of a new similarity criterion for interindividual image registration is presented. The proposed criterion combines intensity and gradient information from the images to achieve a more robust and accurate registration. It builds on a combination of the normalised mutual information (NMI) cost function and a gradient-weighting function, calculated from gradient magnitude and relative gradient angle values from the images. An investigation was made to determine the best settings for the number of bins in the NMI joint histograms, subsampling, and smoothing of the images prior to the registration. The new method was compared with the NMI and correlation-coefficient (CC) criterions for interindividual SPECT image registration. Two different validation tests were performed, based on the displacement of voxels inside the brain relative to their estimated true positions after registration. The results show that the registration quality was improved when compared with the NMI and CC measures. The actual improvements, in one of the tests, were in the order of 30–40% for the mean voxel displacement error measured within 20 different SPECT images. A conclusion from the studies is that the new similarity measure significantly improves the registration quality, compared with the NMI and CC similarity measures. Keywords and phrases: image registration, mutual information, gradient information.
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INTRODUCTION
Registration of neuroimaging data is of great importance for both functional and anatomical studies of the brain. Intraindividual registration is used to bring data from different examinations of the same individual into a compatible form. Interindividual registration, which is a much more complex task, is used to map the anatomy of one individual onto the anatomy of another and allows for direct comparisons of data from multiple individuals. A wide range of different methods for registration of medical images has been proposed in the literature [1]. The differences between them often concern the features in the images used for measuring the similarity between the images. The different methods can be divided into groups, such
as landmark-, surface-, or voxel-based methods. Most methods have their advantages compared to others and the best approach depends to some extent on the characteristics of the images to be registered. In recent years, voxel-based techniques have gained in importance and they are commonly used for both registration within and between imaging modalities. In the case of intramodality re
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