Analysis of Biomedical Images Based on Automated Methods of Image Registration

Abstract

Abstract. This paper gives a review of the methods that we have developed over the last few years to register 2D/3D images and image sequences. Initially, we will introduce a feature-based method, which will be followed up by a summary of the intensity-based methods. Then we will present some applications of our methods using biomedical images with more automated image analysis tasks. The main applications are related to plantar pressure images and SPECT brain images, including both static images and image sequences. IntroductionImage registration, i.e., the process of transforming an image so that features contained in it are properly adjusted to the homologous features contained in a second image, is a paramount research topic. Computational methods of image registration are frequently applied to static bi-dimensional (2D) and tridimensional (3D) images, as well as to 2D and 3D image sequences. For example, in Biomedical Imaging, these methods have been assuming an essential role in supporting enhanced image-based analysis, by addressing the fusion of information acquired by different imaging techniques, from different directions or even acquired at different times. These computational methods are also used for more effective segmentation of bio structures, the building of more realistic models, and the extraction of more reliable information, among other complex tasks. Within image registration, there is image matching and image interpolation. Image matching establishes correspondences between the related features in two related images, and image interpolation is carried out as the transformations are applied to images in the discrete domain [1].Here, we will introduce the computational methodologies for registering static images and image sequences that we have developed, including matching features presented in the input images. Furthermore, we present and discuss practical cases involving static images, image sequences and images acquired by different imaging techniques. Finally, the conclusions and future perspectives are pointed out.

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