Image registration is the determination of a geometrical transformation that aligns points in one image of an object with corresponding points in another image of that object. Registration is often used to compare or integrate images obtained from different imaging modalities or align images obtained from the same modality. Registration finds application in many technology fields, such as image stitching (a process of combining multiple images to produce a segmented panorama or high-resolution image) and image stabilisation (a process to reduce blurring associated with the motion of a camera or other imaging device during exposure). Registration may also be employed as a pre-processing step for a wide range of classification and analysis applications such as image calibration, and depth estimation, measurement, and assessment by professionals.
Many applications of image registration are found in the area of medical imaging, where there are more medical image modalities than ever before. Generally, diagnosing based on medical images may involve piecing together information from multiple imaging modalities to make a clinical decision. It takes years of training to become an expert in the combination and interpretation of information from different medical images, and mistakes can be costly. Accordingly, more and more clinicians are relying on technology that can automatically stitch, register, and/or process medical images captured by the same modality or different modalities. Such image processing saves the clinician time and leads to better diagnoses.
One particular application of image registration can be found in digital pathology, which is an image-based information environment that allows management of information generated from digital slides. Digital pathology is enabled in part by virtual microscopy, which is the practice of converting glass slides into digital slides that can be viewed, managed, and analysed on a computer monitor. Virtual microscopy gives physicians the ability to navigate and observe a biological specimen at different simulated magnifications and through different views as though they were controlling a microscope. This is typically achieved using a display device such as a computer monitor or tablet with access to a database of digital slides of the specimen.
There are a number of advantages of virtual microscopy over traditional microscopy. The specimen itself is not required at the time of viewing, thereby facilitating archiving, telemedicine and education. Also, virtual microscopy can enable the processing of the specimen images to reveal pathological features that would be otherwise difficult to observe by eye, for example as part of a computer aided diagnosis system.
Furthermore, digital pathology is often applied to the analysis of histology specimens. A histology specimen may be prepared from a tissue sample by freezing it in paraffin and then slicing into sections. The slices may be stained to reveal particular features, and placed on a microscope slide under a cover slip. These specimen slides are then converted into digital slides by virtual microscopy. A clinician may subsequently examine several adjacent digital slides of the specimen tissue to assess the extent of a disease throughout the tissue. Accordingly, it is often desirable to align the digital slides so that a clinician can easily assess the tissue sample.
There may, however, be complex, nonlinear deformations between adjacent digital slides, which make registration of digital slides difficult. One such factor is the physical axial distance between the digital slides. For instance, if the axial distance between two adjacent sections is large, the digital slides of these sections may contain less common features. Another factor may be variations in the sections introduced when the sections are cut. For instance, striations (ridges), folds, tears, or other physical deformations may be introduced independently to the sections during cutting. A third factor may be variations in the sections caused by staining the section. For instance, different preparations applied to the tissue for staining may be different between sections and cause the same feature to appear quite differently when imaged.
In spite of the difficulties, a variety of registration methods may be employed to align digital slides including, optical or normal flow based methods (e.g. Horn and Schunk), information-theoretic methods (e.g. mutual information), cross-correlation-based methods, gradient based methods, and more recently methods that utilize advanced statistical methods from machine learning. Additionally, there are many ways to model the non-linear deformations that occur between images that either take into account a physical-based model of tissue deformations, such as elastic material deformation or fluid flow, or attempt to model the deformations using basis functions such as radial basis functions, wavelet transforms, and B-splines.
Most registration methods trade off processing speed against accuracy of registration. For example, mutual information is able to register images where the same structure has different intensity in both images (e.g. a CT scan and an MRI scan). However, calculation of standard mutual information is very slow. Many methods have been developed to speed up this calculation often at the expense of an accurate alignment; whereas, other methods have included more information to obtain higher-order mutual information and improved alignment accuracy at the expense of processing time.
In general, however, these methods scale poorly with image size and may be very slow for large images such as those produced by virtual microscopy. A need therefore exists for techniques to register large images rapidly with a high level of accuracy.