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Fourier transform infrared (FTIR) spectroscopic images provide rich information of the biochemical tissue composition that can be analyzed together with other microscopy modalities to perform objective pathological diagnoses. Hematoxylin and Eosin (H&E) stained images are the reference images that pathologists use to make a final diagnosis of most diseases, such as cancer. Therefore, H&E images may be the most interesting imaging modality to be fused with FTIR images. Unfortunately, H&E stain introduces severe confounding artifacts in the FTIR spectra. Thus, in repeatable studies different slices of tissue must be used to acquire images for each imaging modality, which must be aligned so that the different regions of tissue spatially match. The main objective of this manuscript is to establish a complete pipeline where the two types of images (H&E and FTIR) from different tissue sections are aligned or registered. The proposed automatic framework starts by obtaining grayscale images from both FTIR raw data and H&E images where analogous anatomical structures are easily distinguishable. In the first alignment step, a feature-based registration produces a fast coarse rigid alignment by using the Scale Invariant Feature Transform (SIFT) algorithm to automatically find and match relevant keypoints in both grayscale images. Due to the spatial variability between samples, different combinations of SIFT parameters are explored and the best combination is selected through the maximization of a similarity measure between the aligned images. In the second alignment step, an intensity-based registration refines the initial alignment and compensates for the local spatial differences between the tissue sections by iteratively estimating a non-rigid transformation. This methodology was used to register 47 colon samples from three different pathological groups (16 normal, 16 intermediate and 15 tumoral) with good overall results, which were quantitatively evaluated for both registration steps. In the first rigid alignment step, the global median of difference in positioning compared to a manual registration was under 1 pixel. In the second registration step, the global median gain in mutual information between the registered images was 0.125 bits. In contrast to existing approaches, the proposed method does not need a prior segmentation step that may introduce errors and reduce the spatial information content, which is crucial when different sections of tissue are used. It can improve the accuracy to combine the spatial information extracted from both the traditional H&E stained images and the emerging FTIR spectroscopy. [ABSTRACT FROM AUTHOR]

Non-rigid multi-modal image registration plays an important role in medical image processing and analysis. Optimization is a key component of image registration. Mapped as a large-scale optimization problem, non-rigid image registration often requires global optimization methods because the functions defined by similarity metrics are generally non-convex and irregular. In this paper, a novel optimization method is proposed by combining the limited memory Broyden–Fletcher–Goldfarb–Shanno with boundaries (L-BFGS-B) with cat swarm optimization (CSO) for non-rigid multi-modal image registration using the normalized mutual information (NMI) measure and the free-form deformations (FFD) model. The proposed hybrid L-BFGS-B and CSO (HLCSO) method uses cooperative coevolving to tackle non-rigid image registration, and employs block grouping as the grouping strategy to capture the interdependency among variables. Moreover, to achieve faster convergence and higher accuracy of the final solution, the local optimization method L-BFGS-B and the roulette wheel method are introduced into the seeking mode and the tracing mode of the HLCSO, respectively. Extensive experiments on 3D CT, PET, T1, T2 and PD weighted MR images demonstrate that the proposed method outperforms the L-BFGS-B method and the CSO method in terms of registration accuracy, and it is provided with reasonable computational efficiency. [ABSTRACT FROM AUTHOR]