Medical image registration using fuzzy theory

Mutual information (MI)-based registration, which uses MI as the similarity measure, is a representative method in medical image registration. It has an excellent robustness and accuracy, but with the disadvantages of a large amount of calculation and a long processing time. In this paper, by computing the medical image moments, the centroid is acquired. By applying fuzzy c-means clustering, the coordinates of the medical image are divided into two clusters to fit a straight line, and the rotation angles of the reference and floating images are computed, respectively. Thereby, the initial values for registering the images are determined. When searching the optimal geometric transformation parameters, we put forward the two new concepts of fuzzy distance and fuzzy signal-to-noise ratio (FSNR), and we select FSNR as the similarity measure between the reference and floating images. In the experiments, the Simplex method is chosen as multi-parameter optimisation. The experimental results show that this proposed method has a simple implementation, a low computational cost, a fast registration and good registration accuracy. Moreover, it can effectively avoid trapping into the local optima. It is adapted to both mono-modality and multi-modality image registrations.     

Medical image registration using Edgeworth-based approximation of Mutual Information

We propose a new similarity measure for iconic medical image registration, an Edgeworth-based third order approximation of Mutual Information (MI) and named 3-EMI. Contrary to classical Edgeworth-based MI approximations, such as those proposed for independent component analysis, the 3-EMI measure is able to deal with potentially correlated variables. The performance of 3-EMI is then evaluated and compared with the Gaussian and B-Spline kernel-based estimates of MI, and the validation is leaded in three steps. First, we compare the intrinsic behavior of the measures as a function of the number of samples and the variance of an additive Gaussian noise. Then, they are evaluated in the context of multimodal rigid registration, using the RIRE data. We finally validate the use of our measure in the context of thoracic monomodal non-rigid registration, using the database proposed during the MICCAI EMPIRE10 challenge. The results show the wide range of clinical applications for which our measure can perform, including non-rigid registration which remains a challenging problem. They also demonstrate that 3-EMI outperforms classical estimates of MI for a low number of samples or a strong additive Gaussian noise. More generally, our measure gives competitive registration results, with a much lower numerical complexity compared to classical estimators such as the reference B-Spline kernel estimator, which makes 3-EMI a good candidate for fast and accurate registration tasks.     

Medical image registration: a review

This paper presents a review of automated image registration methodologies that have been used in the medical field. The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application. The registration methodologies under review are classified into intensity or feature based. The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described.     

An automatic image registration scheme using Tsallis entropy

We have investigated the registration of mammograms based on the Tsallis entropy using mutual information measure. Tsallis entropy has one more parameter ‘q’ and the values of ‘q’ decide the quality of the registration. Existing Tsallis entropy based algorithms are not automatic as they claimed to be. In this article, an automatic affine image registration based on Tsallis entropy is proposed and its performance is analyzed for clinically acquired mammograms for globally registering them. The accuracy is compared with traditionally used mutual information and normalized mutual information based on Shannon entropy. Our algorithm shows promising results with increased accuracy with reduction in number of evaluations. Further, the need for pre-registration in mammogram is discussed in detail. Through this experiment, it is found that the proposed algorithm is effective enough to replace Shannon and existing Tsallis entropy based affine registration schemes.     

Two-dimensional gel electrophoresis image registration using block-matching techniques and deformation models

Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique—based on successive deformation, search, fitting, filtering, and interpolation stages—is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D–PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.     

Analysis of expression profile using fuzzy adaptive resonance theory

MOTIVATION: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS: We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence. AVAILABILITY: The software is available at http//www.nubio.nagoya-u.ac.jp/proc/index.html     

Results of automatic image registration are dependent on initial manual registration

Measurement of static alignment of articulating joints is of clinical benefit and can be determined using image-based registration. We propose a method that could potentially improve the outcome of image-based registration by using initial manual registration. Magnetic resonance images of two wrist specimens were acquired in the relaxed position and during simulated grasp. Transformations were determined from voxel-based image registration between the two volumes. The volumes were manually aligned to match as closely as possible before auto-registration, from which standard transformations were obtained. Then, translation/rotation perturbations were applied to the manual registration to obtain altered initial positions, from which altered auto-registration transformations were obtained. Models of the radiolunate joint were also constructed from the images to simulate joint contact mechanics. We compared the sensitivity of transformations (translations and rotations) and contact mechanics to altering the initial registration condition from the defined standard. We observed that with increasing perturbation, transformation errors appeared to increase and values for contact force and contact area appeared to decrease. Based on these preliminary findings, it appears that the final registration outcome is sensitive to the initial registration.     

Marker-free image registration of electron tomography tilt-series

Background  

Tilt series are commonly used in electron tomography as a means of collecting three-dimensional information from two-dimensional projections. A common problem encountered is the projection alignment prior to 3D reconstruction. Current alignment techniques usually employ gold particles or image derived markers to correctly align the images. When these markers are not present, correlation between adjacent views is used to align them. However, sequential pairwise correlation is prone to bias and the resulting alignment is not always optimal.     

Multiresolution image registration for two-dimensional gel electrophoresis

In proteomic research, two-dimensional electrophoresis (2-D) is an important tool for investigating differential patterns of qualitative and quantitative protein expression. The strength of the technique is due to its unrivalled power of being able to separate simultaneously thousands of proteins. The key to the comparison of 2-D protein profiles, however, lies in the use of a fast and robust image matching process which is essential to the subsequent quantification procedure. To satisfy the growing demand for a robust and fully automatic method of matching 2-D gel protein separation profiles, we describe in this paper a novel registration technique based on image intensity distribution rather than selected features. The method uses a multiresolution representation of the gel profiles and exploits the fact that coarse approximations to the optimal matching can be extracted efficiently from low-resolution images. This permits the removal of misalignments at different scales in a systematic manner and the strength of the new method has been confirmed by a double blind trial of 111 2-D gel pairs. The proposed method requires neither landmarks nor an a priori image alignment, and takes about five seconds for processing a typical gel pair on a standard personal computer.     

Segmentation of electron tomographic data sets using fuzzy set theory principles

In electron tomography the reconstructed density function is typically corrupted by noise and artifacts. Under those conditions, separating the meaningful regions of the reconstructed density function is not trivial. Despite development efforts that specifically target electron tomography manual segmentation continues to be the preferred method. Based on previous good experiences using a segmentation based on fuzzy logic principles (fuzzy segmentation) where the reconstructed density functions also have low signal-to-noise ratio, we applied it to electron tomographic reconstructions. We demonstrate the usefulness of the fuzzy segmentation algorithm evaluating it within the limits of segmenting electron tomograms of selectively stained, plastic embedded spiny dendrites. The results produced by the fuzzy segmentation algorithm within the framework presented are encouraging.     

The identification of complementarity of molecular surfaces using fuzzy set theory

Fuzzy logic-based algorithms for the quantitative treatment of complementarity of molecular surfaces are presented. The identification of complementary surface patches can be considered as a first step for the solution of molecular docking problems. Based on these initial guesses, docking structures can be further optimized by standard technologies. In this work a simple downhill simplex method for the optimization is used. The algorithms are applied to various biomolecular complexes. For all these complexes, at least one structure was found to be in very good agreement with the experimental data.     

Spatial correlations of trabecular bone microdamage with local stresses and strains using rigid image registration

Although microdamage is known to accumulate in trabecular bone with overloading and aging, the tissue-level stresses and strains associated with local bone failure are not well known. Local correlation of microdamage with microstructural stresses and strains requires methods to accurately register histological sections with micro-computed tomography (micro-CT) based finite element models. In addition, the resolution of correlation (i.e., grid size) selected for analysis may affect the observed results. Therefore, an automated, repeatable, and accurate image registration algorithm was developed to determine the range of local stresses and strains associated with microdamage initiation. Using a two-dimensional rigid registration algorithm, bone structures from histology and micro-CT imaging were aligned. Once aligned, microdamaged regions were spatially correlated with local stresses and strains obtained from micro-CT based finite element analysis. Using this more sophisticated registration technique, we were able to analyze the effects of varying spatial grid resolution on local stresses and strains initiating microdamage. The results indicated that grid refinement to the individual pixel level (pixel-by-pixel method) more precisely defined the range of microdamage initiation compared to manually selected individual damaged and undamaged trabeculae. Using the pixel-by-pixel method, we confirmed that trabecular bone from younger cows sustained higher local strains prior to microdamage initiation compared to older bone.     

Technology and research developments in carotid image registration

Brain stroke is the leading cause of death worldwide. Mortality, morbidity and economic effects of stroke are alarming. Carotid atherosclerosis is the most common cause of stroke. Early identification, monitoring and quantification of carotid plaque with the help of imaging modalities can help manage the stroke and evaluate the effectiveness of medical therapy. Carotid image registration has the potential to improve the monitoring, quantification and characterization of the disease. It helps to accurately correlate the findings of various imaging modalities for the diagnostic and therapeutic purposes. This paper aims to present the current state-of-the-art in carotid image registration techniques. For the monomodality registration, ultrasound and magnetic resonance imaging are the primary concerns. Multimodality registration will cover the combination of different modalities. The registration process and validation methods for carotid image registration are also discussed.     

Dose warping performance in deformable image registration in lung

PurposeIt is unclear that spatial accuracy can reflect the impact of deformed dose distribution. In this study, we used dosimetric parameters to compare an in-house deformable image registration (DIR) system using NiftyReg, with two commercially available systems, MIM Maestro (MIM) and Velocity AI (Velocity).MethodsFor 19 non-small-cell lung cancer patients, the peak inspiration (0%)-4DCT images were deformed to the peak expiration (50%)-4DCT images using each of the three DIR systems, which included computation of the deformation vector fields (DVF). The 0%-gross tumor volume (GTV) and the 0%-dose distribution were also then deformed using the DVFs. The agreement in the dose distributions for the GTVs was evaluated using generalized equivalent uniform dose (gEUD), mean dose (D_mean), and three-dimensional (3D) gamma index (criteria: 3 mm/3%). Additionally, a Dice similarity coefficient (DSC) was used to measure the similarity of the GTV volumes.ResultsD_mean and gEUD demonstrated good agreement between the original and deformed dose distributions (differences were generally less than 3%) in 17 of the patients. In two other patients, the Velocity system resulted in differences in gEUD of 50.1% and 29.7% and in D_mean of 11.8% and 4.78%. The gamma index comparison showed statistically significant differences for the in-house DIR vs. MIM, and MIM vs. Velocity.ConclusionsThe finely tuned in-house DIR system could achieve similar spatial and dose accuracy to the commercial systems. Care must be taken, as we found errors of more than 5% for D_mean and 30% for gEUD, even with a commercially available DIR tool.     

Automated image registration for assessing three-dimensional alignment of entire lower extremity and implant position using bi-plane radiography

An automated image-matching technique is presented to assess alignment of the entire lower extremity for normal and implanted knees and the positioning of implants with respect to bone. Sawbone femur and tibia and femoral and tibial components of a total knee arthroplasty system were used. Three spherical markers were attached to each sawbone and each component to define the local coordinate system. Outlines of the three-dimensional (3D) bone models and component computer-aided design (CAD) models were projected onto extracted contours of the femur, tibia, and implants in frontal and oblique X-ray images. Three-dimensional position of each model was recovered by minimizing the difference between the projected outline and the contour. Median values of the absolute error in estimating relative positions were within 0.5 mm and 0.6° for the femur with respect to the tibia, 0.5 mm and 0.5° for the femoral component with respect to the tibial component, 0.6 mm and 0.6° for the femoral component with respect to the femur, and 0.5 mm and 0.4° for the tibial component with respect to the tibia, indicating significant improvements when compared to manually obtained results.     

Rapid pedobarographic image registration based on contour curvature and optimization

Image registration, the process of optimally aligning homologous structures in multiple images, has recently been demonstrated to support automated pixel-level analysis of pedobarographic images and, subsequently, to extract unique and biomechanically relevant information from plantar pressure data. Recent registration methods have focused on robustness, with slow but globally powerful algorithms. In this paper, we present an alternative registration approach that affords both speed and accuracy, with the goal of making pedobarographic image registration more practical for near-real-time laboratory and clinical applications. The current algorithm first extracts centroid-based curvature trajectories from pressure image contours, and then optimally matches these curvature profiles using optimization based on dynamic programming. Special cases of disconnected images (that occur in high-arched subjects, for example) are dealt with by introducing an artificial spatially linear bridge between adjacent image clusters. Two registration algorithms were developed: a ‘geometric’ algorithm, which exclusively matched geometry, and a ‘hybrid’ algorithm, which performed subsequent pseudo-optimization. After testing the two algorithms on 30 control image pairs considered in a previous study, we found that, when compared with previously published results, the hybrid algorithm improved overlap ratio (p=0.010), but both current algorithms had slightly higher mean-squared error, assumedly because they did not consider pixel intensity. Nonetheless, both algorithms greatly improved the computational efficiency (25±8 and 53±9 ms per image pair for geometric and hybrid registrations, respectively). These results imply that registration-based pixel-level pressure image analyses can, eventually, be implemented for practical clinical purposes.