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.     

Pedobarographic statistical parametric mapping (pSPM): a pixel-level approach to foot pressure image analysis

Traditional pedobarographic analyses conduct statistical tests on single pressure values extracted from discrete anatomical regions, a process which yields a low-resolution view of the continuous foot-ground interaction and which can involve substantial user interaction for region definition. Using image processing techniques derived from a cerebral imaging methodology called 'statistical parametric mapping' (SPM), we describe a fully automatic method that requires no anatomical assumptions or region definitions and that generates high-resolution continuous statistical maps across the entire plantar foot surface. Here, we demonstrate both pedobarographic SPM (pSPM) and its robustness to arbitrary foot postures by producing statistical maps for a sample of nine healthy young adults walking: normally, with everted feet, and with inverted feet. After spatially smoothing pedobarographic images, within-subjects (WS) and between-subjects (BS) registration were performed using an optimal rigid body transformation and an optimum affine transformation, respectively. Statistical tests were performed over all 742 foot pixels of the 270 registered images using a linear mass-univariate model and the resulting SPMs were compared qualitatively with results obtained using a traditional ten-region technique. SPMs were found to provide a qualitatively improved view of pedobarographic changes, but the more important finding was that regional pedobarographic statistics can misrepresent the trends of their constituent pixels and thus potentially lead to misinterpretations of foot function. Since pSPM is fully non-interactive, is robust to arbitrary foot posture, and provides rapid and easily interpretable results, it appears to be a suitable alternative to regionalization for routine pedobarographic analyses in both laboratory and clinic.     

Assessing the significance of pedobarographic signals using random field theory

Traditional pedobarographic statistical analyses are conducted over discrete regions. Recent studies have demonstrated that regionalization can corrupt pedobarographic field data through conflation when arbitrary dividing lines inappropriately delineate smooth field processes. An alternative is to register images such that homologous structures optimally overlap and then conduct statistical tests at each pixel to generate statistical parametric maps (SPMs). The significance of SPM processes may be assessed within the framework of random field theory (RFT). RFT is ideally suited to pedobarographic image analysis because its fundamental data unit is a lattice sampling of a smooth and continuous spatial field. To correct for the vast number of multiple comparisons inherent in such data, recent pedobarographic studies have employed a Bonferroni correction to retain a constant family-wise error rate. This approach unfortunately neglects the spatial correlation of neighbouring pixels, so provides an overly conservative (albeit valid) statistical threshold. RFT generally relaxes the threshold depending on field smoothness and on the geometry of the search area, but it also provides a framework for assigning p values to suprathreshold clusters based on their spatial extent. The current paper provides an overview of basic RFT concepts and uses simulated and experimental data to validate both RFT-relevant field smoothness estimations and RFT predictions regarding the topological characteristics of random pedobarographic fields. Finally, previously published experimental data are re-analysed using RFT inference procedures to demonstrate how RFT yields easily understandable statistical results that may be incorporated into routine clinical and laboratory analyses.     

Evaluation of a 3D object registration method for analysis of humeral kinematics

In 3D image-based studies of joint kinematics, 3D registration methods should be automatic, insensitive to segmentation inconsistencies and use coordinate systems that have clinically relevant orientations and locations because this is important for analyzing rotation angles and translation directions. We developed and evaluated a registration method, which is based on the cylindrical geometry of the humerus shaft and an analysis of the inertia moments of the humerus head, in order to consistently and automatically orient the humerus coordinate system according to its anatomy. Registration techniques must be thoroughly evaluated. In this study we used a well-detectable marker as reference, from which coordinate system determination errors of a 3D object could be measured. This allowed us to quantify by means of unique error analysis the translational and rotational errors in terms of how much and about/along which humeral axis errors occurred. The evaluation experiments were performed using virtual rotations of 3D humeral binary image, a humerus model and a 3D image of a volunteer's shoulder. They indicated that the humeral coordinate system determination errors primarily originated from segmentation inconsistencies, which influenced mostly the humeral transverse axes orientation. The error analysis revealed that the developed registration method reduced the effect of manual segmentation inconsistencies on the orientation of the humeral transverse axes up to 37%, in comparison to the commonly used inertia registration.     

New insights into the plantar pressure correlates of walking speed using pedobarographic statistical parametric mapping (pSPM)

This study investigates the relation between walking speed and the distribution of peak plantar pressure and compares a traditional ten-region subsampling (10RS) technique with a new technique: pedobarographic statistical parametric mapping (pSPM). Adapted from cerebral fMRI methodology, pSPM is a digital image processing technique that registers foot pressure images such that homologous structures optimally overlap, thereby enabling statistical tests to be conducted at the pixel level. Following previous experimental protocols, we collected pedobarographic records from 10 subjects walking at three different speeds: slow, normal, and fast. Walking speed was recorded and correlated with the peak pressures extracted from the 10 regions, and subsequently with the peak pixel data extracted after pSPM preprocessing. Both methods revealed significant positive correlation between peak plantar pressure and walking speed over the rearfoot and distal forefoot after Bonferroni correction for multiple comparisons. The 10RS analysis found positive correlation in the midfoot and medial proximal forefoot, but the pixel data exhibited significant negative correlation throughout these regions (p<5x10(-5)). Comparing the statistical maps from the two approaches shows that subsampling may conflate pressure differences evident in pixel-level data, obscuring or even reversing statistical trends. The negative correlation observed in the midfoot implies reduced longitudinal arch collapse with higher walking speeds. We infer that this results from pre- or early-stance phase muscle activity and speculate that preferred walking speed reflects, in part, a balance between the energy required to tighten the longitudinal arch and the apparent propulsive benefits of the stiffened arch.     

Measuring humeral head translation using fluoroscopy: A validation study

Numerous techniques have been employed to monitor humeral head translation due to its involvement with several shoulder pathologies. However, most of the techniques were not validated. The objective of this study is to compare the accuracy of manual digitization and contour registration in measuring superior translation of the humeral head. Eight pairs of cadaver scapulae and humerii bones were harvested for this study. Each scapula and humerus was secured in a customized jig that allowed for control of humeral head translations and a vise that permitted rotations of the scapula about three axes. Fluoroscopy was used to take images of the shoulder bones. Scapular orientation was manipulated in different positions while the humerus was at 90° of humeral elevation in the scapular plane. Humeral head translation was measured using the two methods and was compared to the known translation. Additionally, accuracy of the contour registration method to measure 2-D scapular rotations was assessed. The range for the root mean square (RMS) error for manual digitization method was 0.27 mm - 0.43 mm and the contour registration method had a RMS error ranging from 0.18 mm - 0.40 mm. In addition, the RMS error for the scapular angle rotation using the contour registration method was 2.4°. Both methods showed acceptable errors. However, on average, the contour registration method showed lesser measurement error compared to the manual digitization method. In addition, the contour registration method was able to show good accuracy in measuring rotation that is useful in 2-D image analysis.     

Towards an efficient and robust foot classification from pedobarographic images

This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index (AI) and the modified AI. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The results were outstanding, as all feet under analysis were correctly classified as left or right and no significant differences were observed between the footprint indices calculated using the computational solution and the traditional manual method. The robustness of the proposed framework to arbitrary foot orientations and to the acquisition device was also tested and confirmed.     

Registration of pedobarographic image data in the frequency domain

Image registration has been used to support pixel-level data analysis on pedobarographic image data sets. Some registration methods have focused on robustness and sacrificed speed, but a recent approach based on external contours offered both high computational processing speed and high accuracy. However, since contours can be influenced by local perturbations, we sought more global methods. Thus, we propose two new registration methods based on the Fourier transform, cross-correlation and phase correlation which offer high computational speed. We found out that both proposed methods revealed high accuracy for the similarity measures considered, using control geometric transformations. Additionally, both methods revealed high computational processing speed which, combined with their accuracy and robustness, allows their implementation in near-real-time applications. Furthermore, we found that the current methods were robust to moderate levels of noise, and consequently, do not require noise removal procedure like the contours method does.     

Towards an efficient and robust foot classification from pedobarographic images

This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index (AI) and the modified AI. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The results were outstanding, as all feet under analysis were correctly classified as left or right and no significant differences were observed between the footprint indices calculated using the computational solution and the traditional manual method. The robustness of the proposed framework to arbitrary foot orientations and to the acquisition device was also tested and confirmed.     

Non-rigid registration of serial dedicated breast CT,longitudinal dedicated breast CT and PET/CT images using the diffeomorphic demons method

Rationale and objectivesDedicated breast CT and PET/CT scanners provide detailed 3D anatomical and functional imaging data sets and are currently being investigated for applications in breast cancer management such as diagnosis, monitoring response to therapy and radiation therapy planning. Our objective was to evaluate the performance of the diffeomorphic demons (DD) non-rigid image registration method to spatially align 3D serial (pre- and post-contrast) dedicated breast computed tomography (CT), and longitudinally-acquired dedicated 3D breast CT and positron emission tomography (PET)/CT images.MethodsThe algorithmic parameters of the DD method were optimized for the alignment of dedicated breast CT images using training data and fixed. The performance of the method for image alignment was quantitatively evaluated using three separate data sets; (1) serial breast CT pre- and post-contrast images of 20 women, (2) breast CT images of 20 women acquired before and after repositioning the subject on the scanner, and (3) dedicated breast PET/CT images of 7 women undergoing neo-adjuvant chemotherapy acquired pre-treatment and after 1 cycle of therapy.ResultsThe DD registration method outperformed no registration (p < 0.001) and conventional affine registration (p ≤ 0.002) for serial and longitudinal breast CT and PET/CT image alignment. In spite of the large size of the imaging data, the computational cost of the DD method was found to be reasonable (3–5 min).ConclusionsCo-registration of dedicated breast CT and PET/CT images can be performed rapidly and reliably using the DD method. This is the first study evaluating the DD registration method for the alignment of dedicated breast CT and PET/CT images.     

Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel,Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data

Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.     

Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.     

Comparison of image registration performed with MV cone beam CT and CT on rails and Syngo™ Adaptive Targeting software

BackgroundMinimization of geometric errors in treatment delivery is essential in modern conformal and intensity-modulated techniques.AimIn this paper two Siemens systems, MVision megavoltage cone beam CT, and CTVision (CT on rails), are compared.Material and MethodsThe reproducibility and uncertainty of the image registration procedure performed with Adaptive Targeting (AT) software were evaluated. Both systems were evaluated by means of simulating the clinical situation with an anthropomorphic phantom in three anatomical sites: head & neck, thorax and pelvis.ResultsThe results for two methods of image registration, manual and automatic, were evaluated separately. The manual procedure was used by two users, more and less experienced.ConclusionsThe MVision system and CTVision and the Therapist Adaptive software ensure image registration with the uncertainty of about 2.0 mm (2 standard deviations). In the case of the automatic registration method better reproducibility of image registration was obtained for MVision. For CTVision the necessity of manual identification of the machine isocentre made the registration less reproducible. In the case of MVision, the automatic method was more reproducible than the manual one (smaller dispersion of results). In the case of CTVision, similar results were obtained for both registration methods. In the case of manual registration slightly better reproducibility for CT data acquired at 2 mm slice thickness and 2 mm slice separation than for data acquired at 5 mm slice thickness and 5 mm slice separation were obtained. Similar results of manual registration performed by more and less experienced users were obtained.     

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.     

Reorienting in Virtual 3D Environments: Do Adult Humans Use Principal Axes,Medial Axes or Local Geometry?

Studies have shown that animals, including humans, use the geometric properties of environments to orient. It has been proposed that orientation is accomplished primarily by encoding the principal axes (i.e., global geometry) of an environment. However, recent research has shown that animals use local information such as wall length and corner angles as well as local shape parameters (i.e., medial axes) to orient. The goal of the current study was to determine whether adult humans reorient according to global geometry based on principal axes or whether reliance is on local geometry such as wall length and sense information or medial axes. Using a virtual environment task, participants were trained to select a response box located at one of two geometrically identical corners within a featureless rectangular-shaped environment. Participants were subsequently tested in a transformed L-shaped environment that allowed for a dissociation of strategies based on principal axes, medial axes and local geometry. Results showed that participants relied primarily on a medial axes strategy to reorient in the L-shaped test environment. Importantly, the search behaviour of participants could not be explained by a principal axes-based strategy.     

Porosity-geometry interaction in the conservation of bone strength

Automated image analysis of a small sample of femoral cross-section radiographs has revealed a consistent difference in bone porosity relative to geometry. Two sub-periosteal fields were assessed microscopically, with field location determined with reference to the principal moment axes (Imax, Imin). The data indicate that: (1) porosity is greatest in the direction of maximum geometric resistance to bending, along the Imin axis; and (2) porosity differences between the Imax and Imin fields decrease as the bone becomes more circular in cross-sectional shape.     

Cell-by-Cell Alignment of Repeated Specular Microscopy Images from the Same Eye

Purpose

Modern specular microscopes (SM) robustly depict the same central area of the corneal endothelium at different time points through a built-in fixation light. However, repeated image acquisitions slightly shift and rotate because of minute changes in head position in the chin and forehead rest. This prevents the manual retrieval of individual corneal endothelial cells (CECs) in repeated measurements because SM images usually lack obvious landmarks. We devised and validated an image registration algorithm that aligns SM images from the same eye to make corresponding CECs coincide.

Methods

We retrospectively selected 27 image pairs for the presence of significant image overlap. Each image pair had been recorded on the same day and of the same eye. We applied our registration method in each image pair. Two observers independently validated, by means of alternation flicker, that the image pairs had been correctly aligned. We also repeatedly applied our registration method on unrelated image pairs by randomly drawing images and making certain that the images did not originate from the same eye. This was done to assess the specifity of our method.

Results

All automated registrations of the same-day and same-eye image pairs were accurate. However, one single image incorrectly failed to trigger the non-match diagnosis twice in 81 registration attempts between unrelated images. As it turned out, this particular image depicted only 73 CECs. The average number of CECs was 253 (range 73–393).

Conclusion

Repeated non-contact SM images can be automatedly aligned so that the corresponding CECs coincide. Any successful alignment can be considered as proof of the retrieval of identical CECs as soon as at least 100 CEC centroids have been identified. We believe our method is the first to robustly confirm endothelial stability in individual eyes.     

The Correlation Between Texture Features and Fibrous Cap Thickness of Lipid-Rich Atheroma Based on Optical Coherence Tomography Imaging

Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. Methods: Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs of IVOCT images. Then, 32 features, which are associated with the structural and biochemical changes of tissue, were carried out to describe the properties of ROIs. The FCT in grayscale IVOCT images were manually measured by two independent observers. In order to analysis the correlation between IVOCT image features and manual FCT measurements, linear regression approach was performed. Results: Inter-observer agreement of the twice manual FCT measurements was excellent with an intraclass correlation coefficient (ICC) of 0.99. The correlation coefficient between each individual feature set and mean FCT of OCT images were 0.68 for FOS, 0.80 for GLCM, 0.74 for NGTDM, 0.72 for FD, 0.62 for IM and 0.58 for SP. The fusion image features of automatic segmented ROIs and FCT measurements improved the results significantly with a high correlation coefficient (r= 0.91, p<0.001). Conclusion The OCT images features demonstrated the perfect performances and could be used for automatic qualitative analysis and the identification of high-risk plaques instead manual FCT measurements.     

Registration methods in radiotherapy

PurposeThe aim of this study is to present a short and comprehensive review of the methods of medical image registration, their conditions and applications in radiotherapy. A particular focus was placed on the methods of deformable image registration.MethodsTo structure and deepen the knowledge on medical image registration in radiotherapy, a medical literature analysis was made using the Google Scholar browser and the medical database of the PubMed library.ResultsChronological review of image registration methods in radiotherapy based on 34 selected articles. A particular attention was given to show: (i) potential regions of the application of different methods of registration, (ii) mathematical basis of the deformable methods and (iii) the methods of quality control for the registration process.ConclusionsThe primary aim of the medical image registration process is to connect the contents of images. What we want to achieve is a complementary or extended knowledge that can be used for more precise localisation of pathogenic lesions and continuous improvement of patient treatment. Therefore, the choice of imaging mode is dependent on the type of clinical study. It is impossible to visualise all anatomical details or functional changes using a single modality machine. Therefore, fusion of various modality images is of great clinical relevance. A natural problem in analysing the fusion of medical images is geographical errors related to displacement. The registered images are performed not at the same time and, very often, at different respiratory phases.     

Automatic model-based roentgen stereophotogrammetric analysis (RSA) of total knee prostheses

Conventional radiography is insensitive for early and accurate estimation of the mal-alignment and wear of knee prostheses. The two-staged (rough and fine) registration of the model-based RSA technique has recently been developed to in vivo estimate the prosthetic pose (i.e, location and orientation). In the literature, rough registration often uses template match or manual adjustment of the roentgen images. Additionally, possible error induced by the nonorthogonality of taking two roentgen images neither examined nor calibrated prior to fine registration. This study developed two RSA methods for automate the estimation of the prosthetic pose and decrease the nonorthogonality-induced error. The predicted results were validated by both simulative and experimental tests and compared with reported findings in the literature. The outcome revealed that the feature-recognized method automates pose estimation and significantly increases the execution efficiency up to about 50 times in comparison with the literature counterparts. Although the nonorthogonal images resulted in undesirable errors, the outline-optimized method can effectively compensate for the induced errors prior to fine registration. The superiority in automation, efficiency, and accuracy demonstrated the clinical practicability of the two proposed methods especially for the numerous fluoroscopic images of dynamic motion.