Evaluation of the effect of sagging correction calibration errors in radiotherapy software on image matching

To investigate the impact of sagging correction calibration errors in radiotherapy software on image matching. Three software applications were used, with and without a polymethyl methacrylate rod supporting the ball bearings (BB). The calibration error for sagging correction across nine flex maps (FMs) was determined by shifting the BB positions along the Left-Right (LR), Gun-Target (GT), and Up-Down (UD) directions from the reference point. Lucy and pelvic phantom cone-beam computed tomography (CBCT) images underwent auto-matching after modifying each FM. Image deformation was assessed in orthogonal CBCT planes, and the correlations among BB shift magnitude, deformation vector value, and differences in auto-matching were analyzed. The average difference in analysis results among the three softwares for the Winston-Lutz test was within 0.1 mm. The determination coefficients (R2) between the BB shift amount and Lucy phantom matching error in each FM were 0.99, 0.99, and 1.00 in the LR-, GT-, and UD-directions, respectively. The pelvis phantom demonstrated no cross-correlation in the GT direction during auto-matching error evaluation using each FM. The correlation coefficient (r) between the BB shift and the deformation vector value was 0.95 on average for all image planes. Slight differences were observed among software in the evaluation of the Winston-Lutz test. The sagging correction calibration error in the radiotherapy imaging system was caused by an auto-matching error of the phantom and deformation of CBCT images.

An orientation-free ring feature descriptor with stain-variability normalization for pathology image matching

Comprehensively analyzing the corresponding regions in the images of serial slices stained using different methods is a common but important operation in pathological diagnosis. To help increase the efficiency of the analysis, various image registration methods are proposed to match the corresponding regions in different images, but their performance is highly influenced by the rotations, deformations, and variations of staining between the serial pathology images. In this work, we propose an orientation-free ring feature descriptor with stain-variability normalization for pathology image matching. Specifically, we normalize image staining to similar levels to minimize the impact of staining differences on pathology image matching. To overcome the rotation and deformation issues, we propose a rotation-invariance orientation-free ring feature descriptor that generates novel adaptive bins from ring features to build feature vectors. We measure the Euclidean distance of the feature vectors to evaluate keypoint similarity to achieve pathology image matching. A total of 46 pairs of clinical pathology images in hematoxylin-eosin and immunohistochemistry straining to verify the performance of our method. Experimental results indicate that our method meets the pathology image matching accuracy requirements (error ¡ 300μm), especially competent for large-angle rotation cases common in clinical practice.

Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

The first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a 1280 × 768 image resolution is achieved which is favorably faster in comparison with other work.

Impact of abdominal compression on setup error and image matching during radical abdominal radiotherapy

PURPOSE

To determine the impact of abdominal compression (AC) on setup error and image matching time.

MATERIALS AND METHODS

This study included 72 liver, pancreas and abdominal node patients treated radically from 2016 to 2019 in a single centre. Patients received either SBRT or conventional radical fractionation (CRF). Compressed patients were supine, arms up with kneefix and AC equipment. Uncompressed patients were supine, arms up with kneefix. All patients received daily online-matched CBCTs before treatment. Initial setup error was determined for all patients. Registration error was assessed for 10 liver and 10 pancreas patients. Image matching times were determined using beam on times. Statistical tests conducted were an F-test to compare variances in setup error, Student's t-tests for setup error and average image analysis, and a Wilcoxon Mann Whitney test for imaging matching time analysis.

RESULTS

Initial setup displacement was similar between compressed and uncompressed patients. Displacements > 1 cm occurred more frequently in the longitudinal direction for most patients. SBRT patients required more additional manual positioning following imaging. Mean absolute registration error in the SI direction was 5.4 mm and 3.3 mm for uncompressed and compressed pancreas patients respectively and 1.7 mm and 0.8 mm for uncompressed and compressed liver patients respectively. Compressed patients required less time for image matching and fewer images per fraction on average. Repeat imaging occurred more frequently in SBRT and uncompressed patients.

CONCLUSIONS

Although abdominal compression has no significant impact on setup error, it can reduce imaging matching times resulting in improved treatment accuracy.

Anisotropic-Scale Junction Detection and Matching for Indoor Images

Junctions play an important role in characterizing local geometrical structures of images, and the detection of which is a longstanding but challenging task. Existing junction detectors usually focus on identifying the location and orientations of junction branches while ignoring their scales, which, however, contain rich geometries of images. This paper presents a novel approach for junction detection and characterization, which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an a-contrario model. The output junctions are with anisotropic scales, saying that a scale parameter is associated with each branch of a junction and are thus named as anisotropic-scale junctions (ASJs). We then apply the new detected ASJs for matching indoor images, where there are dramatic changes of viewpoints and the detected local visual features, e.g., key-points, are usually insufficient and lack distinctive ability. We propose to use the anisotropic geometries of our junctions to improve the matching precision of indoor images. The matching results on sets of indoor images demonstrate that our approach achieves the state-of-the-art performance on indoor image matching.Junctions play an important role in characterizing local geometrical structures of images, and the detection of which is a longstanding but challenging task. Existing junction detectors usually focus on identifying the location and orientations of junction branches while ignoring their scales, which, however, contain rich geometries of images. This paper presents a novel approach for junction detection and characterization, which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an a-contrario model. The output junctions are with anisotropic scales, saying that a scale parameter is associated with each branch of a junction and are thus named as anisotropic-scale junctions (ASJs). We then apply the new detected ASJs for matching indoor images, where there are dramatic changes of viewpoints and the detected local visual features, e.g., key-points, are usually insufficient and lack distinctive ability. We propose to use the anisotropic geometries of our junctions to improve the matching precision of indoor images. The matching results on sets of indoor images demonstrate that our approach achieves the state-of-the-art performance on indoor image matching.

Improving Retinal Image Quality Using Registration with an SIFT Algorithm in Quasi-Confocal Line Scanning Ophthalmoscope

When high-magnification images are taken with a quasi-confocal line scanning ophthalmoscope (LSO), the quality of images always suffers from Gaussian noise, and the signal to noise ratio (SNR) is very low for a safer laser illumination. In addition, motions of the retina severely affect the stabilization of the real-time video resulting in significant distortions or warped images. We describe a scale-invariant feature transform (SIFT) algorithm to automatically abstract corner points with subpixel resolution and match these points in sequential images using an affine transformation. Once n images are aligned and averaged, the noise level drops by a factor of [Formula: see text] and the image quality is improved. The improvement of image quality is independent of the acquisition method as long as the image is not warped, particularly severely during confocal scanning. Consequently, even better results can be expected by implementing this image processing technique on higher resolution images.

Improved Multiple Matching Method for Observing Glacier Motion with Repeat Image Feature Tracking

Repeat Image Feature Tracking (RIFT) is commonly used to measure glacier surface motion from pairs of images, most often utilizing normalized cross correlation (NCC). The Multiple-Image Multiple-Chip (MIMC) algorithm successfully employed redundant matching (i.e. repeating the matching process over each area using varying combinations of settings) to increase the matching success rate. Due to the large number of repeat calculations, however, the original MIMC algorithm was slow and still prone to failure in areas of high shearing flow. Here we present several major updates to the MIMC algorithm that increase both speed and matching success rate. First, we include additional redundant measurements by swapping the image order and matching direction; a process we term Quadramatching. Second, we utilize a priori ice velocity information to confine the NCC search space through a system we term dynamic linear constraint (DLC), which substantially reduces the computation time and increases the rate of successful matches. Additionally, we develop a novel post-processing algorithm, pseudosmoothing, to determine the most probable displacement. Our tests reveal the complimentary and multiplicative nature of these upgrades in their improvement in overall MIMC performance.

Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume

Due to the availability of good and reasonably priced auxiliary data, the use of model-based regression-synthetic estimators for small area estimation is popular in operational settings. Examples are forest management inventories, where a linking model is used in combination with airborne laser scanning data to estimate stand-level forest parameters where no or too few observations are collected within the stand. This paper focuses on different approaches to estimating the variances of those estimates. We compared a variance estimator which is based on the estimation of superpopulation parameters with variance estimators which are based on predictions of finite population values. One of the latter variance estimators considered the spatial autocorrelation of the residuals whereas the other one did not. The estimators were applied using timber volume on stand level as the variable of interest and photogrammetric image matching data as auxiliary information. Norwegian National Forest Inventory (NFI) data were used for model calibration and independent data clustered within stands were used for validation. The empirical coverage proportion (ECP) of confidence intervals (CIs) of the variance estimators which are based on predictions of finite population values was considerably higher than the ECP of the CI of the variance estimator which is based on the estimation of superpopulation parameters. The ECP further increased when considering the spatial autocorrelation of the residuals. The study also explores the link between confidence intervals that are based on variance estimates as well as the well-known confidence and prediction intervals of regression models.

Covariant Image Representation with Applications to Classification Problems in Medical Imaging

Images are often considered as functions defined on the image domains, and as functions, their (intensity) values are usually considered to be invariant under the image domain transforms. This functional viewpoint is both influential and prevalent, and it provides the justification for comparing images using functional Lp -norms. However, with the advent of more advanced sensing technologies and data processing methods, the definition and the variety of images has been broadened considerably, and the long-cherished functional paradigm for images is becoming inadequate and insufficient. In this paper, we introduce the formal notion of covariant images and study two types of covariant images that are important in medical image analysis, symmetric positive-definite tensor fields and Gaussian mixture fields, images whose sample values covary i.e., jointly vary with image domain transforms rather than being invariant to them. We propose a novel similarity measure between a pair of covariant images considered as embedded shapes (manifolds) in the ambient space, a Cartesian product of the image and its sample-value domains. The similarity measure is based on matching the two embedded low-dimensional shapes, and both the extrinsic geometry of the ambient space and the intrinsic geometry of the shapes are incorporated in computing the similarity measure. Using this similarity as an affinity measure in a supervised learning framework, we demonstrate its effectiveness on two challenging classification problems: classification of brain MR images based on patients' age and (Alzheimer's) disease status and seizure detection from high angular resolution diffusion magnetic resonance scans of rat brains.

How do field of view and resolution affect the information content of panoramic scenes for visual navigation? A computational investigation

The visual systems of animals have to provide information to guide behaviour and the informational requirements of an animal's behavioural repertoire are often reflected in its sensory system. For insects, this is often evident in the optical array of the compound eye. One behaviour that insects share with many animals is the use of learnt visual information for navigation. As ants are expert visual navigators it may be that their vision is optimised for navigation. Here we take a computational approach in asking how the details of the optical array influence the informational content of scenes used in simple view matching strategies for orientation. We find that robust orientation is best achieved with low-resolution visual information and a large field of view, similar to the optical properties seen for many ant species. A lower resolution allows for a trade-off between specificity and generalisation for stored views. Additionally, our simulations show that orientation performance increases if different portions of the visual field are considered as discrete visual sensors, each giving an independent directional estimate. This suggests that ants might benefit by processing information from their two eyes independently.

A secure online image trading system for untrusted cloud environments

In conventional image trading systems, images are usually stored unprotected on a server, rendering them vulnerable to untrusted server providers and malicious intruders. This paper proposes a conceptual image trading framework that enables secure storage and retrieval over Internet services. The process involves three parties: an image publisher, a server provider, and an image buyer. The aim is to facilitate secure storage and retrieval of original images for commercial transactions, while preventing untrusted server providers and unauthorized users from gaining access to true contents. The framework exploits the Discrete Cosine Transform (DCT) coefficients and the moment invariants of images. Original images are visually protected in the DCT domain, and stored on a repository server. Small representation of the original images, called thumbnails, are generated and made publicly accessible for browsing. When a buyer is interested in a thumbnail, he/she sends a query to retrieve the visually protected image. The thumbnails and protected images are matched using the DC component of the DCT coefficients and the moment invariant feature. After the matching process, the server returns the corresponding protected image to the buyer. However, the image remains visually protected unless a key is granted. Our target application is the online market, where publishers sell their stock images over the Internet using public cloud servers.