Affine Registration Research Articles

A feature-based affine registration method for capturing background lung tissue deformation for ground glass nodule tracking

ABSTRACT Apparent changes in lung nodule size assessed via simple image-based measurements from computed tomography (CT) images may be compromised by the effect of the background lung tissue deformation on the nodule, leading to erroneous nodule tracking. We propose a feature-based affine registration method and study its performance vis-a-vis several other registration methods. We implement and test each registration method using a lung- and a lesion-centred region of interest on 10 patient CT datasets featuring 12 nodules. We evaluate each registration method according to the target registration error (TRE) computed across 30–50 homologous fiducial landmarks selected by expert radiologists. Our results show that the proposed feature-based affine lesion-centred registration yielded a 1.11.2 mm TRE, while a Symmetric Normalisation deformable registration yielded a 1.21.2 mm TRE, with a baseline least-square fit of the validation fiducial landmarks of 1.51.2 mm TRE. The proposed feature-based affine registration is computationally efficient, eliminates the need for nodule segmentation, and reduces the susceptibility of artificial deformations. We also conducted a pilot pre-clinical study that showed the proposed feature-based lesion-centred affine registration effectively compensates for the background lung tissue deformation and serves as a reliable baseline registration method prior to assessing lung nodule changes due to disease.

Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease.

A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of Alzheimer's disease (AD) using brain structural magnetic resonance imaging (MRI) data. However, the data available are limited in the case of this specific disease. Training a DL model with a large number of feature parameters on a small dataset of MRI scans will likely lead to overfitting. Overfitting reduces the generality and efficiency of the model. In this study, we show that a traditional nonlinear transformation from native space to template space, as a preprocessing stage, is effective in reducing overfitting through the reduction of spatial variations in the input data. To evaluate this effectiveness, we compare two different pre-processing approaches for DL-based AD classification task: (1) affine registration and (2) nonlinear diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL). The results show that the accuracy of the nonlinear registration based approach is much higher than the affine registration based approach. Furthermore, from the classification results obtained with noisy images, DARTEL is less susceptible to noise than affine registration. In summary, our experimental results suggest that nonlinear transformation is a preferable preprocessing step for training DL-based AD classification models on limited size datasets.

Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning

BackgroundMagnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing. MethodA supervised machine learning (ML) framework is proposed by computing similarity metrics such as normalized cross-correlation, normalized mutual information, and correlation ratio locally. We have used these as candidate features for cross-validation and testing of different ML classifiers. For 5-fold repeated stratified grid search cross-validation, 400 correctly aligned, 2000 randomly generated misaligned images were used from the human connectome project young adult (HCP-YA) dataset. To test the cross-validated models, the datasets from autism brain imaging data exchange (ABIDE I) and information eXtraction from images (IXI) were used. ResultsThe ensemble classifiers, random forest, and AdaBoost yielded best performance with F1-scores, balanced accuracies, and Matthews correlation coefficients in the range of 0.95–1.00 during cross-validation. The predictive accuracies reached 0.99 on the Test set #1 (ABIDE I), 0.99 without and 0.96 with noise on Test set #2 (IXI, stratified w.r.t scanner vendor and field strength). ConclusionsThe cross-validated and tested ML models could be used for QC of both T1w and T2w rigid and affine registrations in large-scale MRI studies.

Globally Optimal Fetoscopic Mosaicking Based on Pose Graph Optimisation With Affine Constraints

Fetoscopic laser ablation surgery could be guided using a high-quality panorama of the operating site, representing a map of the placental vasculature. This can be achieved during the initial inspection phase of the procedure using image mosaicking techniques. Due to the lack of camera calibration in the operating room, it has been mostly modelled as an affine registration problem. While previous work mostly focuses on image feature extraction for visual odometry, the challenges related to large-scale reconstruction (re-localisation, loop closure, drift correction) remain largely unaddressed in this context. This letter proposes using pose graph optimisation to produce globally optimal image mosaics of placental vessels. Our approach follows the SLAM framework with a front-end for visual odometry and a back-end for long-term refinement. Our front-end uses a recent state-of-the-art odometry approach based on vessel segmentation, which is then managed by a key-frame structure and the bag-of-words (BoW) scheme to retrieve loop closures. The back-end, which is our key contribution, models odometry and loop closure constraints as a pose graph with affine warpings between states. This problem in the special Euclidean space cannot be solved by existing pose graph algorithms and available libraries such as G2O. We model states on affine Lie group with local linearisation in its Lie algebra. The cost function is established using Mahalanobis distance with the vectorisation of the Lie algebra. Finally, an iterative optimisation algorithm is adopted to minimise the cost function. The proposed pose graph optimisation is first validated on simulation data with a synthetic trajectory that has different levels of noise and different numbers of loop closures. Then the whole system is validated using real fetoscopic data that has three sequences with different numbers of frames and loop closures. Experimental results validate the advantage of the proposed method compared with baselines.

Construction of a Multi-Phase Contrast Computed Tomography Kidney Atlas.

The Human BioMolecular Atlas Program (HuBMAP) seeks to create a molecular atlas at the cellular level of the human body to spur interdisciplinary innovations across spatial and temporal scales. While the preponderance of effort is allocated towards cellular and molecular scale mapping, differentiating and contextualizing findings within tissues, organs and systems are essential for the HuBMAP efforts. The kidney is an initial organ target of HuBMAP, and constructing a framework (or atlas) for integrating information across scales is needed for visualizing and integrating information. However, there is no abdominal atlas currently available in the public domain. Substantial variation in healthy kidneys exists with sex, body size, and imaging protocols. With the integration of clinical archives for secondary research use, we are able to build atlases based on a diverse population and clinically relevant protocols. In this study, we created a computed tomography (CT) phase-specific atlas for the abdomen, which is optimized for the kidney organ. A two-stage registration pipeline was used by registering extracted abdominal volume of interest from body part regression, to a high-resolution CT. Affine and non-rigid registration were performed to all scans hierarchically. To generate and evaluate the atlas, multiphase CT scans of 500 control subjects (age: 15 - 50, 250 males, 250 females) are registered to the atlas target through the complete pipeline. The abdominal body and kidney registration are shown to be stable with the variance map computed from the result average template. Both left and right kidneys are substantially localized in the high-resolution target space, which successfully demonstrated the sharp details of its anatomical characteristics across each phase. We illustrated the applicability of the atlas template for integrating across normal kidney variation from 64 cm3 to 302 cm3.

Atlas-based segmentation of cochlear microstructures in cone beam CT.

To develop an automated segmentation approach for cochlear microstructures [scala tympani (ST), scala vestibuli (SV), modiolus (Mod), mid-modiolus (Mid-Mod), and round window membrane (RW)] in clinical cone beam computed tomography (CBCT) images of the temporal bone for use in surgical simulation software and for preoperative surgical evaluation. This approach was developed using the publicly available OpenEar (OE) Library that includes temporal bone specimens with spatially registered CBCT and 3D micro-slicing images. Five of these datasets were spatially aligned to our internal OSU atlas. An atlas of cochlear microstructures was created from one of the OE datasets. An affine registration of this atlas to the remaining OE CBCT images was used for automatically segmenting the cochlear microstructures. Quantitative metrics and visual review were used for validating the automatic segmentations. The average DICE metrics were 0.77 and 0.74 for the ST and SV, respectively. The average Hausdorff distance (AVG HD) was 0.11mm and 0.12mm for both scalae. The mean distance between the centroids for the round window was 0.32mm, and the mean AVG HD was 0.09mm. The mean distance and angular rotation between the mid-modiolar axes were 0.11mm and 9.8 degrees, respectively. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. An atlas-based approach using 3D micro-slicing data and affine spatial registration in the cochlear region was successful in segmenting cochlear microstructures of temporal bone anatomy for use in simulation software and potentially for pre-surgical planning and rehearsal.

Polarimetric SAR Image Affine Registration Based on Neighborhood Consensus

Polarimetric SAR Image Affine Registration Based on Neighborhood Consensus

Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains

The use of multiple dyes during histological sample preparation can reveal distinct tissue properties. However, since the slide preparation differs for each dye, the tissue slides are being deformed and a nonrigid registration is required before further processing. The registration of histology images is complicated because of: (i) a high resolution of histology images, (ii) complex, large, nonrigid deformations, (iii) difference in the appearance and partially missing data due to the use of multiple dyes. In this work, we propose a multistep, automatic, nonrigid image registration method dedicated to histology samples acquired with multiple stains. The proposed method consists of a feature-based affine registration, an exhaustive rotation alignment, an iterative, intensity-based affine registration, and a nonrigid alignment based on modality independent neighbourhood descriptor coupled with the Demons algorithm. A dedicated failure detection mechanism is proposed to make the method fully automatic, without the necessity of any manual interaction. The described method was proposed by the AGH team during the Automatic Non-rigid Histological Image Registration (ANHIR) challenge. The ANHIR dataset consists of 481 image pairs annotated by histology experts. Moreover, the ANHIR challenge submissions were evaluated using an independent, server-side evaluation tool. The main evaluation criteria was the target registration error normalized by the image diagonal. The median of median target registration error is below 0.19%. The proposed method is currently the second-best in terms of the average ranking of median target registration error, without statistically significant differences compared to the top-ranked method. We provide an open access to the method software and used parameters, making the results fully reproducible.

Iterative registration for multi‐modality retinal fundus photographs using directional vessel skeleton

This paper proposes an automated registration method for multi-modality retinal fundus photographs based on the directional vessel skeleton. The main purpose is to register two retinal fundus photographs with different modalities of the same scanning region, which can provide multi-modality information for clinicians to diagnose retinal diseases or to make a treatment decision. The directional vessel skeleton of each fundus image is first detected by bias field correction and Gabor filter. The final registered fundus photographs are then obtained by the iterative affine registration between the detected directional vessel skeletons of two photographs. In this work, four kinds of fundus photographs in the macular regions of the patient with diseases, consisting of 20 optical coherence tomography fundus images, 20 colour fundus photographs, 20 fluorescein fundus angiography images and 20 indocyanine green angiography images, are utilised to quantitatively evaluate the proposed method. The root-mean-square errors show an advantageous performance in both registration success rate and accuracy.

Anatomical multiatlas segmentation using local texture statistical properties for matching descriptor with machine learning

AbstractThe use of anatomical multiatlas methods has proven to be one of the most competitive techniques for brain images segmentation. The majority of these methods are based on visual criteria of similarity between groups of an atlas to select a representative patient image to be segmented. However, this criterion is not necessarily linked to the performance of the segmentation. To overcome this dilemma, we present in this article, a new concept of preselection of an anatomical atlas group, which is based on machine learning and using an adapted descriptor that can give an efficient and more precise segmentation of the patient image. The new descriptor, local texture statistical properties for matching descriptor with only affine registration, is adapted from the local texture of matching (LTEMA) descriptor. The proposed method is tested on real MRI brain images (LONI database provided by USC Neurological Imaging Laboratory), and show the capability and the effectiveness of the proposed local descriptor, it has been compared to three local descriptors: scale‐invariant feature transform, speed up robust feature, and LTEMA, as well as the comparison with the registration method. The obtained results show a significant improvement that makes this descriptor recommended for segmentation techniques.

Fast point cloud registration in multidirectional affine transformation

This paper proposed a fast multidirectional affine registration (FMDAR) algorithm for matching two three-dimensional point clouds after multidirectional affine transformation with disorder, noise and missing points. The FMDAR algorithm is based on the statistical characteristics and shape features of point clouds. First, the eigenvalues of the point clouds matrix and the scaling rations are used to establish a system of nonlinear equations by the Vieta's Formulas. In addition, the rotational invariance of global vector features as a constraint is introduced and the cost function is established. Secondly, the scaling rations are calculated by using the Trust Region method to optimize the cost function. Finally, the multidirectional affine registration is transformed into a rigid registration after the scale rations are obtained. The FMDAR algorithm is robust in the presence of noises and we validate the FMDAR algorithm on three-dimensional point clouds with varying degrees of deformation in complex cases. What’s more, results of the simulation show that the FMDAR algorithm has faster registration speed and higher accuracy compared with some existing algorithms.

Deep-learning based multi-modal retinal image registration for the longitudinal analysis of patients with age-related macular degeneration.

This work reports a deep-learning based registration algorithm that aligns multi-modal retinal images collected from longitudinal clinical studies to achieve accuracy and robustness required for analysis of structural changes in large-scale clinical data. Deep-learning networks that mirror the architecture of conventional feature-point-based registration were evaluated with different networks that solved for registration affine parameters, image patch displacements, and patch displacements within the region of overlap. The ground truth images for deep learning-based approaches were derived from successful conventional feature-based registration. Cross-sectional and longitudinal affine registrations were performed across color fundus photography (CFP), fundus autofluorescence (FAF), and infrared reflectance (IR) image modalities. For mono-modality longitudinal registration, the conventional feature-based registration method achieved mean errors in the range of 39-53 µm (depending on the modality) whereas the deep learning method with region overlap prediction exhibited mean errors in the range 54-59 µm. For cross-sectional multi-modality registration, the conventional method exhibited gross failures with large errors in more than 50% of the cases while the proposed deep-learning method achieved robust performance with no gross failures and mean errors in the range 66-69 µm. Thus, the deep learning-based method achieved superior overall performance across all modalities. The accuracy and robustness reported in this work provide important advances that will facilitate clinical research and enable a detailed study of the progression of retinal diseases such as age-related macular degeneration.

Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs

There has been substantial interest in developing techniques for synthesizing CT-like images from MRI inputs, with important applications in simultaneous PET/MR and radiotherapy planning. Deep learning has recently shown great potential for solving this problem. The goal of this research was to investigate the capability of four common clinical MRI sequences (T1-weighted gradient-echo [T1], T2-weighted fat-suppressed fast spin-echo [T2-FatSat], post-contrast T1-weighted gradient-echo [T1-Post], and fast spin-echo T2-weighted fluid-attenuated inversion recovery [CUBE-FLAIR]) as inputs into a deep CT synthesis pipeline. Data were obtained retrospectively in 92 subjects who had undergone an MRI and CT scan on the same day. The patient’s MR and CT scans were registered to one another using affine registration. The deep learning model was a convolutional neural network encoder-decoder with skip connections similar to the U-net architecture and Inception V3 inspired blocks instead of sequential convolution blocks. After training with 150 epochs and a batch size of 6, the model was evaluated using structural similarity index (SSIM), peak SNR (PSNR), mean absolute error (MAE), and dice coefficient. We found that feasible results were attainable for each image type, and no single image type was superior for all analyses. The MAE (in HU) of the resulting synthesized CT in the whole brain was 51.236 ± 4.504 for CUBE-FLAIR, 45.432 ± 8.517 for T1, 44.558 ± 7.478 for T1-Post, and 45.721 ± 8.7767 for T2, showing not only feasible, but also very compelling results on clinical images. Deep learning-based synthesis of CT images from MRI is possible with a wide range of inputs, suggesting that viable images can be created from a wide range of clinical input types.

RegQCNET: Deep quality control for image-to-template brain MRI affine registration

Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. Automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, automated deep neural network approaches have emerged as a method of choice to automatically assess registration quality.In the current study, a compact 3D convolutional neural network, referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template. This quantitative estimation of registration error is expressed using the metric unit system. Therefore, a meaningful task-specific threshold can be manually or automatically defined in order to distinguish between usable and non-usable images.The robustness of the proposed RegQCNET is first analyzed on lifespan brain images undergoing various simulated spatial transformations and intensity variations between training and testing. Secondly, the potential of RegQCNET to classify images as usable or non-usable is evaluated using both manual and automatic thresholds. During our experiments, automatic thresholds are estimated using several computer-assisted classification models (logistic regression, support vector machine, Naive Bayes and random forest) through cross-validation. To this end we use an expert’s visual QC estimated on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared to usual image features such as image correlation coefficient and mutual information.The results show that the proposed deep learning QC is robust, fast and accurate at estimating affine registration error in the processing pipeline.

Point Cloud Registration Algorithm Based on Cauchy Mixture Model

In this paper, the Cauchy mixture distribution is used for rigid and affine registration. The Cauchy function has a wider effective range than the general window function, so it has a greater information utilization ratio than most registration algorithms. This means that the method can quickly match point clouds without initial registration and corresponding points. On the other hand, the computational complexity of Cauchy mixture model is lower than that of widely used Gaussian mixture model. This means that the introduction of Cauchy mixture model will save computing resources. However, the log-likelihood function of the Cauchy mixture distribution is a nonlinear function that is difficult to solve. This paper proposes an optimization method for the Cauchy mixture model, and discusses the advantages of the Cauchy mixture model in rigid and affine registration. In the experiment, compared with several other algorithms, our method quickly and accurately registers point clouds.

Prior-image-based Reconstruction for On-board CBCT Imaging: Application in Image-guided Radiotherapy for Head and Neck Cancer

On-board cone-beam CT (CBCT) imaging has been widely used to reduce set-up errors in head and neck cancer treated with IMRT/VMAT. However, its frequent use has raised concerns for excessive imaging dose and increased risk of developing secondary malignancies. This work investigates the feasibility of significantly reducing CBCT radiation dose by taking advantage of high-quality planning CT and a prior-image-based reconstruction approach. In image-guided radiotherapy for head and neck cancer, one standard protocol (Full-fan, 200 deg rotation, 100 kVp, 0.3 mAs/projection, 500 projections, 150 mAs in total) is employed for kV-CBCT scans. Since the high-quality planning CT scan is a rich source of patient-specific anatomical knowledge, it offers an opportunity to integrate the planning CT into on-board CBCT reconstruction for radiation dose reduction. Therefore, we studied a prior-image-based reconstruction approach for on-board CBCT imaging, which only requires partial projections and thus substantially reduces radiation dose. The prior-image-based reconstruction takes into consideration differences in attenuation coefficients (via histogram matching) and patient positioning (via affine registration) between the planning CT and on-board CBCT scans. Additionally, proper prior information control is applied to ensure the prior-image-based reconstruction does not introduce false features when there are anatomical changes between scans. An anthropomorphic head phantom and two head and neck patient datasets were used to validate the efficacy of the proposed approach. We extracted certain number of projections from the 500 projections to emulate different radiation dose levels. As we decrease the number of projections to as low as 100 (30 mAs equivalent, CTDIvol = 0.63 mGy), the standard FDK reconstructed images are degraded with severe noise and streak artifacts, and the conventional model-based iterative reconstruction generates images with inferior image quality, which could jeopardize the accuracy of patient set-up. In contrast, our proposed prior-image-based reconstruction can yield comparable image quality as the ground truth images reconstructed with the full projections, in terms of noise, resolution, and contrast-to-noise ratio. This indicates an 80% radiation dose reduction for on-board CBCT imaging by the proposed reconstruction scheme. The proposed prior-image-based reconstruction framework provides an opportunity to substantially reduce the radiation dose for on-board CBCT, without compromising image quality. This work offers a solution to reduce cumulative imaging dose as the use of CBCT for image-guided radiotherapy increases.

Multimodal affine registration for ICGA and MCSL fundus images of high myopia.

The registration between indocyanine green angiography (ICGA) and multi-color scanning laser (MCSL) imaging fundus images is vital for the joint linear lesion segmentation in ICGA and MCSL and the evaluation whether MCSL can replace ICGA as a non-invasive diagnosis for linear lesion. To our best knowledge, there are no studies focusing on the image registration between these two modalities. In this paper, we propose a framework based on convolutional neural networks for the multimodal affine registration between ICGA and MCSL images, which contains two parts: coarse registration stage and fine registration stage. In the coarse registration stage, the optic disc is segmented and its centroid is used as a matching point to perform coarse registration. The fine registration stage regresses affine parameters directly using jointly supervised and weakly-supervised loss function. Experimental results show the effectiveness of the proposed method, which lays a sound foundation for further evaluation of non-invasive diagnosis of linear lesion based on MCSL.

Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy

Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of the prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio, and signal-to-noise ratio in TRUS. In this study, we proposed a fully automated deep learning approach based on a weakly supervised method to address these issues. We employed deep learning techniques to combine image segmentation and registration, including affine and nonrigid registration, to perform an automated deformable MRI-TRUS registration. To start with, we trained two separate fully convolutional neural networks (CNNs) to perform a pixel-wise prediction for MRI and TRUS prostate segmentation. Then, to provide the initialization of the registration, a 2D CNN was used to register MRI-TRUS prostate images using an affine registration. After that, a 3D UNET-like network was applied for nonrigid registration. For both the affine and nonrigid registration, pairs of MRI-TRUS labels were concatenated and fed into the neural networks for training. Due to the unavailability of ground-truth voxel-level correspondences and the lack of accurate intensity-based image similarity measures, we propose to use prostate label-derived volume overlaps and surface agreements as an optimization objective function for weakly supervised network training. Specifically, we proposed a hybrid loss function that integrated a Dice loss, a surface-based loss, and a bending energy regularization loss for the nonrigid registration. The Dice and surface-based losses were used to encourage the alignment of the prostate label between the MRI and the TRUS. The bending energy regularization loss was used to achieve a smooth deformation field. Thirty-six sets of patient data were used to test our registration method. The image registration results showed that the deformed MR image aligned well with the TRUS image, as judged by corresponding cysts and calcifications in the prostate. The quantitative results showed that our method produced a mean target registration error (TRE) of 2.53 ± 1.39 mm and a mean Dice loss of 0.91 ± 0.02. The mean surface distance (MSD) and Hausdorff distance (HD) between the registered MR prostate shape and TRUS prostate shape were 0.88 and 4.41 mm, respectively. This work presents a deep learning-based, weakly supervised network for accurate MRI-TRUS image registration. Our proposed method has achieved promising registration performance in terms of Dice loss, TRE, MSD, and HD.

Automated MicroCT-based bone and articular cartilage analysis using iterative shape averaging and atlas-based registration.

Micro-computed tomography (μCT) and contrast-enhanced μCT are important tools for preclinical analysis of bone and articular cartilage (AC). Quantitative data from these modalities is highly dependent on the accuracy of tissue segmentations, which are often obtained via time-consuming manual contouring and are prone to inter- and intra-observer variability. Automated segmentation strategies could mitigate these issues, but few such approaches have been described in the context of μCT. Here, we validated a fully-automated strategy for bone and AC segmentation based on registration of an average tissue atlas. Femora from healthy and arthritic rats underwent μCT scanning, and epiphyseal trabecular bone and AC volumes were manually contoured by an expert. Average tissue atlases composed of 1, 3, 5, 10 and 20 pre-contoured training images (n=10 atlases/group) were generated using iterative shape averaging and registered onto unknown images via affine and non-rigid registration. Atlas-based and expert-defined volumes for bone and AC were compared in terms of shape-based similarity metrics, as well as morphometric and densitometric parameters. Our results demonstrate that atlas-based registrations were capable of highly accurate and consistent segmentation. Atlases built from as few as 3 training images had no incidence of mal-registration and exhibited improved incidence of accurate registration, and higher sensitivity and specificity compared to atlases built from only one training image. Atlas-based segmentation of bone and AC from μCT images is a robust and accurate alternative to manual tissue segmentation, enabling faster, more consistent segmentation of pre-clinical datasets.

Airborne high-resolution image motion target detection combined with hyperspectral features

Aiming at moving object detection in Airborne Hyperspectral Remote Sensing Images under complex background, this paper proposes a background removal method based on Mixture Gauss Model. Firstly, in order to eliminate the influence of the dithering of airborne platform on remote sensing image, this paper uses affine transform-based image registration algorithm to achieve the image registration of 3D data. Then, this paper selects specific spectral images, establishes the Mixture Gauss background model, and subtracts the background to achieve the extraction of moving objects. The experimental results show that the method can extract moving objects in Airborne Hyperspectral Remote Sensing Images effectively.