Pre-segmentation influence on the efficiency of multi-temporal image registration

The analysis of the segmentation effect of various background areas in the image on the quality of registration of multi-temporal bridge structure images using stochasticalgorithms is carried out. The influence of the parameters of bridge structures image registration, such background structures as cloudiness and ground cover, on the operatingrange is analyzed under the assumption that the similarity model describes mutual volumetric images

A Hybrid Particle Swarm Optimization with Affine Transformation Approach for Cloud Free Multi-Temporal Image Registration

An image registration is the major part of the image categorization and cluster formation in multi temporal image processing. The images are affected by the different factors such as cloud shadow, water level, building shadows etc. In this paper, an enhanced registration process and the cloud removal technique is proposed for image enhancement. The Daemons, Combined Registration and Segmentation (CRS) approach, Markov Random Field (MRF) approach and Mutual Information (MI) based approaches results in more computational complexity, minimum edge preservation measure (QAB/F) and Mutual Information in image registration. In order to maximize the quality of edge preservation measure and MI with minimum computational time, this paper proposes Particle Swarm Optimization (PSO) based affine transformation technique. The proposed techniques measure and compare the computation time against the number of pixels of an image with the existing methods of CRS and MRF for the number of images. The comparative analysis of QAB/F and MI with the traditional methods of Clock Point -Least Square (CP-LS) and the Multi-Focus Image Fusion (MFIF) and Discrete Wavelet Transform (DWT) is presented to confirm the effective performance. The simulation results of the proposed transformation for registration process confirms the effective image registration in the multi-temporal image processing

Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images

Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recently, deep learning-based image registration methods have been widely explored, and demonstrated to enable fast and accurate image registration in a variety of applications. However, most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain, which is not always true, especially when registering images whose fields of view contain discontinuities at tissue/organ boundaries. In such scenarios, enforcing smooth, globally continuous deformation fields leads to incorrect/implausible registration results. We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields, leading to more accurate and realistic registration results. The proposed method leverages the complementary nature of image segmentation and registration and enables joint segmentation and pair-wise registration of images. A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images, while a discontinuity-preserving registration strategy is employed in the registration component of the network to ensure plausibility in the estimated deformation fields at tissue/organ interfaces. We evaluate our method on the task of intra-subject spatio-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences, and demonstrate that our method achieves significant improvements over the state-of-the-art for medical image registration, and produces high-quality segmentation masks for the regions of interest

Gates to Gregg High Voltage Transmission Line Study

The usefulness of LANDSAT data in the planning of transmission line routes was assessed. LANDSAT digital data and image processing techniques, specifically a multi-date supervised classification aproach, were used to develop a land cover map for an agricultural area near Fresno, California. Twenty-six land cover classes were identified, of which twenty classes were agricultural crops. High classification accuracies (greater than 80%) were attained for several classes, including cotton, grain, and vineyards. The primary products generated were 1:24,000, 1:100,000 and 1:250,000 scale maps of the classification and acreage summaries for all land cover classes within four alternate transmission line routes

Image registration and visualization of in situ gene expression images.

In the age of high-throughput molecular biology techniques, scientists have incorporated the methodology of in-situ hybridization to map spatial patterns of gene expression. In order to compare expression patterns within a common tissue structure, these images need to be registered or organized into a common coordinate system for alignment to a reference or atlas images. We use three different image registration methodologies (manual; correlation based; mutual information based) to determine the common coordinate system for the reference and in-situ hybridization images. All three methodologies are incorporated into a Matlab tool to visualize the results in a user friendly way and save them for future work. Our results suggest that the user-defined landmark method is best when considering images from different modalities; automated landmark detection is best when the images are expected to have a high degree of consistency; and the mutual information methodology is useful when the images are from the same modality

Experimental Examination of Similarity Measures and Preprocessing Methods Used for Image Registration

An experimental analysis of a number of image registration techniques is described. The objective is to provide a better understanding on a comparative basis of some of the methods of registering imagery that have been proposed. In particular, this study encompasses the choice of a similarity measure and the effects of preprocessing the imagery prior to the registration

Image based cardiac acceleration map using statistical shape and 3D+t myocardial tracking models; in-vitro study on heart phantom

International audienceIt has been demonstrated that the acceleration signal has potential to monitor heart function and adaptively optimize Cardiac Resynchronization Therapy (CRT) systems. In this paper, we propose a non-invasive method for computing myocardial acceleration from 3D echocardiographic sequences. Displacement of the myocardium was estimated using a two-step approach: (1) 3D automatic segmentation of the myocardium at end-diastole using 3D Active Shape Models (ASM); (2) propagation of this segmentation along the sequence using non-rigid 3D+t image registration (temporal diffeomorphic free-form-deformation, TDFFD). Acceleration was obtained locally at each point of the myocardium from local displacement. The framework has been tested on images from a realistic physical heart phantom (DHP-01, Shelley Medical Imaging Technologies, London, ON, CA) in which the displacement of some control regions was known. Good correlation has been demonstrated between the estimated displacement function from the algorithms and the phantom setup. Due to the limited temporal resolution, the acceleration signals are sparse and highly noisy. The study suggests a non-invasive technique to measure the cardiac acceleration that may be used to improve the monitoring of cardiac mechanics and optimization of CRT