scholarly journals A Hybrid Landmark and Contour-Matching Image Registration Model

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Mia Mojica ◽  
Mehran Ebrahimi
Keyword(s):  
Image Registration ◽  
Human Error ◽  
Edge Information ◽  
Contour Matching ◽  
Straightforward Application ◽  
Proposed Model ◽  
Localization Errors ◽  
Image Contour ◽  
Contour Information ◽  
Image Pairs

In this manuscript, we propose a novel hybrid Landmark and Contour-Matching (LCM) image registration model to align image pairs. The proposed model uses image contour information to supplement missing edge information in between exact landmarks. We demonstrate that the model circumvent the drawbacks associated with an straightforward application of the Thin Plate Spline (TPS) registration technique.The proposed model provides higher post-registration Dice similarity between the reference and registered template images by improving the image overlap away from major landmarks and visually reduces the appearance of the ''unnatural bending'' typically present in TPS-registered images. We also show that naively increasing the number of landmarks in a TPS model does not always guarantee an accurate registration result. We indicate how the proposed model using even less number of exact landmarks along with additional approximate contour information provided suitable results, as opposed to the TPS model. Lastly, the proposed model produces physically relevant registration results with improved Dice similarity indices even when landmark localization errors are present in data.Overall, the proposed Landmark and Contour-Matching (LCM) model increases the flexibility of the TPS approach especially when only a few landmarks can be defined, when defining too many landmarks leads to high oscillations in the registration transformations, or when the identification of exact landmarks is susceptible to human error.

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

Reducing Shadow Effects on the Co-Registration of Aerial Image Pairs

2020 ◽  
Vol 86 (3) ◽  
pp. 177-186
Author(s):  
Matthew Plummer ◽  
Douglas Stow ◽  
Emanuel Storey ◽  
Lloyd Coulter ◽  
Nicholas Zamora ◽  
...  
Keyword(s):  
Image Registration ◽  
Image Data ◽  
Aerial Image ◽  
Registration Accuracy ◽  
Illumination Variation ◽  
Scale Invariant ◽  
Match Point ◽  
Linear Correction ◽  
Multi Temporal ◽  
Image Pairs

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (<small>SIFT</small>) and Random Sample Consensus (<small>RANSAC</small>) Alignment (<small>SARA</small>). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.

scholarly journals The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

2022 ◽  
Vol 25 (1) ◽  
pp. 273
Author(s):  
Ahmed Shihab Ahmed ◽  
Hussein Ali Salah
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Registration ◽  
Brain Diseases ◽  
Reference Image ◽  
City Hospital ◽  
Brain Image ◽  
Mri Brain ◽  
Proposed Model ◽  
Mri Image

The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

scholarly journals Rapid Multimodal Image Registration Based on the Local Edge Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dong Zhao
Keyword(s):  
Image Registration ◽  
Edge Information ◽  
Multimodal Image Registration ◽  
Image Blurring ◽  
Self Similar ◽  
Similar Factor ◽  
Local Edge ◽  
Multimodality Registration ◽  
Edge Histogram ◽  
Multimodal Images

Due to significant differences in imaging mechanisms between multimodal images, registration methods have difficulty in achieving the ideal effect in terms of time consumption and matching precision. Therefore, this paper puts forward a rapid and robust method for multimodal image registration by exploiting local edge information. The method is based on the framework of SURF and can simultaneously achieve real time and accuracy. Due to the unpredictability of multimodal images’ textures, the local edge descriptor is built based on the edge histogram of neighborhood around keypoints. Moreover, in order to increase the robustness of the whole algorithm and maintain the SURF’s fast characteristic, saliency assessment of keypoints and the concept of self-similar factor are presented and introduced. Experimental results show that the proposed method achieves higher precision and consumes less time than other multimodality registration methods. In addition, the robustness and stability of the method are also demonstrated in the presence of image blurring, rotation, noise, and luminance variations.

SU-E-J-82: Ground-Truth Tests of Deformable Image Registration Using Matched PET-CT Image Pairs

2013 ◽  
Vol 40 (6Part7) ◽  
pp. 169-169
Author(s):  
J Lamb ◽  
S Jani ◽  
B White ◽  
D Thomas ◽  
S Gaudio ◽  
...  
Keyword(s):  
Image Registration ◽  
Ground Truth ◽  
Deformable Image Registration ◽  
Ct Image ◽  
Pet Ct ◽  
Image Pairs

scholarly journals Target Image Mask Correction Based on Skeleton Divergence

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 251
Author(s):  
Yaming Wang ◽  
Zhengheng Xu ◽  
Wenqing Huang ◽  
Yonghua Han ◽  
Mingfeng Jiang
Keyword(s):  
Point Cloud ◽  
High Performance ◽  
Image Features ◽  
Edge Information ◽  
Target Region ◽  
Proposed Model ◽  
University Of Munich ◽  
Traditional Approaches ◽  
Simultaneous Source ◽  
Image Mask

Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms.

Microscopic Image Stitching Based on Regional Frog Leaping Search Algorithm and Image Contour Matching

2019 ◽  
Vol 56 (15) ◽  
pp. 151002
Author(s):  
颜振翔 Zhenxiang Yan ◽  
王寒迎 Hanying Wang ◽  
石齐双 Qishuang Shi ◽  
莫艳红 Yanhong Mo ◽  
杨辉华 Huihua Yang
Keyword(s):  
Search Algorithm ◽  
Image Stitching ◽  
Microscopic Image ◽  
Contour Matching ◽  
Image Contour

scholarly journals Structural Information Reconstruction of Distorted Underwater Images Using Image Registration

2020 ◽  
Vol 10 (16) ◽  
pp. 5670
Author(s):  
Tao Sun ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Image Registration ◽  
Reference Frame ◽  
Structural Information ◽  
Motion Blur ◽  
Edge Information ◽  
Registration Algorithm ◽  
Underwater Image ◽  
Image Registration Algorithm ◽  
Surface Fluctuations ◽  
Restoration Method

Imaging through wavy air-water surface suffers from uneven geometric distortions and motion blur due to surface fluctuations. Structural information of distorted underwater images is needed to correct this in some cases, such as submarine cable inspecting. This paper presents a new structural information restoration method for underwater image sequences using an image registration algorithm. At first, to give higher priority to structural edge information, a reference frame is reconstructed from the sequence frames by a combination of lucky patches chosen and the guided filter. Then an iterative robust registration algorithm is applied to remove the severe distortions by registering frames against the reference frame, and the registration is guided towards the sharper boundary to ensure the integrity of edges. The experiment results show that the method exhibits improvement in sharpness and contrast, especially in some structural information such as text. Furthermore, the proposed edge-first registration strategy has faster iteration velocity and convergence speed compared with other registration strategies.

scholarly journals Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

2019 ◽  
Vol 7 (6) ◽  
pp. 178
Author(s):  
Armagan Elibol ◽  
Nak Young Chong
Keyword(s):  
Image Registration ◽  
Optical Mapping ◽  
Probabilistic Methods ◽  
Computational Time ◽  
Intermediate Step ◽  
Geometrical Properties ◽  
Geometric Invariants ◽  
Salient Points ◽  
Feature Based ◽  
Image Pairs

Image registration is one of the most fundamental and widely used tools in optical mapping applications. It is mostly achieved by extracting and matching salient points (features) described by vectors (feature descriptors) from images. While matching the descriptors, mismatches (outliers) do appear. Probabilistic methods are then applied to remove outliers and to find the transformation (motion) between images. These methods work in an iterative manner. In this paper, an efficient way of integrating geometric invariants into feature-based image registration is presented aiming at improving the performance of image registration in terms of both computational time and accuracy. To do so, geometrical properties that are invariant to coordinate transforms are studied. This would be beneficial to all methods that use image registration as an intermediate step. Experimental results are presented using both semi-synthetically generated data and real image pairs from underwater environments.

scholarly journals Feature-Based Retinal Image Registration Using D-Saddle Feature

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Roziana Ramli ◽  
Mohd Yamani Idna Idris ◽  
Khairunnisa Hasikin ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Retinal Diseases ◽  
Feature Points ◽  
Registration Accuracy ◽  
Gaussian Pyramid ◽  
Difference Of Gaussian ◽  
Lower Success Rate ◽  
Feature Based ◽  
Image Pairs

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

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