A rapid and automatic image registration algorithm with subpixel accuracy

Althof, R.J.; Wind, Mark; Dobbins, James T.

doi:10.1109/42.585765

Cited by 110 publications

(51 citation statements)

References 7 publications

(10 reference statements)

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“…Therefore, in order to obtain the most accurate strain measurements, subpixel shifts in disk position must be measured. While many subpixel registration algorithms exist [17,18,19,20], several considerations must be taken into account when choosing which to use. Due to the incredibly large size of the datasets obtained, both in file size and number of disk positions to be measured, only the fastest algorithms are considered.…”

Section: Methods For Determining Diffraction Disk Positionsmentioning

confidence: 99%

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

et al. 2017

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Scanning nanobeam electron diffraction strain mapping is a technique by which the positions of diffracted disks sampled at the nanoscale over a crystalline sample can be used to reconstruct a strain map over a large area. However, it is important that the disk positions are measured accurately, as their positions relative to a reference are directly used to calculate strain. In this study, we compare several correlation methods using both simulated and experimental data in order to directly probe susceptibility to measurement error due to non-uniform diffracted disk illumination structure. We found that prefiltering the diffraction patterns with a Sobel filter before performing cross correlation or performing a square-root magnitude weighted phase correlation returned the best results when inner disk structure was present. We have tested these methods both on simulated datasets, and experimental data from unstrained silicon as well as a twin grain boundary in 304 stainless steel.

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Section: Methods For Determining Diffraction Disk Positionsmentioning

confidence: 99%

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

et al. 2017

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“…Many methods use different feature matching techniques like Area based and Feature based [1], [2]. In Area based methods, frames of predefined size or even entire images are used for the corresponding estimation [3], [4], [5]. Area based methods may be mutual information [6] oriented, correlation coefficient [7] oriented, Fourier transform [8], [9] oriented etc.…”

Section: Introductionmentioning

confidence: 99%

Statistical Regression based Rotation Estimation Technique of Color Image

Hazra¹,

Chowdhury²,

Dutta³

2014

IJCA

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ABSTRACT-This paper describes a rotational angle estimation of different color images. This estimation method is primarily based on weighted linear regression lines of the three color components of a color image as well as the influence of each component. Preservation of the chromatic information makes this method helpful to efficiently calculate the rotational angle between the referenced and sensed image pair. The experiments justify that the proposed method is robust ensuring its applicability to any kind of color images.

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“…Thus, correlation-like registration methods are preferably applied to such medical image (8). In previous approaches, people use windows with a predefined size or even entire images for the correspondence estimation (9,10).…”

Section: Introductionmentioning

confidence: 99%

Early lung cancer detection based on registered perfusion MRI

et al. 2006

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Abstract. Lung cancer remains the most common fatal malignancy in both men and women in the United States and elsewhere around the world among people who have been exposed passively or actively to tobacco smoke. Technological advances have produced imaging modalities that are proving to be useful for the early detection of lung cancer. In the usual modes, computed tomography (CT) and magnetic resonance imaging (MRI) can identify suspicious lesions, but further work is needed to detect cancer in its early stage. Tumor angiogenesis assessed by perfusion-sensitive MRI is a promising method for early and accurate identification of lung cancer that avoids patient stress and the potential progression to a less treatable stage inherent in serial imaging. However, compensating for the respiratory and anatomical structure motion is a challenge. In order to use perfusion MRI in a signal imaging session to define metabolic and vascular characteristics of tumors, we present a novel affine registration method for perfusion MRI that can register points of interest by tracking image intensity changes around the target point. The registration results are used to generate accurate time intensity curves (TIC) of different regions of interest (ROI). The patterns of different TICs are mapped on the cancer and other structures. The method is computationally efficient and performs well in our perfusion MRI sequence analysis.

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A rapid and automatic image registration algorithm with subpixel accuracy

Cited by 110 publications

References 7 publications

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

Statistical Regression based Rotation Estimation Technique of Color Image

Early lung cancer detection based on registered perfusion MRI

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