Mutual information-based multimodal image registration using a novel joint histogram estimation

Lu, Xuesong; Zhang, Su; Su, He; Chen, Yazhu

doi:10.1016/j.compmedimag.2007.12.001

Cited by 77 publications

(56 citation statements)

References 15 publications

(22 reference statements)

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“…The RPCME algorithm is compared with the Mutual Information maximization algorithm (MI), having an important robustness to multi-sources images (Lu et al 2008).…”

Section: Methodsmentioning

confidence: 99%

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Robust Phase-Correlation Based Registration of Airborne Videos Using Motion Estimation

Morsier

Borgeaud

Küchler

et al. 2012

Lecture Notes in Geoinformation and Cartography

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This paper presents an algorithm for near-real time registration of airborne video sequences with reference images from a different sensor type. Phase-correlation using Fourier-Melin Invariant (FMI) descriptors allow to retrieve the rigid transformation parameters in a fast and non-iterative way. The robustness to multi-sources images is obtained by an enhanced image representation based on the gradient norm and by the extrapolation of registration parameters by motion estimation between frames. A phase-correlation score, indicator of the registration quality, is introduced to regulate between frame-toreference image registration and extrapolation from previous frames only. Our Robust Phase-Correlation based registration algorithm using Motion Estimation (RPCME) is compared with a Mutual Information (MI) algorithm for the registration of two different panchromatic airborne videos with Geoeye reference images. The RPCME algorithm registered most of the frames accurately, retrieving much better orientation than MI. It shows robustness and good accuracy to multisource images with the advantage of being a direct (non-iterative) method.

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“…The RPCME algorithm is compared with the Mutual Information maximization algorithm (MI), having an important robustness to multi-sources images (Lu et al 2008).…”

Section: Methodsmentioning

confidence: 99%

“…The registration is based on maximizing the mutual information between the image I 1 and the transformed image I 0 2 . The implementation has been realized using the HPV interpolation and the Powell's Direction set method, for more details see Lu et al (2008).…”

Section: Mutual Information Registrationmentioning

confidence: 99%

Robust Phase-Correlation Based Registration of Airborne Videos Using Motion Estimation

Morsier

Borgeaud

Küchler

et al. 2012

Lecture Notes in Geoinformation and Cartography

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show abstract

“…35 Image registration was performed on B-mode images (tissue image), and the resulting transformation parameters were applied to the contrast-specific images (microbubble only image). Rather than registering all frames sequentially 36 or arbitrarily choosing a single reference frame 37 as in previous reports, a unique image registration system was employed.…”

Section: Image Registrationmentioning

confidence: 99%

Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics

Kono

Barback

et al. 2012

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

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Contrast-enhanced ultrasound (CEUS) enables highly specific time-resolved imaging of vasculature by intravenous injection of $2 lm gas filled microbubbles. To develop a quantitative automated diagnosis of breast tumors with CEUS, breast tumors were induced in rats by administration of N-ethyl-N-nitrosourea. A bolus injection of microbubbles was administered and CEUS videos of each tumor were acquired for at least 3 min. The time-intensity curve of each pixel within a region of interest (ROI) was analyzed to measure kinetic parameters associated with the wash-in, peak enhancement, and wash-out phases of microbubble bolus injections since it was expected that the aberrant vascularity of malignant tumors will result in faster and more diverse perfusion kinetics versus those of benign lesions. Parameters were classified using linear discriminant analysis to differentiate between benign and malignant tumors and improve diagnostic accuracy. Preliminary results with a small dataset (10 tumors, 19 videos) show 100% accuracy with fivefold cross-validation testing using as few as two choice variables for training and validation. Several of the parameters which provided the best differentiation between malignant and benign tumors employed comparative analysis of all the pixels in the ROI including enhancement coverage, fractional enhancement coverage times, and the standard deviation of the envelope curve difference normalized to the mean of the peak frame. Analysis of combinations of five variables demonstrated that pixel-by-pixel analysis produced the most robust information for tumor diagnostics and achieved 5 times greater separation of benign and malignant cases than ROI-based analysis.

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“…Joint histogram has been used in image processing and computer vision, including image comparison (Pass and Zabih, 1999), change detection (Kita, 2006), image registration (Lu et al, 2008), and estimation of camera response function (Kim and Pollefeys, 2008). With any two geo-registered images taken for the same scene, a robust mapping function can be defined in the joint histogram plot so that the relationship between the intensity values of the two images can be established with a fitted regression model (Mann, 2000).…”

Section: Introductionmentioning

confidence: 99%

Reduction of Striping Noise in Overlapping Lidar Intensity Data by Radiometric Normalization

Yan

Shaker

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:To serve seamless mapping, airborne LiDAR data are usually collected with multiple parallel strips with one or two cross strip(s). Nevertheless, the overlapping regions of LiDAR data strips are usually found with unbalanced intensity values, resulting in the appearance of stripping noise. Despite that physical intensity correction methods are recently proposed, some of the system and environmental parameters are assumed as constant or not disclosed, leading to such an intensity discrepancy. This paper presents a new normalization technique to adjust the radiometric misalignment found in the overlapping LiDAR data strips. The normalization technique is built upon a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The method was tested on Teledyne Optech's Gemini dataset (at 1064 nm wavelength), where the LiDAR intensity data were first radiometrically corrected based on the radar (range) equation. Five land cover features were selected to evaluate the coefficient of variation (cv) of the intensity values before and after implementing the proposed method. Reduction of cv was found by 19% to 59% in the Gemini dataset, where the striping noise was significantly reduced in the radiometrically corrected and normalized intensity data. The Gemini dataset was also used to conduct land cover classification, and the overall accuracy yielded a notable improvement of 9% to 18%. As a result, LiDAR intensity data should be pre-processed with radiometric correction and normalization prior to any data manipulation.

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Mutual information-based multimodal image registration using a novel joint histogram estimation

Cited by 77 publications

References 15 publications

Robust Phase-Correlation Based Registration of Airborne Videos Using Motion Estimation

Robust Phase-Correlation Based Registration of Airborne Videos Using Motion Estimation

Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics

Reduction of Striping Noise in Overlapping Lidar Intensity Data by Radiometric Normalization

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