An efficient algorithm for measurement and correction of chromatic aberrations in fluorescence microscopy

Kozubek, Michal; Matula, Petr

doi:10.1046/j.1365-2818.2000.00754.x

Cited by 60 publications

(55 citation statements)

References 14 publications

(24 reference statements)

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“…Chromatic aberration has previously been measured by Kuzubek and Matula (2000) using florescent dyed beads. These are then imaged in 3D, when their centroids are estimated.…”

Section: Measuring Lateral Chromatic Aberrationsmentioning

confidence: 99%

“…The models used are again surface approximations, which are far from optimal solutions, especially since only a limited number of control points are available to estimate the surface parameters. An algorithm for the measurement and compensation of both LCA and ACA has been proposed for fluorescence microscopy by Kuzubek and Matula (2000), and for electron microscopes by Freitag et al (2005). These techniques are not transferrable to images acquired with regular imaging systems.…”

Section: Introductionmentioning

confidence: 99%

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Calibration and removal of lateral chromatic aberration in images

Mallon

Whelan

2007

Pattern Recognition Letters

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This paper addresses the problem of compensating for lateral chromatic aberration in digital images through colour plane realignment. Two main contributions are made: the derivation of a model for lateral chromatic aberration in images, and the subsequent calibration of this model from a single view of a chess pattern. These advances lead to a practical and accurate alternative for the compensation of lateral chromatic aberrations. Experimental results validate the proposed models and calibration algorithm. The effects of colour channel correlations resulting from the camera colour filter array interpolation is examined and found to have a negligible magnitude relative to the chromatic aberration. Results with real data show how the removal of lateral chromatic aberration significantly improves the colour quality of the image.

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“…Chromatic aberration has previously been measured by Kuzubek and Matula (2000) using florescent dyed beads. These are then imaged in 3D, when their centroids are estimated.…”

Section: Measuring Lateral Chromatic Aberrationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration and removal of lateral chromatic aberration in images

Mallon

Whelan

2007

Pattern Recognition Letters

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“…Such approach may not be practical since it requires taking three pictures for each channel under different camera settings. Kozubek and Matula [4] show how to correct for both types of aberrations in the environment of fluorescent microscopy; however, this method cannot be applied for camera imaging systems.…”

Section: Introductionmentioning

confidence: 99%

Precise Correction of Lateral Chromatic Aberration in Images

Rudakova

Monasse

2014

Image and Video Technology

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Abstract. This paper addresses the problem of lateral chromatic aberration correction in images through color planes warping. We aim at high precision (largely sub-pixel) realignment of color channels. This is achieved thanks to two ingredients: high precision keypoint detection, which in our case are disk centers, and more general correction model than what is commonly used in the literature, radial polynomial. Our setup is quite easy to implement, requiring a pattern of black disks on white paper and a single snapshot. We measure the errors in terms of geometry and of color and compare our method to three different software programs. Quantitative results on real images show that our method allows alignment of average 0.05 pixel of color channels and a residual color error divided by a factor 3 to 6.

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“…It is expected that a more general assumption on the nonrigid transformations between multiple channels could lead to further improvement. This may involve the use of fluorescent beads (18) and design of different computational methods. Registering the other color images to the DAPI image may be least optimal because the optical performance of the microscope system is most different in the ultraviolet/blue region where DAPI excites and emits.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, each object in the specimen also changes its off-axis position when imaged with different wavelengths, resulting in lateral chromatic aberration. Many studies conducted on this kind of error have demonstrated that even the best available objectives with high numerical aperture produce axial and lateral chromatic aberration (18). For M-FISH imaging instruments, the effect of chromatic aberration becomes even more severe.…”

Section: Sources Of Misalignmentmentioning

confidence: 99%

Normalization of multicolor fluorescence in situ hybridization (M‐FISH) images for improving color karyotyping

Wang

Castleman

2005

Cytometry Pt A

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Background: Multiplex or multicolor fluorescence in situ hybridization (M-FISH) is a recently developed cytogenetic technique for cancer diagnosis and research on genetic disorders. By simultaneously viewing the multiply labeled specimens in different color channels, M-FISH facilitates the detection of subtle chromosomal aberrations. The success of this technique largely depends on the accuracy of pixel classification (color karyotyping). Improvements in classifier performance would allow the elucidation of more complex and more subtle chromosomal rearrangements. Normalization of M-FISH images has a significant effect on the accuracy of classification. In particular, misalignment or misregistration across multiple channels seriously affects classification accuracy. Image normalization, including automated registration, must be done before pixel classification. Methods and Results:We studied several image normalization approaches that affect image classification. In particular, we developed an automated registration technique to correct misalignment across the different fluor images (caused by chromatic aberration and other factors). This new registration algorithm is based on wavelets and spline approximations that have computational advantages and improved accuracy. To evaluate the performance improvement brought about by these data normalization approaches, we used the downstream pixel classification accuracy as a measurement. A Bayesian classifier assumed that each of 24 chromosome classes had a normal probability distribution. The effects that this registration and other normalization steps have on subsequent classification accuracy were evaluated on a comprehensive M-FISH database established by Advanced Digital Imaging Research (http://www.adires.com/05/Project/ MFISH_DB/MFISH_DB.shtml). Conclusions: Pixel misclassification errors result from different factors. These include uneven hybridization, spectral overlap among fluors, and image misregistration. Effective preprocessing of M-FISH images can decrease the effects of those factors and thereby increase pixel classification accuracy. The data normalization steps described in this report, such as image registration and background flattening, can significantly improve subsequent classification accuracy. An improved classifier in turn would allow subtle DNA rearrangements to be identified in genetic diagnosis and cancer research. © 2005 Wiley-Liss, Inc.Key terms: classification; registration; translocation; wavelets; chromosome image analysis; fluorescence in situ hybridization; automated cytogeneticsThe progress of cytogenetics has become significantly faster since the discovery of chromosome banding and molecular fluorescence in situ hybridization (FISH) techniques (1-8). FISH-based methodologies greatly enhance the detection of genetic abnormalities in normal and cancer cells. Multiplex FISH (M-FISH) of human chromosomes is based on the simultaneous hybridization of a pool of 24 chromosome-specific probes (3). Figure 1 is an M-FISH image dataset that is ...

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An efficient algorithm for measurement and correction of chromatic aberrations in fluorescence microscopy

Cited by 60 publications

References 14 publications

Calibration and removal of lateral chromatic aberration in images

Calibration and removal of lateral chromatic aberration in images

Precise Correction of Lateral Chromatic Aberration in Images

Normalization of multicolor fluorescence in situ hybridization (M‐FISH) images for improving color karyotyping

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