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Motion compensated image averaging

A moving and averaging technology, applied in the field of image averaging, can solve problems such as low SNR and difficult to analyze images

Inactive Publication Date: 2010-06-02
AGENCY FOR SCI TECH & RES
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the SNR of GFAP-GFP images of murine retina is often very low, and it is difficult to analyze these images using conventional techniques

Method used

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  • Motion compensated image averaging
  • Motion compensated image averaging
  • Motion compensated image averaging

Examples

Experimental program
Comparison scheme
Effect test

example

[0142] Using the computer 200, an exemplary image frame is processed according to process S100.

[0143] The processing used to process these image frames includes algorithms utilizing the following pseudocode example:

[0144] =======================================

[0145] %% Begin Pseudocode

[0146] Loop from i=1 to N

[0147] {execute subprocess S102 to generate filtered frame I f i from raw frame I i

[0148] execute subprocess S104 to generate deconvolved frame J i from filtered

[0149] frame I i

[0150] execute subprocess S160 to identify landmark points in J i

[0151] If i > 1

[0152] {execute S162 to correlate landmark points in J i and J i-1

[0153] execute S108 to align I f i with I a i-1 , generating aligned frame I a i

[0154] execute S110 to correct alignment of I a i to I ref i-1 , generating

[0155] corrected frame I n i and reference frame I i ref

[...

example I

[0164] Figure 3A A sample original frame of a GFAP-GFP image of a mouse retina taken in vivo and processed in this example is shown in .

[0165] Figure 3B shows the deconvolved frame resulting from the filtered frame, itself obtained from Figure 3A generated by the original frame. It can be seen that in Figure 3B In , the base features in the image are enhanced without significant distortion. This result demonstrates that, in this case, deconvolution of the filtered frames facilitates accurate assessment of the basal fluorescence signal in the filtered frames.

[0166] Figure 3C shows just by applying a 9×9 median filter from Figure 3B The control frame generated by the filtered frame of . and Figure 3B In contrast, the basal signal was less enhanced. Figure 3C Image artifacts are visible in , but in Figure 3B There is no such image artifact in .

[0167] Figure 3D Control frames generated by simple averaging are shown.

[0168] Figure 4A , 4B , 4C a...

example II

[0170] Raw image frames were acquired from the retinas of two different mice with FVB / N lesions using the Heidelberg Retina Angiograph (HRA2) system. A blue laser (488nm) was used to excite the transgenic GFAP-GFP expression and the blocking filter was set at 500nm. The field of view was initially set to 30°, but the image shown here is of a more localized area around the optic nerve head, as this is the area of ​​interest. All retinal images have a lateral resolution of 10 μm. Raw retinal images were collected over two weeks at specific times (different days). At the beginning of the experiment, ie, on day 0 immediately after retinal imaging, control mice were injected with saline and treated mice received intraperitoneal injection of kainic acid. A sequence of raw images is taken each day, and as detailed in the example above, the sequence of raw images is averaged according to process S100 to obtain the final image for that day. The parameters used for this averaging pro...

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Abstract

A method is provided for averaging a sequence of image frames. A noise-reducing filter is applied to the image frames to generate filtered frames. A deconvolution filter is applied to the filtered frames to generate corresponding deconvolved frames. The filtered frames are transformed by an affine transformation to align them, generating aligned frames. The aligned frames are motion corrected by non-linear transformation based on intensity rank matching, generating a sequence of motion-corrected frames. The motion-corrected frames are averaged to generate a resultant frame.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to US Provisional Application 60 / 924,162, filed May 2, 2007, the contents of which are incorporated herein by reference. technical field [0003] The present invention relates to image averaging and, in particular, to averaging over sequences of image frames requiring noise filtering and motion correction. Background technique [0004] Often it is necessary or desirable to average multiple images taken from the same object to obtain an averaged image with improved quality (eg with reduced noise). Conventional techniques for averaging image frames do not provide good or satisfactory results in certain situations, eg, when the signal-to-noise ratio (SNR) is very low and object motion is imaged while taking different image frames. For example, intravital fluorescence images of the murine retina can be obtained based on a technique using a green fluorescent protein (GFP) transgenic mouse m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50
CPCG06T2207/30004G06T2207/10016G06T2207/10064G06T5/001G06T5/20G06T2207/20016G06T5/003G06T5/002G06T5/50G06T2207/20036G06T5/73G06T5/70
Inventor L·卓S·库马尔G·何
Owner AGENCY FOR SCI TECH & RES
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