Image Menu

Image processing functions

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Contents

Mode

Allows to change mode of the shown dataset, the following options are available:

Adjust Display/Image

Starts a dialog to adjust display settings or resample image intensities. See more in the Adjust display window section

Color Channels

Perform some actions with color channels of the image

A brief demonstration is available in the following video:
https://youtu.be/gT-c8TiLcuY

It is also possible to do color channel operations from the Colors table in the View settings panel.

Contrast

Adjust contrast of the dataset. For the linear contrast stretching it is recommended to use Image Adjustment dialog available via the Display button in the View Settings panel.

A tutorial on image normalization is available in the following video:
https://youtu.be/MmBmdGtuUdM

Invert image

Invert image intensities, shortcut Ctrl+i.

A brief demonstration is available in the following video:
https://youtu.be/1DG2w5XYA18

Image filter

Open a dialog with various image filters arranged into 4 categories:

For further details, please refer to the Image Filters help page.

Tools for images --> Content-aware fill

Content-aware fill

A brief demonstration is available in the following video:
https://youtu.be/H_TVvgA_br4

inpaintCoherent

only for Matlab R2019a and newer
Restore specific image regions using coherence transport based image inpainting.

The areas for the content-aware fill can be specified using the Mask or Selection layers of MIB.

  • Radius - the inpainting radius denotes the radius of the circular neighborhood region centered on the pixel to be inpainted
  • Smoothing Factor - smoothing factor is used to compute the scales of the Gaussian filters while estimating the coherence direction
References
[1] F. Bornemann and T. M?rz. "Fast Image Inpainting Based on Coherence Transport." Journal of Mathematical Imaging and Vision. Vol. 28, 2007, pp.259?278.

inpaintExemplar

only for Matlab R2019b and newer
Fill image regions using exemplar-based image inpainting

The areas for the content-aware fill can be specified using the Mask or Selection layers of MIB.

  • PatchSize - size of the image patch, for example, '9' or a pair of numbers as '9,9', where the image patches are the image regions considered for patch matching and inpainting
  • FillOrder - the filling order denotes the priority function to be used for calculating the patch priority. The patch priority value specifies the order of filling of the image patches in target regions
References
[1] A. Criminisi, P. Perez and K. Toyama. "Region Filling and Object Removal by Exemplar-Based Image Inpainting." IEEE Trans. on Image Processing. Vol. 13, No. 9, 2004, pp. 1200?1212.

Tools for images --> Debris removal

Debris removal

A brief demonstration is available in the following video:
https://youtu.be/iM2nHBxTjRw

Automatically or manually restore areas of volumetric datasets that are corrupted with debris. The areas can either be automatically detected or manually selected into the Mask or Selection layers

  • Automatic detection - automatic detection of debris areas:
    • the tool takes a difference between the current and the previous and the following slices;
    • the difference is summarized and thresholded using the Intensity threshold parameter
    • the thresholded area that are smaller than the Object size threshold parameter are removed from the consideration
    • all other areas are subjected to a series of erosion and dilation morphological operations with the strel size defined in the Strel size field
    • finally the detected area is replaced with an average image generated using the previous and the following slice

  • Masked areas - the debris removal operation is performed on the specified in the Mask layer areas
  • Selected areas - the debris removal operation is performed on the specified in the Selection layer areas

Tools for images --> Image arithmetics

Image arithmetics


Use Matlab syntax to apply custom arithmetic expression to Image, Model, Mask or Selection layers, see more in
a brief video and examples below.
For MIB 2.60 and newer https://youtu.be/sDwvnJGLi8Q
For MIB 2.52 and older https://youtu.be/-puVxiNYGsI
    Parameters and options:

  • Coding:,
    • I, I1, I2 ... -> use I letter to identify the image layer; a number indicates MIB container that has the image, without the number the currently selected dataset is taken
    • O, O1, O2 ... -> use O letter to identify the model layer
    • M, M1, M2 ... -> use M letter to identify the mask layer
    • S, S1, S2 ... -> use S letter to identify the selection layer
  • Input variables: list here all datasets that are used in the expression
  • Output variable: specify here the output variable
  • Previous expresson, a list of previous successfully executed expressions. Selection of any expression from this list will populate the expression edit box
  • Expression, an expression with arithmetic operation to perform, see below for some examples

    Examples:
  • I = I * 2, increase intensity of all pixels of the current image in 2 times
  • I2 = I2 + 100, increase intensity of all pixels in image 2 by 100
  • I1 = I1 + I2, add image from container 2 to an image in container 1 and return result back to container 1
  • I3 = I3 + mean(I3(:)), add mean value of image 3 to image 3
  • I1 = I1 - min(I1(:)), decrease intensity of pixels in the image 1 by the min value of the dataset
  • I(:,:,2,:) = I(:,:,2,:)*1.4, increase image intensity of the second color channel in 1.4 times
  • I(I==0) = I(I==0)+100, increase image intensity of the black areas by 100 intensity counts
  • disp(sum(abs(single(I1(:))-single(I2(:))))), find the difference between two images loaded to container 1 and 2 and print it to console; or use msgbox(num2str()) instead of disp to show result as a message box
  • M2 = M1, copy mask layer from image 1 to image 2
  • for z=1:size(I, 4)
    slice = I(:,:,2,z);
    mask = M(:,:,z);
    slice(mask==1) = 0;
    I(:,:,2,z) = slice;
    end
    - replace intensity of the second color channel in the masked area to 0

Tools for images --> Intensity projection

Intensity projection


A brief demonstration is available in the following video:
https://youtu.be/hwFpS_3eP9U


Select image frame


A brief demonstration is available in the following video:
https://youtu.be/sWjipmeU5eA
Detects the frame (which is an area of the same intensity that touches edge of the image) of the image. The detected area can be assinged to the Selection or Mask layers, or that area can be replaced with another color for the Image layer.

Morphological operations

This section contains number of morphological operations that can be applied to images. The processed image may be also added or subtracted from the existing image (see the settings in the Additional action to the result panel).

A brief demonstration is available in the following video:
https://youtu.be/itbVLFm0FKQ

List of available morphological operations

Intensity profile

Generate an intensity profile of the image data. The profiles can be obtained in two modes:

For intensity profiles it is recommended to use the Measure length tool.

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