Download the open source software Fiji from http://fiji.sc/wiki/index.php/Fiji It’s essentially the same as ImageJ, but comes with many pre-installed plugins and can update itself automatically.

Introduction

The description below pertains to material fibres, the principle is however applicable to anything which resembles such an arrangement. The software plugin used was designed for bone analysis, so don’t let software descriptors limit their usefulness to you.

Spatial Calibration

Open the original image and spatially calibrate it before continuing. Do this by using the line tool to draw along a known distance on the image, in this case the scale bar. Then go to the menu Analyse / Set Scale and enter the known distance in the appropriate field. Change the units as appropriate and the image is calibrated. Areas, lengths and volumes are now calculated in these units instead of pixels.

Making a Binary Image

A binary image is one consisting of only two pixel values, usually black (0) and white (1). The most subjective step in this process is deciding which parts of the original image will contribute to the binary image. As the analysis will be done on the binary image, it is very important to judge this step carefully.

In this case the image had the bottom region cropped off to remove it from the analysis.

To crop an image, use the rectangle selection tool from the main menu and draw around the region you wish to keep. Go to Image / Crop to make it happen.

To threshold the image go to Image / Adjust / Threshold and adjust the sliders so the red colour selects the regions you want to analyse. This is the step where informed subjectivity is vital.

Quantifying Fibre Thickness Using Fiji

Andrew McNaughton - 1 - http://occm.otago.ac.nz /resources

In this example, the image was converted to an 8 bit image (256 grey scale) and pixels with values between 42 and 255 selected, as shown in red.

Tp produce the binary image, as based upon this intensity range, press ‘Apply’ in the dialogue box. Choosing a different intensity range will produce an entirely different result, even though it comes from the same original image. Always refer to the original image to check you are not deviating too far from what is really there. There’s a fine line between image analysis and inadvertently ‘making stuff up’.

The binary image consists of only black or white pixels, the white regions are what will be counted as your sample, black pixels are background.

Thickness Map

The Fiji plugin BoneJ was designed to measure the degree of porosity in bone, the image shown here is not bone, but the program doesn’t care. The image e x h i b i t s m a n y o f t h e characteristics of bone: curving highlights, separated by black voids. BoneJ can the re fo re measure the thickness of its highlights and spaces, irrespective of its origins.

Quantifying Fibre Thickness Using Fiji

Andrew McNaughton - 2 - http://occm.otago.ac.nz /resources

With the binary image selected, go to Plugins / BoneJ / Thickness. The dialogue box shown below will appear.

Select Thickness and Graphic Result by default, if you want to also quantify the spaces between the fibres (ie. the black regions) tick the Spacing option too. This is useful if you want an estimate of the porosity of your sample.

The result is a coloured thickness map, each colour corresponding to a thickness range. To see the value of each colour, move the cursor over the image and watch the value in the bottom of the main menu. Normally this would show pixel intensity values, but in this image it represents thickness.

The results table above shows the mean, standard deviation and maximum thickness of the f i b r e s . T b s t a n d s f o r ‘trabecular’, a form of porous bone. Ignore this label if your sample is not bone.

In this example results table the TbSp columns refer to a similar image of the spaces between the fibres (because the ‘Space’ option was selected). Analyse / Tools / Calibration Bar adds a colour coded scale to the image.

Notes

If you compare the binary image to the original, it is apparent some fibres have merged into single (thicker) fibres. This is due to the thresholding not being able to discriminate sufficiently between fibres in some cases. All manner of methods can be used to overcome this to a greater or lesser degree. All have inherent pitfalls and all should be regarded with suspicion until proven reliable.

Quantifying Fibre Thickness Using Fiji

Andrew McNaughton - 3 - http://occm.otago.ac.nz /resources