Biology Imaging

Image Processing withImageJ

Biology Imaging

Outlines1.

Spatial filters

background correctionimage denoisingedges detection

2.

Fourier domain filteringcorrection of periodic artefacts

3.

Binary operationsmasksmorphological operators

5.

ImageJ pluginsplugins installationsome examples

4.

Particles analysis

Biology Imaging

ImageJ -

Spatial filtersSpatial filtering:

convolution

of your image (or stack of images) with a 2D (or 3D) mask kernel

Biology Imaging

ImageJ -

Spatial filtersImageJ implements a variety of low-pass and high-pass spatial filters

Smoothing filter, convolution with Gaussian kernel .

-the median of the neighbouring pixels. Nonlinear filter, good for salt-and-pepper noise reduction.-

the average value of the neighbouring pixels. Smoothing filter.-

the smallest value of the neighbouring pixels values (grayscale erosion).-

the biggest value of the neighbouring pixels values (grayscale dilation).-

the variance of the neighbouring pixels values. Indicator of image textures; highlight edges.

Replace each pixel with:

Convolution with specified kernel.

Subtracts to the image a blurred version of the image itself. High-pass

filter.

Biology Imaging

ImageJ -

Spatial filtersImageJ implements a variety of low-pass and high-pass spatial filters

-1 -1 -1

-1 12 -1

-1 -1 -11 2 1

0 0 0

-1 -2 -1

1 0 -1

2 0 -2

1 0 -1

3X3 difference filter. Exalts local intensity peaks.

Sobel filter for edge detection.

3X3 mean filter

Biology Imaging

ImageJ -

Spatial filtersBackground correction

Original imageProcess > Filter > Gaussian

Blur (sigma = 12 pixels)Result of pixel-by-pixel

subtraction.Note: in Process > Image

Calculator select 32-bit resultNote: the wider the kernel size, the bigger the effect of the filtering operation.

Biology Imaging

ImageJ -

Spatial filtersImage denoising

Original image

Thresholding on the original image:

segmentation is not possible

Median filter result (3 pixels radius)

Thresholding on the filtered image:

correct segmentation

Biology Imaging

ImageJ -

Spatial filtersEdge detection

Original image Result of Process > Find Edges operation

Note: edge detection filters are not sensible to background

Biology Imaging

ImageJ –

Fourier domain filteringFourier transform: a signal can be re-written as the sum of sinusoids with

different frequency, amplitudes and phases

First 5 harmonics (sinusoidal components) of a square wave.

Sum of the first five harmonics compared to the square wave.

Two images with pure horizontal and pure vertical component only.

Fourier transforms of the two images

1D case

2D case

DFT:

Biology Imaging

ImageJ –

Fourier domain filteringA large variety of low-pass, high-pass or band-pass filters are implemented in the Fourier domain.

ImageJ allows you to compute Fast Fourier Transform and inverse transform of your data and to define masks for filtering in the Fourier domain.

Compute Fast Fourier Transform.

Compute inverse Fast Fourier Transform.

Does filtering in the Fourier domain using a filter mask provided by the user. The filter mask (binary image) should represent the bands of the Fourier transform of the image which will be passed or filtered away. The filter mask should be

symmetric with respect to the centre (image

continuous component).

Biology Imaging

ImageJ –

Fourier domain filteringCorrection for periodic artefacts

EM image, periodic

artefact in vertical

direction

FFT. The Fourier transform of an image is symmetric respect to the centre.

The centre of the FFT displays the image continuous component (frequency = 0, red arrow). Close to the centre you can read the low-frequency components values, far from

the centre the high-frequency components.Typically the most of the frequency components of the image are concentrated in the low

frequency region.In this case we can observe two peaks on the vertical axis (green arrows), most likely

corresponding to the periodic artefact in the vertical direction.

ℑ

Biology Imaging

ImageJ –

Fourier domain filteringCorrection for periodic artefacts

EM image, periodic artefact in vertical direction

Filter mask for Fourier domain filtering.This mask filter away the two peaks on

the vertical axis which corresponds to

the image artefact.

Result of the Fourier domain

filtering

Biology Imaging

ImageJ –

Binary operationsA typical format for images segmentation results is the binary image, a black-

and-white image which represents the structures of interest which have been segmented in contrast to the background. Binary masks are often improved using morphological operations. The two most basic operations in mathematical morphology are erosion and dilation. The entity of the erosion or dilation effect depends on the dimension of the structuring element which is used.

EROSION DILATION

The erosion operation removes pixels from the edges of objects.

The dilation operation adds pixels to the edges of objects.

Biology Imaging

ImageJ –

Binary operationsRemoves pixels from the edges of objects,

considering a 3X3 neighbourhood.

Adds pixels from the edges of objects, considering a 3X3 neighbourhood.

Open = Erode, then DilateThis operation smooths objects

and remove isolated pixels.

Close = Dilate, then ErodeThis operation smooths objects

and fills in small holes.

NOTE:All the morphological operations are accomplished sliding a structuring element

(basically a mask) through the images and performing logical operations between the image pixels which are selected by the structuring element. The bigger the structuring element is, the heavier the effect of the morphological operation on the image. To have the possibility of controlling the structuring element size, use for example the ‘Gray Morphology’ plugin of ImageJ Fiji.

Biology Imaging

ImageJ –

Binary operationsExample: fluorescence quantification in embryo cortex

Original image

Embryo segmentation by thresholding in the red channel

Erosion to get a shrunk version of the binary mask

Subtraction of the shrunk version of the embryo mask from the mask itself to get

the cortex mask.

1

2

3

Cortex selection

Biology Imaging

ImageJ –

Binary operationsExample: cells segmentation using watershed

transformation

Original image,

DAPI staining.

Nuclei segmentation through image thresholding. It is not possible to separate touching nuclei!

Watershed on the thresholded image. The watershed transformation allows to separate touching objects.

Biology Imaging

ImageJ –

Binary operations

How does watershed work?

The watershed transform allow to cut apart particles that touch.

The Watershed ImageJ command first calculates the Euclidian distance map and finds the ultimate eroded

points. It then dilates each of the local maxima of the Euclidean distance map (ultimate erode points) as far as possible, until the edge of the particle is reached.

Biology Imaging

ImageJ –

Particles

analysisThe Analyze Particles

function of ImageJ is a useful tool to evaluate the number

of cells in you image and to carry out morphological analysis.

‘Analyze Particles’ counts and measures objects in binary or thresholded images.

In the ‘Set Measurements’

menu you can set the characteristics of the objects you want to be visualized (ex.: area, perimeter, mean gray value)

You can filter objects by dimension and by circularity.

Biology Imaging

ImageJ –

PluginsThe functionalities of ImageJ can be extended by writing additional programs in Java language

(the so-called plugins).

A wide collection of ImageJ plugins

is available on the web.

You can easily find out solutions for general or really specific image processing problems.

Installation

Download the .jar or .class file in the ImageJ/plugins folder (note that by default ImageJ is installed in the Program Files folder).

Then start ImageJ: you will find the new plugin command under the Plugins menu.

http://rsbweb.nih.gov/ij/plugins/

Biology Imaging

ImageJ –

PluginsSome examples

Color Segmentation

( http://bigwww.epfl.ch/sage/soft/colorsegmentation/

)

If you do histological classical staining, it is most likely that you need to characterize different kinds of tissue in your image.This Color Segmentation plugins for ImageJ allows the user to segment the image in different colors

regions. It implements two dierent algorithms for pixels clustering based on the distribution of the pixels in the color space and on some spatial constraints. The number of clusters is given by the user and is equal to the number of regions in the image to be distinguished, including the background.

Color Segmentation

Biology Imaging

ImageJ –

PluginsColocalization: JACoP

(

http://rsbweb.nih.gov/ij/plugins/track/jacop.html

)

JACoP is a toolbox for subcellular colocalization analysis under ImageJ. It integrates global statistics methods and object-based approach. Particularly, the JACoP plugin can:

compute commonly used colocalization indicators, such as Paerson ‘s coefficient and Manders’ coefficient

generate a fluorogramapply more complex analysis methods, such as Costes’ automatic threshold, Costes’

randomization and objects based methods.

Biology Imaging

ImageJ –

PluginsNeuronJ

( http://www.imagescience.org/meijering/software/neuronj/

)

NeuronJ is an ImageJ plugin to facilitate the tracing and quantification of elongated structures in two-

dimensional (2D) images (8-bit gray-scale and indexed color), in particular neurites in fluorescence

microscopy images

Fluorescent microscopy, collagen fibres tracing

Fluorescent microscopy, neurites tracing

NeuronJ output: filaments statistics