Image Processing with. ImageJ. Biology. Imaging

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1 Image Processing with ImageJ

2 1. Spatial filters Outlines background correction image denoising edges detection 2. Fourier domain filtering correction of periodic artefacts 3. Binary operations masks morphological operators 4. Particles analysis 5. ImageJ plugins plugins installation some examples

3 ImageJ - Spatial filters Spatial filtering: convolution of your image (or stack of images) with a 2D (or 3D) mask kernel

4 ImageJ - Spatial filters ImageJ implements a variety of low-pass and high-pass spatial filters Convolution with specified kernel. Smoothing filter, convolution with Gaussian kernel. Subtracts to the image a blurred version of the image itself. High-pass filter. Replace each pixel with: -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.

5 ImageJ - Spatial filters ImageJ implements a variety of low-pass and high-pass spatial filters 3X3 mean filter Sobel filter for edge detection. 3X3 difference filter. Exalts local intensity peaks

6 ImageJ - Spatial filters Background correction Original image Process > Filter > Gaussian Blur (sigma = 12 pixels) Note: the wider the kernel size, the bigger the effect of the filtering operation. Result of pixel-by-pixel subtraction. Note: in Process > Image Calculator select 32-bit result

7 ImageJ - Spatial filters Image denoising Original image Median filter result (3 pixels radius) Thresholding on the original image: segmentation is not possible Thresholding on the filtered image: correct segmentation

8 ImageJ - Spatial filters Edge detection Original image Result of Process > Find Edges operation Note: edge detection filters are not sensible to background

9 ImageJ Fourier domain filtering Fourier transform: a signal can be re-written as the sum of sinusoids with different frequency, amplitudes and phases 1D case First 5 harmonics (sinusoidal components) of a square wave. Sum of the first five harmonics compared to the square wave. 2D case Two images with pure horizontal and pure vertical component only. Fourier transforms of the two images

10 ImageJ Fourier domain filtering A 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).

11 ImageJ Fourier domain filtering Correction for periodic artefacts EM image, periodic artefact in vertical direction I 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.

12 ImageJ Fourier domain filtering Correction for periodic artefacts EM image, periodic artefact in vertical direction Result of the Fourier domain filtering Filter mask for Fourier domain filtering. This mask filter away the two peaks on the vertical axis which corresponds to the image artefact.

13 ImageJ Binary operations A typical format for images segmentation results is the binary image, a blackand-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.

14 NOTE: ImageJ Binary operations Removes pixels from the edges of objects, considering a 3X3 neighbourhood. Adds pixels from the edges of objects, considering a 3X3 neighbourhood. Open = Erode, then Dilate This operation smooths objects and remove isolated pixels. Close = Dilate, then Erode This operation smooths objects and fills in small holes. 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.

15 ImageJ Binary operations Example: fluorescence quantification in embryo cortex 1 Embryo segmentation by thresholding in the red channel Original image 2 Erosion to get a shrunk version of the binary mask Cortex selection Subtraction of the shrunk version of the embryo mask from the mask itself to get the cortex mask. 3

16 ImageJ Binary operations Example: cells segmentation using watershed transformation Watershed on the thresholded image. The watershed transformation allows to separate touching objects. Original image, DAPI staining. Nuclei segmentation through image thresholding. It is not possible to separate touching nuclei!

17 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.

18 ImageJ Particles analysis The 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.

19 ImageJ Plugins The 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.

20 ImageJ Plugins Some examples Color Segmentation ( ) 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

21 ImageJ Plugins Colocalization: JACoP ( ) 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 fluorogram apply more complex analysis methods, such as Costes automatic threshold, Costes randomization and objects based methods.

22 ImageJ Plugins NeuronJ ( ) NeuronJ is an ImageJ plugin to facilitate the tracing and quantification of elongated structures in twodimensional (2D) images (8-bit gray-scale and indexed color), in particular neurites in fluorescence microscopy images NeuronJ output: filaments statistics Fluorescent microscopy, collagen fibres tracing Fluorescent microscopy, neurites tracing

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