EBImage: Image processing package in

RCharles Howard

Portland R Users Group16-Sep-2015

EBImage• EBImage is an image processing and analysis toolbox for R.• Development of the package arose from the need for general purpose

tools for segmenting cells and extracting quantitative cellular descriptors• Package is hosted on Bioconductor (http://bioconductor.org/)

•

Read in image readImage(files, type, all = TRUE, ...)

Supports .jpg, .png, .tiff filesall if the file contains more than one

image read all?

Apply Filtering

Thresholding & Morphological Operations

Labeling, Extracting, Analyzing features

Basic Image Processing

Filtering - Basics• Define a structuring element• Pass structuring element over

each pixel • Structuring element alters each

pixel it touches.• Example: average the pixel

strength of neighboring pixels and assign that value to the center pixel

Filtering in EBImage• makeBrush: function for defining a structuring element• makeBrush(size, shape=c('box', 'disc', 'diamond', 'gaussian', 'line'), step=TRUE,

sigma=0.3, angle=45)• Size must be an odd number• Step: True binary pixel strengths/False grey scale pixel strengths• Sigma: applies to the ‘gaussian’ shape, defines standard deviation of the Gaussian.

• Applying the filter• img.br<-makeBrush(7,’gaussian’,sigma=10)• img.blur<-filter2(img,img.br) : fillter2() function for filtering

Gaussian Structuring Element (11X11 pixels)

2.27E-121 1.18E-99 9.12E-83 1.06E-70 1.83E-63 4.73E-61 1.83E-63 1.06E-70 9.12E-83 1.18E-99 2.27E-1211.18E-99 6.10E-78 4.73E-61 5.47E-49 9.48E-42 2.45E-39 9.48E-42 5.47E-49 4.73E-61 6.10E-78 1.18E-999.12E-83 4.73E-61 3.66E-44 4.24E-32 7.34E-25 1.90E-22 7.34E-25 4.24E-32 3.66E-44 4.73E-61 9.12E-831.06E-70 5.47E-49 4.24E-32 4.91E-20 8.50E-13 2.20E-10 8.50E-13 4.91E-20 4.24E-32 5.47E-49 1.06E-701.83E-63 9.48E-42 7.34E-25 8.50E-13 1.47E-05 0.003807 1.47E-05 8.50E-13 7.34E-25 9.48E-42 1.83E-634.73E-61 2.45E-39 1.90E-22 2.20E-10 0.003807 0.984714 0.003807 2.20E-10 1.90E-22 2.45E-39 4.73E-611.83E-63 9.48E-42 7.34E-25 8.50E-13 1.47E-05 0.003807 1.47E-05 8.50E-13 7.34E-25 9.48E-42 1.83E-631.06E-70 5.47E-49 4.24E-32 4.91E-20 8.50E-13 2.20E-10 8.50E-13 4.91E-20 4.24E-32 5.47E-49 1.06E-709.12E-83 4.73E-61 3.66E-44 4.24E-32 7.34E-25 1.90E-22 7.34E-25 4.24E-32 3.66E-44 4.73E-61 9.12E-831.18E-99 6.10E-78 4.73E-61 5.47E-49 9.48E-42 2.45E-39 9.48E-42 5.47E-49 4.73E-61 6.10E-78 1.18E-99

2.27E-121 1.18E-99 9.12E-83 1.06E-70 1.83E-63 4.73E-61 1.83E-63 1.06E-70 9.12E-83 1.18E-99 2.27E-121

Original Image

Image source: //upload.wikimedia.org/wikipedia/commons/a/a4/Misc_pollen.jpg

Original - blurred

> img.br<-makeBrush(7,shape='gaussian',sigma=10)> img.blur<-filter2(img,img.br)

Original – blurred/larger structure element

> filter.br<-makeBrush(15,shape='gaussian',sigma=10)> img.blur<-filter2(img,filter.br)

Original – blurred/larger structure element/doubled sigma

> filter.br<-makeBrush(15,shape='gaussian',sigma=20)> img.blur<-filter2(img,filter.br)

Thresholding & Morphological Operations• Thresholding: applies a structuring element to generate a binary image

which retains only those pixels which exceed a given threshold. Used to find edges in an image.• img.th<-thresh(img,w=9,h=9,offset=0.03)

• Morphological Operations: functions that dilate or erode thresholded features.• open.br<-makeBrush(7,shape="disc",step=TRUE)• img.th<-thresh(img,w=9,h=9,offset=0.03)• img.op<-opening(img.th,open.br)

• An erosion followed by a dilation is called an “opening”• A dilation followed by an erosion is called a “closing”

Thresholded Image

Thresholded image after an erosion followed by a dilation (“Opening”)

Thresholded image after a dilation followed by an erosion (“Closing”)

open.br<-makeBrush(7,shape="disc",step=TRUE)img.cl<-closing(img.th,open.br)

Feature extraction• Once a satisfactory threshold and erosion/dilation combination is arrived

at, features can be extracted for statistical purposes. The binary features must be labeled. The functions for labeling and computing features are:

imgcl.lab<-bwlabel(img.cl)ftrs<-computeFeatures(imgcl.lab,img)

• More than 20 feature parameters may be found. Basic features such as the center of mass of each feature, number of pixels per feature, number of perimeter pixels, etc.• Complex feature characteristics like Zernicke polynomial moments and

Haralick features can also be computed.

Feature Statistics ExamplesThis img.cl image has 1182 individual features

Feature Statistics Examples• What are the 0.4-0.5 eccentricity features?ecc.ftrs<-which(ftrs[,'x.a.m.eccentricity']>0.4 & ftrs[,'x.a.m.eccentricity']<=0.5)ecc.img<-Image(0,dim=dim(img))ecc.img[which(imgcl.lab %in% ecc.img)]<-img[which(imgcl.lab %in% ecc.img)]

Thanks!