R: Image Analysis using EBImage
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Currently, I am taking Statistics for Image Analysis on my masteral, and have been exploring this topic in R. One package that has the capability in this field is the EBImage from Bioconductor, which will be showcased in this post.
For those using Ubuntu, you may likely to encounter this error:
It has something to do with the
Yes, this is the photo that we are going to use for our analysis. Needless to say, that’s me and my friends. In the proceeding section we will do image manipulation and other processing.
There are two sections (Summary and array of the pixels) in the above output, with the following entries for the first section:
The second section is the obtained values from mapping pixels in the image to the real line between 0 and 1 (inclusive). Both extremes of this interval [0, 1], are black and white colors, respectively. Hence, pixels with values closer to any of these end points are expected to be darker or lighter, respectively. And because pixels are contained in a large array, then we can do all matrix manipulations available in R for processing.
Installation
For those using Ubuntu, you may likely to encounter this error:
It has something to do with the
tiff.h
C header file, but it’s not that serious since mytechscribblings has an effective solution for this, do check that out.Importing Data
To import a raw image, consider the following codes:Output of display(Image) . 
Image Properties
So what do we get from our raw image? To answer that, simply runprint(Image)
. This will return the properties of the image, including the array of pixel values. With these information, we apply mathematical and statistical operations to do enhancement on the image.There are two sections (Summary and array of the pixels) in the above output, with the following entries for the first section:
Code  Value  Description 

Table 1: Information from 1st section of print(Image) .  
colormode  Color  The type (Color/Grayscale) of the color of the image. 
storage.mode  double  Type of values in the array. 
dim  1984 1488 3  Dimension of the array, (x, y, z). 
nb.total.frames:  3  Number of channels in each pixel, z entry in dim . 
nb.render.frames  1  Number of channels rendered. 
The second section is the obtained values from mapping pixels in the image to the real line between 0 and 1 (inclusive). Both extremes of this interval [0, 1], are black and white colors, respectively. Hence, pixels with values closer to any of these end points are expected to be darker or lighter, respectively. And because pixels are contained in a large array, then we can do all matrix manipulations available in R for processing.
Adjusting Brightness
It is better to start with the basic first, one of which is the brightness. As discussed above, brightness can be manipulated using+
or 
:Lighter  Darker  

Table 2: Adjusting Brightness.  


Adjusting Contrast
Contrast can be manipulated using multiplication operator(*
): Low  High  

Table 3: Adjusting Contrast.  


Gamma Correction
Gamma correction is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems, defined by the following powerlaw expression: begin{equation}nonumber V_{mathrm{out}} = AV_{mathrm{in}}^{gamma} end{equation} where $A$ is a constant and the input and output values are nonnegative real values; in the common case of $A = 1$, inputs and outputs are typically in the range 01. A gamma value $gamma< 1$ is sometimes called an encoding gamma (Wikipedia, Ref. 1).$gamma = 2$  $gamma = 0.7$  

Table 4: Adjusting Gamma Correction.  


Cropping
Slicing array of pixels, simply mean cropping the image.Output of the above code. 
Spatial Transformation
Spatial manipulation like rotate (rotate
), flip (flip
), and translate (translate
) are also available in the package. Check this out, Color Management
Since the array of pixels has three axes in its dimension, for example in our case is 1984 x 1488 x 3. The third axis is the slot for the three channels: Red, Green and Blue, or RGB. Hence, transforming thecolor.mode
from Color
to Grayscale
, implies disjoining the three channels from single rendered frame (three channels for each pixel) to three separate array of pixels for red, green, and blue frames.Original  Red Channel  

Table 5: Color Mode Transformation.  
Green Channel  Blue Channel  
To revert the color mode, simply run
Filtering
In this section, we will do smoothing/blurring using lowpass filter, and edgedetection using highpass filter. In addition, we will also investigate median filter to remove noise.LowPass (Blur) 

Table 6: Image Filtering. 
High Pass 
Original  Filtered  

Table 7: Median Filter.  


For comparison, I run median filter on firstneighborhood in Mathematica, and I got this
Clearly, Mathematica has better enhancement than R for this particular filter. But R has a good foundation already, as we witness with EBImage. There are still lots of interesting functions in the said package, that is worth exploring, I suggest you check that out.
For the meantime, we will stop here, but hoping we can play more on this topic in the succeeding post.
References
 Gamma Correction. Wikipedia. Retrieved August 31, 2014.
 Gregoire Pau, Oleg Sklyar, Wolfgang Huber (2014). Introduction to EBImage, an image processing and analysis toolkit for R.
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