Question

So what I want to do is write an application that, at least in the future, could be ported to mobile platforms(such as android) that can scan an image of a protein gel and return data such as the number of bands(ie weights) in a column, relative concentration(thickness of the band), and the weights of each in each column.

For those who aren’t familiar, mixtures of denatured proteins(basically, molecules made completed straight) are loaded into each column, and with the use of electricity the proteins are pulled through a gel(because the proteins are polar molecules). The end columns of each side of this image http://i52.tinypic.com/205cyrl.gif are where you place a mixture of proteins of known weights(so if you have 4 different weights, the band on top is the largest weight, and the weight/size of the protein decreases the further it travels down). Is something like this possible to analyze using OpenCV? The given image is a really clean looking gel, they can often get really messy(see google images). I figured if I allowed a user to enter the number of columns, which columns contain known weight markers and their actual weights, as well as provide an adjustable rectangle to size around the edges of the gel, that maybe it would be possible to scan and extract data from the images of these gels? I skimmed through a textbook on OpenCV but I didn’t see any obvious and reliable way I could approach this. Any ideas? Maybe a different library would be better suited?

Answer 1

I believe you can do this using OpenCV

My approach would be a color based separation. And then counting the separate different components.

In big steps your app would do the following steps:

1. Load the image, rotate it scale manually through the GUI of your app, to match your needs

2. Create a second grayscale image in which each pixel contains a value between [0,255], that represents how good the color of the original point matches the target color (in the case of this image the shade of blue)
   In one of my experiments I’ve used the concept of fuzzy sets and alpha cuts to extract objects of a certain color. The triangular membership function gave me pretty good results. This simply meant that I’ve defined triangular functions for all three color channels RGB, and summed their result for each color given as input. If the values of the color were close to the centers of the triangles, then I had a strong similarity. Plus, by controlling the width of the triangles you can define the tolerance of the matches. (another option would be to use trapezoidal membership functions)

3. At this point you have a grayscale image, where the background (gel) is black and the proteins are gray/white. If you wish to clear up some noise use the morphological operators (page 127) erode and dilate (cvErode and cvDelate in openCV).

4. After it, can use this great openCV based blob extraction library to extract the bounding boxes of the remaining gray areas – representing the proteins

5. Having all the coordinates of the bounding boxes you can apply your own algorithms, to extract whatever data you wish

In my opinion OpenCV gives you all the necesarry tools. However a fully automated solution might be hard to obtain. But I’m sure you can easily build a GUI where you can set the parameters of the operators you apply during the above described steps

As for Android: I didn’t develop for mobile platforms, but I know that you can create C++ apps for these devices – have read several questions regarding iPhone & openCV -, so I think that your app would be portable, or at least the image processing part of it (the GUI might be too platform specific).