Measuring weighted mean length from an electrophoresis gel image - matlab

Question

Background:
My question relates to extracting feature from an electrophoresis gel (see below). In this gel, DNA is loaded from the top and allowed to migrate under a voltage gradient. The gel has sieves so smaller molecules migrate further than longer molecules resulting in the separation of DNA by length. So higher up the molecule, the longer it is.
Question:
In this image there are 9 lanes each with separate source of DNA. I am interested in measuring the mean location (value on the y axis) of each lane.
I am really new to image processing, but I do know MATLAB and I can get by with R with some difficulty. I would really appreciate it if someone can show me how to go about finding the mean of each lane.

Answer 1

Here's my try. It requires that the gels are nice (i.e. straight lanes and the gel should not be rotated), but should otherwise work fairly generically. Note that there are two image-size-dependent parameters that will need to be adjusted to make this work on images of different size.
%# first size-dependent parameter: should be about 1/4th-1/5th
%# of the lane width in pixels.
minFilterWidth = 10;
%# second size-dependent parameter for filtering the
%# lane profiles
gaussWidth = 5;
%# read the image, normalize to 0...1
img = imread('http://img823.imageshack.us/img823/588/gele.png');
img = rgb2gray(img);
img = double(img)/255;
%# Otsu thresholding to (roughly) find lanes
thMsk = img < graythresh(img);
%# count the mask-pixels in each columns. Due to
%# lane separation, there will be fewer pixels
%# between lanes
cts = sum(thMsk,1);
%# widen the local minima, so that we get a nice
%# separation between lanes
ctsEroded = imerode(cts,ones(1,minFilterWidth));
%# use imregionalmin to identify the separation
%# between lanes. Invert to get a positive mask
laneMsk = ~repmat(imregionalmin(ctsEroded),size(img,1),1);
Image with lanes that will be used for analysis
%# for each lane, create an averaged profile
lblMsk = bwlabel(laneMsk);
nLanes = max(lblMsk(:));
profiles = zeros(size(img,1),nLanes);
midLane = zeros(1,nLanes);
for i = 1:nLanes
profiles(:,i) = mean(img.*(lblMsk==i),2);
midLane(:,i) = mean(find(lblMsk(1,:)==i));
end
%# Gauss-filter the profiles (each column is an
%# averaged intensity profile
G = exp(-(-gaussWidth*5:gaussWidth*5).^2/(2*gaussWidth^2));
G=G./sum(G);
profiles = imfilter(profiles,G','replicate'); %'
%# find the minima
[~,idx] = min(profiles,[],1);
%# plot
figure,imshow(img,[])
hold on, plot(midLane,idx,'.r')

Answer 2

Here's my stab at a simple template for an interactive way to do this:
% Load image
img = imread('gel.png');
img = rgb2gray(img);
% Identify lanes
imshow(img)
[x,y] = ginput;
% Invert image
img = max(img(:)) - img;
% Subtract background
[xn,yn] = ginput(1);
noise = img((yn-2):(yn+2), (xn-2):(xn+2));
noise = mean(noise(:));
img = img - noise;
% Calculate means
means = (1:size(img,1)) * double(img(:,round(x))) ./ sum(double(img(:,round(x))), 1);
% Plot
hold on
plot(x, means, 'r.')

Answer 3

The first thing to do to is convert your RGB image to grayscale:
gr = rgb2gray(imread('gelk.png'));
Then, take a look at the image intensity histogram using imhist. Notice anything funny about it? Use imcontrast(imshow(gr)) to pull up the contrast adjustment tool. I found that eliminating the weird stuff after the major intensity peak was beneficial.
The image processing task itself can be divided into several steps.
Separate each lane
Identify ('segment') the band in each lane
Calculate the location of the bands
Step 1 can be done "by hand," if the lane widths are guaranteed. If not, the line detection offered by the Hough transform is probably the way to go. The documentation on the Image Processing Toolbox has a really nice tutorial on this topic. My code recapitulates that tutorial with better parameters for your image. I only spent a few minutes with them, I'm sure you can improve the results by tuning the parameters further.
Step 2 can be done in a few ways. The easiest technique to use is Otsu's method for thresholding grayscale images. This method works by determining a threshold that minimizes the intra-class variance, or, equivalently, maximizes the inter-class variance. Otsu's method is present in MATLAB as the graythresh function. If Otsu's method isn't working well you can try multi-level Otsu or a number of other histogram based threshold determination methods.
Step 3 can be done as you suggest, by calculating the mean y value of the segmented band pixels. This is what my code does, though I've restricted the check to just the center column of each lane, in case the separation was off. I'm worried that the result may not be as good as calculating the band centroid and using its location.
Here is my solution:
function [locations, lanesBW, lanes, cols] = segmentGel(gr)
%%# Detect lane boundaries
unsharp = fspecial('unsharp'); %# Sharpening filter
I = imfilter(gr,unsharp); %# Apply filter
bw = edge(I,'canny',[0.01 0.3],0.5); %# Canny edges with parameters
[H,T,R] = hough(bw); %# Hough transform of edges
P = houghpeaks(H,20,'threshold',ceil(0.5*max(H(:)))); %# Find peaks of Hough transform
lines = houghlines(bw,T,R,P,'FillGap',30,'MinLength',20); %# Use peaks to identify lines
%%# Plot detected lines above image, for quality control
max_len = 0;
imshow(I);
hold on;
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
%# Plot beginnings and ends of lines
plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');
%# Determine the endpoints of the longest line segment
len = norm(lines(k).point1 - lines(k).point2);
if ( len > max_len)
max_len = len;
end
end
hold off;
%%# Use first endpoint of each line to separate lanes
cols = zeros(length(lines),1);
for k = 1:length(lines)
cols(k) = lines(k).point1(1);
end
cols = sort(cols); %# The lines are in no particular order
lanes = cell(length(cols)-1,1);
for k = 2:length(cols)
lanes{k-1} = im2double( gr(:,cols(k-1):cols(k)) ); %# im2double for compatibility with greythresh
end
otsu = cellfun(#graythresh,lanes); %# Calculate threshold for each lane
lanesBW = cell(size(lanes));
for k = 1:length(lanes)
lanesBW{k} = lanes{k} < otsu(k); %# Apply thresholds
end
%%# Use segmented bands to determine migration distance
locations = zeros(size(lanesBW));
for k = 1:length(lanesBW)
width = size(lanesBW{k},2);
[y,~] = find(lanesBW{k}(:,round(width/2))); %# Only use center of lane
locations(k) = mean(y);
end
I suggest you carefully examine not only each output value, but the results from each step of the function, before using it for actual research purposes. In order to get really good results, you will have to read a bit about Hough transforms, Canny edge detection and Otsu's method, and then tune the parameters. You may also have to alter how the lanes are split; this code assumes that there will be lines detected on either side of the image.

Answer 4

Let me add another implementation similar in concept to that of #JohnColby's, only without the manual user-interaction:
%# read image
I = rgb2gray(imread('gele.png'));
%# middle position of each lane
%# (assuming lanes are somewhat evenly spread and of similar width)
x = linspace(1,size(I,2),10);
x = round( (x(1:end-1)+x(2:end))./2 );
%# compute the mean value across those columns
m = mean(I(:,x));
%# find the y-indices of the mean values
[~,idx] = min( bsxfun(#minus, double(I(:,x)), m) );
%# show the result
figure(1)
imshow(I, 'InitialMagnification',100, 'Border','tight')
hold on, plot(x, idx, ...
'Color','r', 'LineStyle','none', 'Marker','.', 'MarkerSize',10)
and applied on the smaller image: