Matlab video processing of heart beating. code supplemented

Question

I'm trying to write a code The helps me in my biology work. Concept of code is to analyze a video file of contracting cells in a tissue

Example 1

Example 2: youtube.com/watch?v=uG_WOdGw6Rk

And plot out the following:

1. Count of beats per min.
2. Strenght of Beat
3. Regularity of beating

And so i wrote a Matlab code that would loop through a video and compare each frame vs the one that follow it, and see if there was any changes in frames and plot these changes on a curve.

Example of My code Results

Core of Current code i wrote:

for i=2:totalframes
        compared=read(vidObj,i);
        ref=rgb2gray(compared);%% convert to gray
        level=graythresh(ref);%% calculate threshold
        compared=im2bw(compared,level);%% convert to binary        
        differ=sum(sum(imabsdiff(vid,compared))); %% get sum of difference between 2 frames
        if (differ ~=0) && (any(amp==differ)==0) %%0 is = no change happened so i dont wana record that !
            amp(end+1)=differ;  % save difference to array amp wi
            time(end+1)=i/framerate; %save to time array with sec's, used another array so i can filter both later.
            vid=compared; %% save current frame as refrence to compare the next frame against.
        end
end
figure,plot(amp,time);

=====================

So thats my code, but is there a way i can improve it so i can get better results ?

because i get fealing that imabsdiff is not exactly what i should use because my video contain alot of noise and that affect my results alot, and i think all my amp data is actually faked !

Also i actually can only extract beating rate out of this, by counting peaks, but how can i improve my code to be able to get all required data out of it ??

thanks also really appreciate your help, this is a small portion of code, if u need more info please let me know. thanks

Answer 1

What are the structures we see in the video? For example what is the big dark object in the lower part of the image? This object would be relativly easy to track, but would data from this object be relevant to get data about cell contraction?

Is this image from a light microscop? At what magnification? What is the scale? From the video it looks like there are several motions and regions of motion. So should you focus on a smaller or larger area to get your measurments? Per cell contraction or region contraction? From experience I know that changing what you do at the microscope might be much better then complex image processing ;)

I had sucsess with Gunn and Nixons Dual Snake for a similar problem: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.6831

I placed the first aproximation in the first frame by hand and used the segmentation result as starting curv for the next frame and so on. My implementation for this is from 2000 and I only have it on paper, but if you find Gunn and Nixons paper interesting I can probably find my code and scan it.

@Matt suggested smoothing and edge detection to improve your results. This is good advise. You can combine smoothing, thresholding and edge detection in one function call, the Canny edge detector.Then you can dialate the edges to get greater overlap between frames. Little overlap will probably mean a big movement between frames. You can use this the same way as before to find the beat. You can now make a second pass and add all the dialated edge images related to one beat. This should give you an idea about the area traced out by the cells as they move trough a contraction. Maybe this can be used as a useful measure for contraction of a large cluster of cells.

I don't have access to Matlab and the Image Processing Toolbox now, so I can't give you tested code. Here are some hints: http://www.mathworks.se/help/toolbox/images/ref/edge.html , http://www.mathworks.se/help/toolbox/images/ref/imdilate.html and http://www.mathworks.se/help/toolbox/images/ref/imadd.html.

Answer 2

You say you are trying to write a "simple code", but this is not really a simple problem. If you want to measure the motion accuratly, you should use an optical flow algorithm or look at the deformation field from a registration algorithm.

EDIT: As Matt is saying, and as we see from your curve, your method is suitable for extracting the number of beats and the regularity. To accuratly find the strength of the beats however, you need to calculate the movement of the cells (more movement = stronger beat). Unfortuantly, this is not straight forwards, and that is why I gave you links to two algorithms that can calculate the movement for you.

Answer 3

A few fairly simple things to try that might help:

• I would look in detail at what your thresholding is doing, and whether that's really what you want to do. I don't know what graythresh does exactly, but it's possible it's lumping different features that you would want to distinguish into the same pixel values. Have you tried plotting the differences between images without thresholding? Or you could threshold into multiple classes, rather than just black and white.
• If noise is the main problem, you could try smoothing the images before taking the difference, so that differences in noise would be evened out but differences in large features, caused by motion, would still be there.
• You could try edge-detecting your images before taking the difference.

As a previous answerer mentioned, you could also look into motion-tracking and registration algorithms, which would estimate the actual motion between each image, rather than just telling you whether the images are different or not. I think this is a decent summary on Wikipedia: http://en.wikipedia.org/wiki/Video_tracking. But they can be rather complicated.

I think if all you need is to find the time and period of contractions, though, then you wouldn't necessarily need to do a detailed motion tracking or deformable registration between images. All you need to know is when they change significantly. (The "strength" of a contraction is another matter, to define that rigorously you probably would need to know the actual motion going on.)