Blobs detection using machine learning?

Question

I have a large stack of images showing a bar with some dark blobs, whose position changes with time (see Figure, b). To detect the blobs I am now using an intensity threshold (c in Figure, where all intensity values below the threshold are set to 1) and then I search for blobs in the binary image using the Matlab code below. As you see the binary image is quite noisy, complicating the blobs detection process. Do you have any suggestion on how to improve the shape detection, maybe including some machine learning algorithm? Thanks!

Code:

se = strel('disk',1);
se_1 = strel('disk',3);
pw2 = imclose(IM,se);
pw3 = imopen(pw2,se_1);
pw4 = imfill(pw3, 'holes');

% Consider only the blobs with more than threshold pixels
[L,num] = bwlabel(pw4);
counts = sum(bsxfun(@eq,L(:),1:num));
number_valid_counts = length(find(counts>threshold));

enter image description here

so basically, you just want to extract the blob from figure B? or the blob(from figure b) mius the bar(in figure c) ? — Gary Tsui
– Gary Tsui, Commented Sep 24, 2014 at 9:01

Dib · Accepted Answer · 2014-09-22 04:47:47Z

1

This might help.

Extract texture features of the boundary of the blobs you want to extract. This can be done using Local binary patterns. There are many other texture features, you can get a detailed survey here.
Then use them to train a binary classifier.

answered Sep 22, 2014 at 4:47

Dib

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Comments

Ahmad Hassanat · Accepted Answer · 2014-09-22 10:24:58Z

1

It seems that the data come like pulses in the lower side of the image, I suggest to get some images and to slice vertical lines of the pixels perpendicular to the pulse direction, each time you take a line of values, little bit above and lower the pulse, the strip width is one pixel, and its height is little bit larger than the pulse image to take some of the light values lower and above the pulse, you may start from pixel 420-490, each time you save 70 grey values, those will form the feature vector, take also lines from the non blob areas to save for class 2, do this on several images and lines from each image.

now you get your training data, you may use any machine learning algorithm to train the computer for pulses and non pulses,

in the test step, you scan the image reading each time 70 pixels vertically and test them against the trained model, create a new black image if they belong to class "bolob" draw white vertical line starting from little below the tested pixel, else draw nothing on the output image.

at the end of scanning the image: check if there is an isolated white line you may delete considering it as false accepted . if you find a dark line within a group of white line, then convert it to white, considering false rejection.

you may use my classifier: https://www.researchgate.net/publication/265168466_Solving_the_Problem_of_the_K_Parameter_in_the_KNN_Classifier_Using_an_Ensemble_Learning_Approach

if you decide I will send you coed to do it. the distance metric is a problem, because the values varies between 0 and 255, so the light values will dominate the distance, to solve this problem you may use Hassanat distance metric at : https://www.researchgate.net/publication/264995324_Dimensionality_Invariant_Similarity_Measure

because it is invariant to scale in data, as each feature output a value between 0 and 1 no more, thus the highest values will not dominate the final distance.

Good luck

answered Sep 22, 2014 at 10:24

Ahmad Hassanat

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1 Comment

kkuilla Over a year ago

Welcome to StackOverflow. It's great that you would like to contribute. However, your answer doesn't really answer the question. You say that "you may use any machine learning algorithm" which isn't really helpful. Also, if you know something that could work (which could in this instance be your own algorithm), include a minimal example of how this would work. Remember to include input and output images so that other can re-produce your result. Posting a link to your own paper doesn't help future readers as that link may become invalid.

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