I am microbiology student new to computer vision, so any help will be extremely appreciated.
This question involves microscope images that I am trying to analyze. The goal I am trying to accomplish is to count bacteria in an image but I need to pre-process the image first to enhance any bacteria that are not fluorescing very brightly. I have thought about using several different techniques like enhancing the contrast or sharpening the image but it isn't exactly what I need.
I want to reduce the noise(black spaces) to 0's on the RBG scale and enhance the green spaces. I originally was writing a for loop in OpenCV with threshold limits to change each pixel but I know that there is a better way.
Here is an example that I did in photo shop of the original image vs what I want.
Original Image and enhanced Image.
I need to learn to do this in a python environment so that I can automate this process. As I said I am new but I am familiar with python's OpenCV, mahotas, numpy etc. so I am not exactly attached to a particular package. I am also very new to these techniques so I am open to even if you just point me in the right direction.
Thanks!
You can have a look at histogram equalization. This would emphasize the green and reduce the black range. There is an OpenCV tutorial here. Afterwards you can experiment with different thresholding mechanisms that best yields the bacteria.
Use TensorFlow:
create your own dataset with images of bacteria and their positions stored in accompanying text files (the bigger the dataset the better).
Create a positive and negative set of images
update default TensorFlow example with your images
make sure you have a bunch of convolution layers.
train and test.
TensorFlow is perfect for such tasks and you don't need to worry about different intensity levels.
I initially tried histogram equalization but did not get the desired results. So I used adaptive threshold using the mean filter:
th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 3, 2)
Then I applied the median filter:
median = cv2.medianBlur(th, 5)
Finally I applied morphological closing with the ellipse kernel:
k1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
dilate = cv2.morphologyEx(median, cv2.MORPH_CLOSE, k1, 3)
THIS PAGE will help you modify this result however you want.
Related
I have an ML model that I am using to create a mask to separate background and object. The problem is that the model is not that accurate and we still get regions of background around the edges.
The background could be any color but it is not uniform as you can see in the image.
This is the model output.
I was wondering if there is a way I could apply masking only around the edges so it doesn't affect other parts of the object which have been extracted properly. Basically I only want to trim down these edges which contain the background so any solutions using python are appreciated.
I'm really sorry for not being at the liberty to share the code but I'm only looking for ideas that I can implement to solve this problem.
You can use binary erosion or dilation to "grow" the mask so that it covers the edge
https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.morphology.binary_dilation.html
https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.morphology.binary_erosion.html
as for "apply masking only around the edges" (this is not the same mask I was writing about above), you can flag pixels that are close to the edge by iteration over a mask and finding where there is a 0 that has a neighbouring 1 or vice versa.
I am working on breast region segmentation using Huang Thresholding. The original and result image is provided here:
As you can see the mask edges are not smooth enough but it's accepted in this sample. Next is another sample with the mask edges pretty jagged:
In the attached picture I already implemented some preprocessing to smoothing the edge by using close operation with the following codes (I tried it with the median filter but not much effect).
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))
closed = cv2.morphologyEx(canvas.copy(), cv2.MORPH_CLOSE, kernel, iterations=3)
I also tried the solution provided here:
but not satisfying enough for me in this case.
Can anybody help me or suggest me a method to smooth the edges of the mask? Here I have provided the mask images for sample1:
and for sample2:
.
FYI, I planned to bitwise_and the image with the mask so I can remove the background images. Background images in mammograms sometimes not really black background and contain too much noise which you don't want when enhancing the image after.
`
I am doing some studies on eye vascularization - my project contains a machine which can detect the different blood vessels in the retinal membrane at the back of the eye. What I am looking for is a possibility to segment the picture and analyze each segmentation on it`s own. The Segmentation consist of six squares wich I want to analyze separately on the density of white pixels.
I would be very thankful for every kind of input, I am pretty new in the programming world an I actually just have a bare concept on how it should work.
Thanks and Cheerio
Sam
Concept DrawOCTA PICTURE
You could probably accomplish this by using numpy to load the image and split it into sections. You could then analyze the sections using scikit-image or opencv (though this could be difficult to get working. To view the image, you can either save it to a file using numpy, or use matplotlib to open it in a new window.
First of all, please note that in image processing "segmentation" describes the process of grouping neighbouring pixels by context.
https://en.wikipedia.org/wiki/Image_segmentation
What you want to do can be done in various ways.
The most common way is by using ROIs or AOIs (region/area of interest). That's basically some geometric shape like a rectangle, circle, polygon or similar defined in image coordinates.
The image processing is then restricted to only process pixels within that region. So you don't slice your image into pieces but you restrict your evaluation to specific areas.
Another way, like you suggested is to cut the image into pieces and process them one by one. Those sub-images are usually created using ROIs.
A third option which is rather limited but sufficient for simple tasks like yours is accessing pixels directly using coordinate offsets and several nested loops.
Just google "python image processing" in combination with "library" "roi" "cropping" "sliding window" "subimage" "tiles" "slicing" and you'll get tons of information...
i retrieve contours from images by using canny algorithm. it's enough to have a descriptor image and put in SVM and find similarities? Or i need necessarily other features like elongation, perimeter, area ?
I talk about this, because inspired by this example: http://scikit-learn.org/dev/auto_examples/plot_digits_classification.html i give my image in greyscale first, in canny algorithm style second and in both cases my confusion matrix was plenty of 0 like precision, recall, f1-score, support measure
My advice is:
unless you have a low number of images in your database and/or the recognition is going to be really specific (not a random thing for example) I would highly recommend you to apply one or more features extractors such SIFT, Fourier Descriptors, Haralick's Features, Hough Transform to extract more details which could be summarised in a short vector.
Then you could apply SVM after all this in order to get more accuracy.
I would like to do some odd geometric/odd shape recognition. But I'm not sure how to do it.
Here's what I have so far:
Convert RGB image to Monochrome.
Otsu Threshold
Hough Transform.
I'm not sure what to do next.
For geometric information, you could do a raster to vector conversion to convert your image into coordinated vectors (lines and points) and finite element analysis to look for known shapes. Not easy but libraries should be available for both.
Edit: Note that there are sometimes easier practical solutions, but they depend on the image and types of errors. For example, removing perspective, identifying a 3d object from a 2d image, significance of colour, etc... You often see registration markers added to the real world object to overcome
this and allow much easier identification. Looking up articles on feature extraction techniques might help.