Cell-segmentation from overstaining images - matlab

I am currently trying to segment cells from digital pathology images.
The method I use is an algorithm based on color distance.
This works for most of the cases, however, when dealing with the images which cells are overstaining, then the segmentation results is pretty bad.
Is there any golden-standard method in treating these poorly stained with highly adhered-cell images?

Related

Size of image for prediction with SageMaker object detection?

I'm using the AWS SageMaker "built in" object detection algorithm (SSD) and we've trained it on a series of annotated 512x512 images (image_shape=512). We've deployed an endpoint and when using it for prediction we're getting mixed results.
If the image we use for prediciton is around that 512x512 size we're getting great accuracy and good results. If the image is significantly larger (e.g. 8000x10000) we get either wildly inaccurate, or no results. If I manually resize those large images to 512x512pixels the features we're looking for are no longer discernable to the eye. Which suggests that if my endpoint is resizing images, then that would explain why the model is struggling.
Note: Although the size in pexels is large, my images are basically line drawings on a white background. They have very little color and large patches of solid white, so they compress very well. I'm mot running into the 6Mb request size limit.
So, my questions are:
Does training the model at image_shape=512 mean my prediction images should also be that same size?
Is there a generally accepted method for doing object detection on very large images? I can envisage how I might chop the image into smaller tiles then feed each tile to my model, but if there's something "out of the box" that will do it for me, then that'd save some effort.
Your understanding is correct. The endpoint resizes images based on the parameter image_shape. To answer your questions:
As long as the scale of objects (i.e., expansion of pixels) in the resized images are similar between training and prediction data, the trained model should work.
Cropping is one option. Another method is to train separate models for large and small images as David suggested.

Matlab edge detection problems - can I do it manually?

I have a set of roughly 2000 images to process, and have stumbled on a problem regarding my edges. The original images are CT Scans of a heart, which are then thresholded and sobel filtered to produce a binary image of parts of the tissue. Unfortunately the thresholding and filtering has resulted in certain images missing the 'edges' of the heart --> R&LHS you can see the gaps & further down Just a small one at the top hopefully this shows how annoying this is to do automatically
I've tried various inbuilt methods of edge detection, but the difference in size of the gaps makes it nearly impossible to do, without filling out the bits that are supposed to remain as gaps due to the sizes of the filters I use.
Is there a way of manually "connecting the dots" so to speak? It would take forever on the whole image set but seems to be my only option, or if you could suggest any other way of doing it would be cool!

OpenCV Matching Exposure across images

I was wondering if its possible to match the exposure across a set of images.
For example, lets say you have 5 images that were taken at different angles. Images 1-3,5 are taken with the same exposure whilst the 4th image have a slightly darker exposure. When I then try to combine these into a cylindrical panorama using (seamFinder with: gc_color, surf detection, MULTI_BAND blending,Wave correction, etc.) the result turns out with a big shadow in the middle due to the darkness from image 4.
I've also tried using exposureCompensator without luck.
Since I'm taking the pictures in iOS, I maybe could increase exposure manually when needed? But this doesn't seem optimal..
Have anyone else dealt with this problem?
This method is probably overkill (and not just a little) but the current state-of-the-art method for ensuring color consistency between different images is presented in this article from HaCohen et al.
Their algorithm can correct a wide range of errors in image sets. I have implemented and tested it on datasets with large errors and it performs very well.
But, once again, I suppose this is way overkill for panorama stitching.
Sunreef has provided a very good paper, but it does seem overkill because of the complexity of a possible implementation.
What you want to do is to equalize the exposure not on the entire images, but on the overlapping zones. If the histograms of the overlapped zones match, it is a good indicator that the images have similar brightness and exposure conditions. Since you are doing more than 1 stitch, you may require a global equalization in order to make all the images look similar, and then only equalize them using either a weighted equalization on the overlapped region or a quadratic optimiser (which is again overkill if you are not a professional photographer). OpenCV has a simple implmentation of a simple equalization compensation algorithm.
The detail::ExposureCompensator class of OpenCV (sample implementation of such a stitiching is here) would be ideal for you to use.
Just create a compensator (try the 2 different types of compensation: GAIN and GAIN_BLOCKS)
Feed the images into the compensator, based on where their top-left cornes lie (in the stitched image) along with a mask (which can be either completely white or white only in the overlapped region).
Apply compensation on each individual image and iteratively check the results.
I don't know any way to do this in iOS, just OpenCV.

min max flow graph cuts for image segmentation MATLAB wrapper

My objective is to segment to perform cell segmentation. The cells are of different sizes, and the image is grayscale using graph cuts
I used the MATLAB wrapper by Shai Bagon, and I am able to run the basic test code which was given here
I am not able to get the label image as desired. I need help to interpret the results from this method.
Also another quick question, from the examples I have looked do far. Graph cut seems to be used for images with a single connected foreground and the rest is background. However I can not find examples where the foreground is disjoint (as in my case, cells all over the image) and the background. This makes me assume that graph cuts does not work on a disjoint foreground ? If someone can let me know this is correct.
Your help is appreciated. Thank you
Regarding your results on cell segmentation: you provide very little input to work with. It is unclear what you are trying to do and what is wrong with what you actually get. You'll have to post some example input image and short code.
As for the second question, in general GraphCut image segmentation is not restricted to a single connected output segment. The number of segments depend on the data and smoothness terms provided. As a rule of thumb, the weaker the smoothness term the more connected-components you'll see at the output.

Sparse Image matching in iOS

I am building an iOS app that, as a key feature, incorporates image matching. The problem is the images I need to recognize are small orienteering 10x10 plaques with simple large text on them. They can be quite reflective and will be outside(so the light conditions will be variable). Sample image
There will be up to 15 of these types of image in the pool and really all I need to detect is the text, in order to log where the user has been.
The problem I am facing is that with the image matching software I have tried, aurasma and slightly more successfully arlabs, they can't distinguish between them as they are primarily built to work with detailed images.
I need to accurately detect which plaque is being scanned and have considered using gps to refine the selection but the only reliable way I have found is to get the user to manually enter the text. One of the key attractions we have based the product around is being able to detect these images that are already in place and not have to set up any additional material.
Can anyone suggest a piece of software that would work(as is iOS friendly) or a method of detection that would be effective and interactive/pleasing for the user.
Sample environment:
http://www.orienteeringcoach.com/wp-content/uploads/2012/08/startfinishscp.jpeg
The environment can change substantially, basically anywhere a plaque could be positioned they are; fences, walls, and posts in either wooded or open areas, but overwhelmingly outdoors.
I'm not an iOs programmer, but I will try to answer from an algorithmic point of view. Essentially, you have a detection problem ("Where is the plaque?") and a classification problem ("Which one is it?"). Asking the user to keep the plaque in a pre-defined region is certainly a good idea. This solves the detection problem, which is often harder to solve with limited resources than the classification problem.
For classification, I see two alternatives:
The classic "Computer Vision" route would be feature extraction and classification. Local Binary Patterns and HOG are feature extractors known to be fast enough for mobile (the former more than the latter), and they are not too complicated to implement. Classifiers, however, are non-trivial, and you would probably have to search for an appropriate iOs library.
Alternatively, you could try to binarize the image, i.e. classify pixels as "plate" / white or "text" / black. Then you can use an error-tolerant similarity measure for comparing your binarized image with a binarized reference image of the plaque. The chamfer distance measure is a good candidate. It essentially boils down to comparing the distance transforms of your two binarized images. This is more tolerant to misalignment than comparing binary images directly. The distance transforms of the reference images can be pre-computed and stored on the device.
Personally, I would try the second approach. A (non-mobile) prototype of the second approach is relatively easy to code and evaluate with a good image processing library (OpenCV, Matlab + Image Processing Toolbox, Python, etc).
I managed to find a solution that is working quite well. Im not fully optimized yet but I think its just tweaking filters, as ill explain later on.
Initially I tried to set up opencv but it was very time consuming and a steep learning curve but it did give me an idea. The key to my problem is really detecting the characters within the image and ignoring the background, which was basically just noise. OCR was designed exactly for this purpose.
I found the free library tesseract (https://github.com/ldiqual/tesseract-ios-lib) easy to use and with plenty of customizability. At first the results were very random but applying sharpening and monochromatic filter and a color invert worked well to clean up the text. Next a marked out a target area on the ui and used that to cut out the rectangle of image to process. The speed of processing is slow on large images and this cut it dramatically. The OCR filter allowed me to restrict allowable characters and as the plaques follow a standard configuration this narrowed down the accuracy.
So far its been successful with the grey background plaques but I havent found the correct filter for the red and white editions. My goal will be to add color detection and remove the need to feed in the data type.

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