Threshold value question using otsu method
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I am doing Plant leaf disease detection & classification project. In that i am using otsu method to calculate threshold value for all leaves. But thershold value for some type of leaves could not detect the diseased part correctly..so i am using some constant threshold value(0.17,0.6). Can anyone please give some other method to find threshold value other than otsu method that is suitable for all type of leaves..
Accepted Answer
Image Analyst
on 30 Dec 2014
2 votes
See my attached code. I use an image with black background and even black spots inside it. I correctly identify the diseased areas and give the area fraction of them relative to the entire leaf area (diseased + healthy areas).
If this solves your question, please mark as Accepted.
24 Comments
Packiya
on 31 Dec 2014
Packiya
on 3 Jan 2015
Image Analyst
on 3 Jan 2015
I looked at the histogram. 0.15 is in the valley between the two humps.
Packiya
on 4 Jan 2015
Image Analyst
on 4 Jan 2015
It simply counts the number of white pixels in the image. Just try it and see if it works on all your images. If it doesn't, then you'll have to make the algorithm more sophisticated to handle those cases.
Packiya
on 4 Jan 2015
Image Analyst
on 4 Jan 2015
My code assumes that the healthy pixels are green and non-green pixels are diseased. If you have an image where that is not the case, then please attach it, and state what green pixels are labeled as healthy but are actually diseased.
Image Analyst
on 5 Jan 2015
There are many texture algorithms listed here http://www.visionbib.com/bibliography/contentstwod.html#2-D%20Feature%20Analysis,%20Extraction%20and%20Representations,%20Shape,%20Skeletons,%20Texture, but it sounds like you already have a paper that says what textures are good for plant diseases so I'm surprised that it doesn't work for your images.
Packiya
on 9 Jan 2015
Packiya
on 9 Jan 2015
Packiya
on 13 Jan 2015
Image Analyst
on 13 Jan 2015
No, I don't. There are many, many measurements that could be made, and you have to compare a weighted average of them (or some algorithm) to compare to feature vectors of whatever they're trying to match (like reference images in a database).
Packiya
on 20 Jan 2015
Image Analyst
on 20 Jan 2015
Yes, you can. Just use the healthy and diseased masks to get the mean HSV value of the image in the mask area
meanHealthyH = mean(h(healthyMask));
meanHealthyS = mean(s(healthyMask));
meanHealthyV = mean(v(healthyMask));
meanDiseasedH = mean(h(diseasedMask));
meanDiseasedS = mean(s(diseasedMask));
meanDiseasedV = mean(v(diseasedMask));
Packiya
on 22 Jan 2015
Image Analyst
on 22 Jan 2015
Whoever is sponsoring your project should be able to provide those. If someone is paying you to do this project then they must have a need for whatever software you deliver, so they must have diseased leaves. Obtain them from that person or organization.
Image Analyst
on 23 Jan 2015
Why do you say "i need to process some constant number of pixels from all the blobs."? I don't think that is necessary.
By the way, the method I used in my demo above - segmentation by thresholding in HSV color space - is the same one I use in my "SimpleColorDetectionByHue.m" tutorial.
Packiya
on 23 Jan 2015
Image Analyst
on 23 Jan 2015
What? Why???? No it won't. Why do you think the mean of a region depends on how many pixels are in that region?
Asmalina Mohamed Saat
on 19 Oct 2020
Forgive me for interrupting your discussion...
i'm bit confused when you choose 0.15 and 0.5 as healthy image...
"I looked at the histogram. 0.15 is in the valley between the two humps." this is from your previous answer..
can you explain more what if the graph only have one humps if 0 until 0.3.. what value i one to pick as healty image?
Image Analyst
on 19 Oct 2020
For your image (which you forgot to attach), it seems like the hue is not a feature that will have discriminating power. You should probably choose another feature, like maybe saturation or value or texture.
More Answers (4)
Sean de Wolski
on 24 Dec 2014
0 votes
Show us an image! Without an image, it's very difficult if not impossible to guess at what you need.
Have you tried the multilevel otsu threshold? I.e. the Image Processing function imquantize?
7 Comments
Packiya
on 29 Dec 2014
Sean de Wolski
on 29 Dec 2014
Only use otsu to identify the object, i.e. the whole leaf. Use other methods to detect the rot.
Image Analyst
on 29 Dec 2014
I don't know who thought of photographing a green leaf on a green background, but it's a bad idea. Photograph them on a black background.
Packiya
on 30 Dec 2014
Image Analyst
on 30 Dec 2014
Edited: Image Analyst
on 30 Dec 2014
No, that's not true. Or it's only true if you don't do it the way you're supposed to do it. With your way, you can't find the area of the leaf so you can't get area fractions.
First of all you segment for black. But you only want black if it's connected to the background, not if, say, there is a hole in the leaf due to disease, so you call imclearborder() and subtract that from the first binary image to get only the black connected to the border. Now mask your image and convert to hsv color space. Then define some hue sector that is "green" and sum the pixels in this sector - that's your green area. The rest of the pixels that aren't green and aren't part of the background are "disease". See my color segmentation routines, probably the HSV one would be best, in my File Exchange. Let me know if you need further help.
Spandan Tiwari
on 15 Jan 2015
A clarification - the function to use for multilevel Otsu is multithresh . It outputs the threshold level(s). If required, the image can then be quantized using those thresholds using imquantize.
Image Analyst
on 24 Dec 2014
0 votes
I almost never use Otsu. It's usually no good. It's good mostly in cases with high contrast images with widely separated bimodal histograms, like where there is a uniform object on a uniform background and they have very different intensities. In real world situations, Otsu is often/usually not the best choice.
Actually using a fixed threshold like you're doing is a very good but under-used and under-appreciated method. People always want to use automatic thresholding but simple-minded autothresholding methods like Otsu have the drawback in that they always have to find a threshold somewhere in the histogram. Let's say you're looking at some image and the disease or stuff you want to find can be anywhere from 0% area fraction all the way up to 100% area fraction. Let's say you have a hump centered at 100 for good/normal/non-diseased pixels, and as the pixel goes "bad" the pixels get darker, which means their histogram shifts darker. Let's say one that was 100% bad would give you a hump at 50 gray levels. But what if you had a 100% bad image and Otsu would try to split that histogram and might give you something around 48 or 55 or whatever. It would give you the wrong area fraction because it was forced to find a threshold in a histogram hump that was essentially all the same - all bad. It would be much better to just say that anything below, say, 75 is bad and above 75 is good. That way you can get accurate area fractions all the way from 0% to 100%. If you have good control over your lighting and exposure so that similar images produce similar gray levels, then using a fixed threshold is a fine idea.
But in general, you need to decide upon a thresholding scheme based on the complete range of images that you expect to encounter. You haven't shown us even a single image, much less the entire span of images you expect the algorithm to be able to handle. Usually people asking for image processing advice attach an image to we don't have to work "blind."
1 Comment
Sean de Wolski
on 24 Dec 2014
I agree that fixed thresholds are useful, especially for x-rays where the pixel values are a direct measure of energy passing through.
However, Otsu is appealing too for images where there is an object in the foreground and a background. It can do a good job dealing with changes in lighting etc.
Anand
on 15 Jan 2015
I haven't read the entire thread above. I'm sure there's a lot of good information there. I take it you are trying to threshold the 'diseased' portion of the leaf. So, essentially you want an image with disease pixels vs non-disease pixels.
I tried using the colorThresholder App with a couple of the images you've attached.
Here's how it looks:
im = imread('http://www.mathworks.com/matlabcentral/answers/uploaded_files/23137/Bacterial1.jpg');
% Open the colorThresholder
colorThresholder(im)
The hue channel in the HSV color space seems to separate the disease pretty well, so select HSV. Once the image opens in the HSV color space, move the hue slider appropriately to threshold the image.
Now export the code and use this on the other images.
Hope this helps!
tan aik hong
on 5 Oct 2018
0 votes
thanks and it is very helpful for my project, but I had some problem, which is if the image of the leaf is healthy, through the code above, I had got the healthy region and diseases region. may I know is there any code that able to choose the image of leaf whether it is healthy or not. thanks
1 Comment
Image Analyst
on 5 Oct 2018
No, that is the part that you need to do yourself, or hire someone to do. But once they've all been analyzed, and you have a boolean variable "isDiseased", then you can find the index of diseased ones, something like this
for k = 1 : length(isDiseased)
if ~isDiseased
continue; % Skip healthy ones.
end
thisFileName = allFileNames{k};
imshow(thisFileName);
drawnow;
end
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