how to find mean of an image

how to find mean of an image

[Ivan van der Kroon]

what is the class of your picture?

In most cases it is uint8, so type mean(mean(I)), with I the picture variable.

[Aditi]

the class of my picture is double...and please tell me how do find variance of an image..

[Walter Roberson]

mean() and variance() convert the values as if they were double precision, so you do not usually need to be concerned about what the original data type was.

Answer 1

[Wolfgang Schwanghart]

m = mean(I(:));

[Anil Sarode]

how does the mean is calculated, I mean by adding all the pixel values and then divide by a total number of pixels?

[Walter Roberson]

mean() is usually calculated by taking converting the numbers to double, taking the sum and dividing by the number of elements.

However, there are is an option for mean(), 'native', that tells it not to convert the numbers to double, to use the passed-in data type for the mean calculation. That calculation uses more complicated logic.

Answer 2

[murk hassan memon]

Currently i am working on blood images in order to detect the malaria.. i have to identify out the behaviour of normal and abnormal blood images for this first i have to find out the mean value of those blood images in a sense if suddenly variation occur in mean value of any image then it is said to be abnormal image right? now i am getting a confusion that whether i have to find the mean value of image or first i have to make matrix order of an image. waiting for response

[Walter Roberson]

I do not understand what you mean about making a matrix order of the images?

[murk hassan memon]

Img=imread('picture.jpg');

here Img is matrix with large number of rows and column ok? now should i take the mean of this Img or i make the some order of matrix like 10*10 or 100*100? which one is preferable to get the mean value in order to identify that image is normal or abnormal?

this is actualy i wanted to ask

[Walter Roberson]

It depends what your image is of.

Suppose you have an image of a single cell, but it happens to be high resolution. Then, after you have removed the parts of the image that are outside of the boundaries of the single cell, you would perhaps be wanting to take the mean of the entire image.

But suppose instead you have an image of multiple cells, where only some of the cells were abnormal. You could still mask out areas that are outside any cell. If you take the mean of the part only inside the cells, then you could be taking the mean of a bunch of good cells with a small number of abnormal cells -- the more good cells you had the less the influence of the abnormal cells would be, so you could end up missing the abnormal cells. In such a case you would want to consider the mean of each cell separately.

Now, for any one cell, is it the case that the color is affected for the entire cell? Or is it the case that part of the cell is affected in color? If only part is affected in color, then the mean color over the entire cell is going to be affected by the proportion of affected area to non-affected area. And perhaps that is reasonable for your situation. But if you need to identify the part of the cell that is affected then you need to look at only portions of the cell at a time.

If you are attempting to look at portions of a cell at a time, then there is the question of how large an area you should examine. That can depend on what you are looking for. Ask yourself: if the image were at (say) 5x further magnification, then would you want the area you take of the mean of to grow with the magnification, or would you instead want the higher resolution to identify the abnormal section more precisely ?

[murk hassan memon]

ok i got it. and will also try along with your suggestions. thank u soo much