%%The primary functions for loading images is 'imread' and for displaying images is 'imshow'.
 
im=imread('baboon.png');
imshow(im);
 
%% Images are basically two dimensional array of pixels (picture elements) with a third dimension
%% that shows the intensity of pixel. If the image is a gray scale image,, the intensity is a single number
%% at each pixel. If it is a color image, it is stored as a "third dimension" with 3 value
%% each corresponding to the intensity of r (red), g (green) and b (blue) colors. Thus if we do:
 
size(im)
 
%% The result is 512 512 3, which indicates it is a two dimensional array of 512 x  512 pixels with three colors.
%% To get the 'red' intensity, you can do:
 
r=im(:, :, 1);
 
%% This is now a two dimensional array. The color intensity can vary anywhere from 8 bits (256 levels)
%% for grayscale to 24 bits (8 bits for each color) for color images. There are even 30-, 36-, or 48- bit
%% images. As far as Matlab is concerned,. the image once loaded is a three dimensional array with the last 
%% dimension taking on only 3 values. 


max(r(:)),min(r(:))
 
%% will show 255 and 0 (the red color is stored in 8 bits).


imshow(r)
 
%% will show the image in grayscale with intensity corresponding to "red" color intensity in the 
%% original image. You can convert the color image to a grayscale image:
 
img=rgb2gray(im);
imshow(img)
 
This is now a two dimensional image with a grayscale intensity of 8 bits. We have compressed the image
at the expense of color!

%% Many medical images (such as MRI, CT, ultrasound, etc.) are likely to be grayscale images. 
%% They can 10 or 12 bit images and it is convenient to represent them using 16 bits as computers can 16 bits efficiently.
A=imread('Brain-MRI.jpg');
size(A)
imshow(A)
%% We can convert this to an grayscale image with:
B=rgb2gray(A)
%% This will be a 2D array of pixels with gray shades represented by 8 bits (256 shades).
%% We can plot a histogram of color intensity (x-axis intensity, y axis -- number of pixels) with:
imhist(B)
%% We can reduce the variation in the intensity among the pixels (and thereby visually enhance the darker regions)
%% by using the matlab function histeq.
histeq(B)
%%will display the image with formerly darker regions more bright and brighter regions darker.