Image Processing - Term Paper Example

Histogram Analysis is where one chooses a threshold value T which will separate bright features from the black ground. Image histograms will help to view how distributed is gray objects by providing f grayscale intensity values such as thresholds, background values and visualizing the effect of contrast adjustments. The method applied to complete the convolution process began by using Matlab in-built histogram analysis function. During the development of the code that was used in solving the function, the input was put in small increments producing solutions which were related to each iteration.

This means the iteration worked well during the development of the code (Gallo and Spinello, 2000). Every iteration was approached with its trade-off in case of delays we waited for the result before inputting new codes. Here the purpose was to use the histogram analysis function given to create new pixels for use. In the matlab function, the histogram analysis function to visualize image histograms is imhist is recorded as I = imread('imagefile’). This function aims at developing values that would be used to create new pixels.

It has 256 bins by default with 20 bins commands which is recorded imhist(f,20);. To begin with the image CT.JPG has been analyzed using histogram analysis as well as pixels. Histogram equalisation has been done to improve the pixels by distributing them to grey levels. It compresses the image or stretch to the point that the image is visible. To open the image, the following code was entered >> I = imread('D:\Documents and Settings\Administrator\My Documents\CT.JPG'); And to display the image, the following scripts was used >> imshow(I) The following was used to create a histogram of the pixels in the image >> I=double(I); >> [m,n]=size(I); Showing the number of pixels >> len=m*n; Coverting the image to consider the number of pixels.

>> x=reshape(I,len,1); Equalisation is performed using the following scripts >> xf=hist(x,[0:L-1]); To normalise the image so that all the features from number of pixels to sum of the image edges. >> xf=xf/len; >> sk=xf*triu(ones(L)) >> for k=0:L-1 if (xf(k+1) > 0) to find the value of the image that has been used list=find(I ==k); mapping the image. y0(list)=sk(k+1)*L; end end the equalisation figure comparing the three images uses the following scripts.

>> y=reshape(y0,len,1); >> yf=hist(y,[0:L-1])/len; Preparing to produce necessary images