Image histogram

Intensity classification is often based on image histogram which shows different peaks, each corresponding to one region and adjacent peaks are likely to be separated by a valley. The problem posed is to determine thresholds corresponding to these valleys [Fig 04]. 

Compute the mean gray scale value of all pixels in the AOI using the Image Histogram function in Image-Pro Plus software. 

Fig. 6. (a) Noisy MR image (b) SR-enhanced image where the lesion, sulci and gyii are visible (c) (d) Image histograms of input image of fig.6a and SR-enhanced image of fig.6b. 

The probability distribution for the evaluation of MI and NMI can be estimated with Parzen windows, histograms or other probability density estimators. The most common method uses images histograms. Q and K are images with M pixels that can assume N gray levels gi>g2y-gN The MI between Q and K can be defined as ... 

The image entropy can be written in terms of the image histogram Hisq. 

Further, the j oint entropy of images Q and K with the same number of pixels M and the same gray level range N can be written in terms of joint image histogram ffisqjf ... 

Image histogram mean, SD, median Shape indices... 

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