Image processing has to deal with many information of an image. Gray histogram can contain a lot of image information. Maximum entropy theorem of Information Theory is one of the useful tools to treat with this kind of information. There are several formulas for computing the maximum entropy. But almost of the existing formulas have some common disadvantages

such as expensively computing and more complex algorithm realizing. In order to overcome these weaknesses of the existing entropy formulas

in this paper we define a new approach to entropy

and use it to automatically select thresholds of the image. It bases on one of Shannon entropy's basic properties that the equivalent probability distributing has maximum entropy to get the image thresholds. And by this way

we can segment an image into several equivalent probability sub parts. This new method has some advantages

such as simplified

stabilized and easily realized comparing with some traditional entropy methods. At the same time

it can get image thresholds quickly. We have employed the newly proposed approach to perform image enhancement

segmentation and thresholding

and obtained satisfactory results.