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What is the first-order entropy of imaging?
Specifically, first-order entropy is a method for measuring the amount of information in a signal (in this case, an image). It quantifies the randomness and diversity of the image by calculating the difference between pixel values. The values of the first-order entropy range from 0 to infinity, where 0 means that the pixel values are exactly the same and infinity means that the pixel values are completely different.
In image genomics, first-order entropy is usually used to extract statistical information of images. It can be used to analyze the differences between tumor tissues and normal tissues, or to detect the growth and changes of tumors by comparing the similarities between two different image data sets. The first-order entropy can also be used in other fields, such as computer vision and signal processing.
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