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matlabimage-processingimage-segmentationfeature-extractionpattern-recognition

What is the difference between entropy, energy, mean, skewness, variance, inertia and kurtosis in image processing?


I am reading in Feature Extraction in Medical Imaging field, especially in brain tumor detection, and I had found the above feature extraction and I don't understand the difference between them.


Solution

  • The various features that can be calculated from the co-occurrence matrices (C) are inertia (contrast), absolute value, inverse difference, energy, and entropy. Contrast is the element difference moment of order 2, which has a relatively low value when the high values of C are near the main diagonal. Energy value is highest when all values in the co-occurrence matrix are all equal. (Sigma(i) Sigma (j) cij^2). Entropy of the image is the measure of randomness of the image gray levels. ( -1*Sigma(i) Sigma (j) cij^2 * log cij^2 (with base 2))