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| * **Variance** - Like the standart deviation, the variance also represents the numerical data dispersion degree surrounding the mean but in the original data values scale. It is defined by: | * **Variance** - Like the standart deviation, the variance also represents the numerical data dispersion degree surrounding the mean but in the original data values scale. It is defined by: | ||
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| {{ interimage:att_dissimilarityglcm.gif }} | {{ interimage:att_dissimilarityglcm.gif }} | ||
| - | * **EntropyGLCM** - | + | * **EntropyGLCM** - Like the simple statistical entropy the GLCM entropy also is a statistical measure of image data randomness. The difference is that it uses frequencies of gray levels co-ocurrences instead of using point values frequencies. The co-ocurrences matrix is used and the calculus is showed by the next formula where "i" and "j" are adjacent image points values under one pre-defined direction. p(i,j) is the probability of that co-ocurrence over the image. |
| + | {{ interimage:att_entropyglcm.gif }} | ||
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| + | * **HomogeneityGLCM** - Returns a value representing the distance between the distribuition of co-ocurrence matrix elements and those diagonal elements. The returned values range is between [0,1]. For images with low values variation the returned value will be near to zero. The calculus is showed by the next formula where "i" and "j" are adjacent image points values under one pre-defined direction. p(i,j) is the probability of that co-ocurrence over the image. | ||
| + | {{ interimage:att_homogeneityglcm.gif }} | ||
| - | * **HomogeneityGLCM** - | + | * **MeanGLCM** - The GLCM mean value is expressed in function of the frequency of co-ocorrence of image elements related to their neighborhood under one pre-defined direction. The calculus is showed by the next formula where "i" and "j" are adjacent image points values under one pre-defined direction. p(i,j) is the probability of that co-ocurrence over the image. |
| + | {{ interimage:att_meanglcm.gif}} | ||
| - | * **MeanGLCM** - | + | * **QuiSquareGLCM** - This metric can be understood as a form of energy normalization expressed in function of the linear dependency gray levels for image elements. The calculus is showed by the next formulas where "i" and "j" are adjacent image points values under one pre-defined direction. p(i,j) is the probability of that co-ocurrence over the image. Pj is the marginal probability for that co-ocurrence. |
| + | {{ interimage:att_quisquareglcm_1.gif }} | ||
| + | {{ interimage:att_quisquareglcm_2.gif }} | ||
| + | {{ interimage:att_quisquareglcm_3.gif }} | ||
| - | * **QuiSquareGLCM** - | ||
| * **StdDeviationGLCM** - | * **StdDeviationGLCM** - | ||
