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. 2022 Nov 4;10:1801111. doi: 10.1109/JTEHM.2022.3219891

TABLE 2. Features Extracted for the Diagnosis of Breast Masses.

Name Description
Shape-based features
Area number of pixels
Circularity how compact or circular is the region
Compactness degree of deviation of the mass from a perfect circle
Convex area number of pixels in the convex image
Eccentricity ratio of distance between the foci of the ellipse and its major axis length
Equivalent diameter diameter of the circle with the same area as the region
Euler number subtraction of the number of objects and the number of holes in those objects
Extent ratio of pixels inside the region to pixels in the bounding box
Filled area number of pixels inside the filled image
Major axis length length of the major axis of an ellipse that has same normalized central moment with the region
Minor axis length length of the minor axis of an ellipse that has same normalized central moment with the region
Orientation angle between the axis of the ellipse that has same second moments with the region
Perimeter distance around the boundary of the region
Solidity relative amount of pixels that appeared in both the convex hull and the region
Shape ratio proportional relationship of the width to the height
Intensity-based features
Maximum intensity maximum of all the intensity values
Minimum intensity minimum of all the intensity values
FOS features
Average intensity mean of all the intensity values
Entropy texture measurement
Kurtosis correlated with probability distribution
Skewness asymmetry measurement
Smoothness measures the relative smoothness of intensity
Standard deviation variation from the average value
Variance how far are the pixels values from the average
GLCM features
Contrast amount of local variations
Correlation measurement of gray tone linear-dependencies
Energy information measurement
Homogeneity information about the distribution elements