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b Segmentation of the ROIs using the membership matrix U into 2 groups.
Using the membership matrix of each group of samples it can be calculated how typical a sample is for each group.
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Furthermore, the membership degree is recalculated by using the membership probability-based confidence map.
This step is done by using the membership functions.
To optimize J rf with respect to the membership matrix, we use the Lagrange multiplier technique to obtain the following updated equation: u ij = 1 ∑ k = 1 c ρ ∥ x i − c j ∥ 2 ρ ∥ x i − c k ∥ 2 1 m − 1 (12).
Because the value of partition matrix represents the membership of a sample belonging to a class, we can directly use the membership value of partition matrix as the BPA or mass function of D-S theory.
Based on the membership functions with the evaluation levels, the membership matrix R can be composed by using index data to calculate the index membership values.
Last, the membership matrix Ψ ′ will be classified using IFS entropy to obtain a final segmented image.
The typicality matrix T. The membership matrix U.
The membership matrix is the degree of membership of an index to a comment.
Run the PFCM algorithm to obtain: The membership matrix U.
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