FCM-Segmentation
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Image Segmentation using Fuzzy C-Means Clustering with Bias Field Correction
Segmentation using FCM with Bias Field Correction
A modified Fuzzy-C-Means (FCM) approach to segment an image while estimating and accounting for the bias/inhomogeneity field.
Usage:
See code/main.mlx
and run it cell by cell.
Documentation:
-
code/main.mlx
: The main script/driver program -
code/computeA.m
: Computes the bias-removed image -
code/distance.m
: Computes the "distance" values used inupdateU.m
-
code/iterate.m
: Driver class for the algorithm -
code/KMeans.m
: Returns the initial segmentation using the standard K-means algorithm -
code/objectiveFunction.m
: Evaluates the objective function at the current estimates -
code/showSegmented.m
: Plots the segmented image using a custom colormap -
code/updateB.m
: Computes the optimal value of the bias field, within every iteration. -
code/updateC.m
: Computes the optimal value of the class means, within every iteration. -
code/updateU.m
: Computes the optimal value of the class memberships, within every iteration.
(This was done as a course assignment for CS736: Medical Image Computing, Spring 2021, IIT Bombay)