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 in updateU.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.

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(This was done as a course assignment for CS736: Medical Image Computing, Spring 2021, IIT Bombay)