PII: S0031-3203(97)00004-6 INVESTIGATIONS ON FUZZY THRESHOLDING BASED ON FUZZY CLUSTERING

Abstract---Thresholding, the problem of pixel classification isattempted here using fuzzy clustering algorithms. The segmented regions are fuzzy subsets, with soft partitions characterizing the region boundaries. The validity of the assumptions and thresholding schemes are investigated inthe presence of distinct region proportions. The hard k means and fuzzy c means algorithms have been found useful when object and background regions are well balanced. Fuzzy thresholding is also formulated as extraction of normal densities to provide optimal partitions. Regional imbalances in gray distributions are taken care of in region normalized histograms. ~ 1997 Pattern Recognition Society. Published by Elsevier Science Ltd. Fuzzy clustering Thresholding Segmentation Bayesian classifier Fuzzy c means algorithm Gray level thresholding is the process of partitioning pixels in a digital image into two mutually exclusive and exhaustive regions. Let J: [lmn]m×N denote the image with pixel at (re, n) assuming a discrete gray valu