A Novel Maximum Fuzzy Entropy Thresholding of Seismic Images

Image thresholding is very useful for keeping the significant part of an image and getting rid of the unimportant part or noise. This holds true under the assumption that a reasonable threshold value is chosen. The study of image thresholding techniques in earthquake engineering, remote sensing, geology and geophysics seems to be extremely important for recognition of certain patterns such as faults, folding, fracturing, thrusting, closure, salt domes, strong reflectors, seismic facies, channels, bright spots etc, and the identification of large zones of common signal texture which are not detectable so minutely by other techniques. This paper presents a novel maximum fuzzy entropy thresholding of seismic images. The concept of fuzzy probability and fuzzy partition is introduced first. Then, based on the conditional probabilities and fuzzy partition, a 2-level optimal thresholding is searched adaptively through the maximum entropy principle of the seismic images.