Table 3.

Image segmentation methods

Name of segmentation method	Description of method	Benefits	Limitations
Edge detection method	Depends on discontinuity detection; generally aims to situate points with less or more rapid gray-level changes	• A human-like approach • Appropriate methods to get the best picture disproportionate area	• Not good with images where edges are unclearly defined • Not suitable for images whose edges are not completely clear • Not suitable for images with a large number of edges • It has the lowest noise compared to other methods
Thresholding method	Requires images with diffident sharp edges, each of which fits a single area	• No need for prior knowledge about the image • Minimum complexity of computation	• Not good for image with no clear peaks • Not suitable for images that do not have any clear edge • Do not consider distances, so there is no guarantee for the division of continuous areas
Region-dependent method	• The same pixel in nearby areas are similar • Counting the growing area, separating, assembling or reversing them	• Does well if region homogeneity norm is painless to define • Extra noise resistant as compared to edge detection method	• The amount of memory and calculation time is very costly • Region growing relies on seed region selection and sequence by which regions, pixels are inspected • Output segments by region splitting emerge too square because of splitting format
Fuzzy method	Uses operators, mathematics, properties and rules of fuzzy inference	• Fuzzy membership function can be used to represent the degree of low properties or linguistic phrases	• Determining the fuzzy membership function is not easy • The calculations used in the fuzzy method can be difficult
Neural network method	Uses for clustering or categorization	• Does not require writing tedious programs • Could entirely exploit the parallel nature of neural net	• Need a lot of time to train • Initialization might affect the outcome

Note: Data adapted from Pradeep et al.¹⁶