Table 1.

Summary of advantages and disadvantages of segmentation methods.

Methods	Advantages	Disadvantages
GTa	Widely used as preprocessing step in image processing as these methods are easy to implement	Not suitable for segmentation of ROIsb, as GT methods produce high false positive detections
Local thresholding	Works well compared with GT, sometimes used to improve the GT results	Widely used in literature as initialization step of other algorithms, but local thresholding fails to separate the pixels accurately into suitable regions
Region growing	Uses pixel connectivity properties to grow iteratively and sum up the region having similar pixel properties	Need initialization point, that is, a seed point to begin with and highly dependent on initial guess
Region clustering	No seed point required to initialize; it can directly search the cluster regions.	Total number of clusters need to be predefined at initial stage
Edge detection	Highly suitable for detecting the object boundaries and contours of the suspected ROIs	Requires some information about object properties
Template matching	Needs ground truth and are easily implemented. Easy implementation; if the prototypes are suitably selected, it can produce good results.	Need prior information about the region properties of the objects such as size, shape, and area.
Multiscale technique	Do not require any prior knowledge about object properties	Requires empirical evaluation to select the appropriate wavelet transform
	Easily discriminate among the coefficients at different level and scale of decompositions	Need to select scale of decompositions

^aGT: Global thresholding.

^bROI: region of interest.