Table 1.

Summary of merits and demerits of mammograms segmentation methods.

Category	Merits	Demerits
Edge-based segmentation methods	Works well when an edge is prominent	Sensitivity to noise
		Reduces overall contrast in mammograms
	Easy to find locally edge orientation
		Produce unsatisfactory results when it detects fake and weak edges in mammograms
		Not suitable for mammogram images having smooth edges
Threshold-based segmentation methods	Simple and easy to implement	It is not applicable if the tumour area ratio is unknown
		Sensitive to noise in mammograms
	Faster
		Gives poor results when mammograms have low contrast
	Inexpensive
		Difficulties to fix the threshold value if the number of regions increases
		Not easy to process the mammogram whose histograms are nearly unimodal
Region-based segmentation	Connected regions are guaranteed	Causes over segmentation if mammograms are noisy
	Multiple criterion and gives good results with less noise	Cannot distinguish the shading of the real mammograms
		Time consuming due to the high resolution of mammograms
		Not suitable for noisy mammograms
		Seed point must be selected
Unsupervised machine learning methods	Few data are required	Number of clusters must be defined
	Easy to implement
	Prior information required
	Automatic segment masses
Supervised machine learning methods	Easy to detect error	Knowledge about the mammogram to be segmented is required
		Require lab data
Deep learning methods	Solve complex tasks	Limited annotated data
	Required unlabeled data	Time consuming during training
		Expensive because it requires higher computational machines
	Produce accurate results