Review Article

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Table 1

Summary of merits and demerits of mammograms segmentation methods.

CategoryMeritsDemerits

Edge-based segmentation methodsWorks well when an edge is prominentSensitivity 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 methodsSimple and easy to implementIt 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 segmentationConnected regions are guaranteedCauses over segmentation if mammograms are noisy
Multiple criterion and gives good results with less noiseCannot 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 methodsFew data are requiredNumber of clusters must be defined
Easy to implement
Prior information required
Automatic segment masses
Supervised machine learning methodsEasy to detect errorKnowledge about the mammogram to be segmented is required
Require lab data

Deep learning methodsSolve complex tasksLimited annotated data
Required unlabeled dataTime consuming during training
Expensive because it requires higher computational machines
Produce accurate results