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Breast cancer is the most common disease of the current century in the female population of the world. The main task of the research of most scientists is the detection of this pathology at an early stage (the tumor size is less than 7 mm) when a woman can still be helped. An indicator of this disease is the presence of small-point microcalcifications, located in groups within or in the immediate circle of the tumor. Microcalcification is a small-point character at cancer, reminding grains of sand of irregular shape which sizes are from 100 to 600 microns. The probability of breast cancer increases with the increase in the number of microcalcifications per unit area. So, the probability of cancer is 80% if more than 15 microcalcifications on 1 sq. cm. The microcalcifications are often the only sign of breast cancer, therefore, their detection even in the absence of a tumor node could be a harbinger to cancer. Image segmentation is one way to identify microcalcifications. The conducted research allowed us to choose the optimal segmentation algorithms of mammograms to highlight areas of microcalcifications for further analysis of their groups, sizes, and so on.

Comparative analysis of segmentation algorithms for the allocation of microcalcifications on mammograms