Abstract
Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: segmentation. That is, identifying the region of interest in order to further analyze and classify it either as benign or malignant. However, due to the different types of mammary tissues, mammography segmentation can prove to be a difficult task.
Various techniques of mammography segmentation have been proposed so far. Yet, since obtaining the ground-truth for mam-mographic images might be problematic, recent literature leans towards unsupervised techniques. In this paper we present a segmentation approach based on the GrowCut algorithm. The original method starts with a number of seed points inside and outside the region of interest, selected by a human expert, and iterates over the pixels multiple times, until it reaches a stable state where all the pixels have been assigned to a class. Our proposal aims to reduce the human intervention by eliminating the need of selecting initial background seeds and, also, to reduce the computational time by limiting the process of parsing the entire image to a small, fixed number of iterations.
The proposed approach was compared to the original method, using three variants: (1) automatically constructing the initial background seeds surrounding the foreground ones; (2) using mammograms’s background as initial background seeds; (3) not using initial background seeds. Experimental results obtained for the Mini-MIAS dataset show that, for the variations that use background seeds outside the breast and that do not use any background seeds, our approach yields much better results than the original method.
Citare
Moroz-Dubenco C., Dioşan L., Andreica A., Mammography Lesion Detection Using an Improved GrowCut Algorithm, Procedia Computer Science, Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021