Comparative analysis of mammography image segmentation strategies.

Breast cancer is a serious medical problem that affects women all over the world, and it is one of the most well-known tumors that kill women. The specialists of Breast cancer Prefer to use imaging methods such as a mammography to speed up recovery and reduce the risk of breast cancer. An ROI descri...

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Veröffentlicht in:Journal La Multiapp Jg. 3; H. 2; S. 37 - 43
Hauptverfasser: Abed, Areej Rebat, Hussein, Karim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 31.03.2022
ISSN:2716-3865, 2721-1290
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Zusammenfassung:Breast cancer is a serious medical problem that affects women all over the world, and it is one of the most well-known tumors that kill women. The specialists of Breast cancer Prefer to use imaging methods such as a mammography to speed up recovery and reduce the risk of breast cancer. An ROI describe the tumor will be retrieved from the image that is entered to detect a malignant tumor. One of the basic techniques used to classify breast cancer is segmentation. Segmentation may be difficult in the presence of noise, blurring or low contrast. Pre-processing aids in the removal of extraneous data from a picture or the enhancement of image contrast in the early stages. Classification is greatly influenced by segmentation. Recent research have presented automatic and semi-automated segmentation algorithms for extracting the region of interest (ROI), lesions, and masses to check for breast cancer. In this study provides high-level overview of approaches of segmentation, with a focus on mammography images from current research. The datasets that were available were discussed as well as the problems encountered during the segmentation operation for the identification of breast cancer.
ISSN:2716-3865
2721-1290
DOI:10.37899/journallamultiapp.v3i2.567