Dynamic graph cut based segmentation of mammogram

Angayarkanni, S. Pitchumani; Kamal, Nadira Banu; Thangaiya, Ranjit Jeba

doi:10.1186/s40064-015-1180-7

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ROC was found to be 0.862 with 2.8 false positives per image and a sensitivity of 82.0%. Angayarkanni et al [123] proposed a dynamic graph cut segmentation of mammogram images. The dynamic graph cut is based on Otsu's segmentation.…”

Section: Mammogram Breast Cancer Segmentation Based On the Edge Methods (Em)mentioning

confidence: 99%

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Michael

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

show abstract

Section: Mammogram Breast Cancer Segmentation Based On the Edge Methods (Em)mentioning

confidence: 99%