A review of automatic mass detection and segmentation in mammographic images

By Arnau Oliver, Jordi Freixenet, Joan Martí, Elsa Pérez, Josep Pont, Erika R. E. Denton, Reyer Zwiggelaar. Medical Image Anal., 2010

435 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/07a94891c522d59a1343e6292234aa9d43e89d89

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Citations in the corpus

Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome (2014, methodology): “Evidence for clinical benefit Texture analyses have been used extensively in x-ray mammography (52).”
Medical Image Segmentation Methods, Algorithms, and Applications (2014, methodology): “Region growing has been widely used in mammograms in order to extract the potential lesion from its background [49]. k -nearest neighbour is a simple classiﬁer method and maximum likelihood is a popular parametric classiﬁer method.”
INbreast: toward a full-field digital mammographic database. (2012, background): “As noted by Oliver and colleagues (20), there is no public available database made with digital mammograms.”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “ortant step in the diagnostic process which identies an abnormality (such as a mass or a nodule), an organ, an anatomical structure, or a region of interest for further classication or segmentation [84, 85, 86]. Here, we discuss the common architectures used for various detection tasks in radiology ”
Benign and malignant breast tumors classification based on region growing and CNN segmentation (2015, methodology): “(i) Region-based methods (such as region growing, split/merge using quad-tree decomposition) in which similarities are detected, and (ii) boundary-based methods (such as thresholding, gradient edge detection) in which discontinuities are detected and linked to form region boundaries (Oliver et al., ”
Adversarial Deep Structural Networks for Mammographic Mass Segmentation (2016, background): “There are two types of mammographic abnormality for most abnormal cases: micro-calcifications and masses [24].”