@inproceedings{50b0bc62978c4d318b3e82d0ada262f7,
title = "Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features",
abstract = "Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features.",
author = "Robert Marti and Yago D{\'i}ez and Arnau Oliver and Meritxell Tortajada and Reyer Zwiggelaar and Xavier Llad{\'o}",
year = "2014",
month = jun,
day = "6",
doi = "10.1007/978-3-319-07887-8_85",
language = "English",
isbn = "978-3-319-07886-1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "612--619",
editor = "Hiroshi Fujita and Takshi Hara and Chisako Muramatsu",
booktitle = "Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings",
address = "Switzerland",
}