@inproceedings{71f44b242fba460ca6c7720a8c0927e2,
title = "Nonlinear Local Transformation Based Mammographic Image Enhancement",
abstract = "Mammography is one of the most effective techniques for early detection of breast cancer. The quality of the image may suffer from poor resolution or low contrast, which can effect the efficiency of radiologists. In order to improve the visual quality of mammograms, this paper introduces a new mammographic image enhancement algorithm. Firstly an intensity based nonlinear transformation is used for reducing the background tissue intensity, and secondly adaptive local contrast enhancement is realized based on local standard deviation and luminance information. The proposed method can obtain improved performance compared to alternative methods both covering objective and subjective aspects, based on 45 images. Experimental results demonstrate that the proposed algorithm can improve the contrast effectively and enhance lesion information (microcalcifications and/or masses).",
keywords = "Local contrast enhancement, Mammography, Nonlinear transformation",
author = "Cuiping Ding and Min Dong and Hongjuan Zhang and Yide Ma and Yaping Yan and Reyer Zwiggelaar",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
month = jun,
day = "17",
doi = "10.1007/978-3-319-41546-8_22",
language = "English",
isbn = "978-3-319-41545-1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "167--173",
editor = "Kristina Lang and Anders Tingberg and Pontus Timberg",
booktitle = "Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings",
address = "Switzerland",
}
