@inproceedings{fc95f46c7b0d4f78960720598811279b,
title = "Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study",
abstract = "Mammographic tissue density is considered to be one of the major risk factors for developing breast cancer. In this paper we use quantitative measurements of Local Binary Patterns and its variants for breast tissue classification. We compare the classification results of LBP, ELBP, Uniform ELBP and M-ELBP for classifying mammograms as fatty, glandular and dense. A Bayesian-Network classifier is used with stratified ten-fold cross-validation. The experimental results indicate that ELBP patterns at different orientations extract more relevant elliptical breast tissue information from the mammograms indicating the importance of directional filters for breast tissue classification.",
author = "Minu George and Reyer Zwiggelaar",
year = "2018",
month = aug,
day = "21",
doi = "10.1007/978-3-319-95921-4_15",
language = "English",
isbn = "978-3-319-95920-7",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "143--152",
editor = "Mark Nixon and Sasan Mahmoodi and Reyer Zwiggelaar",
booktitle = "Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018",
address = "Switzerland",
note = "Proceedings 22nd Conference Medical Image Understanding and Analysis, MIUA 2018 ; Conference date: 09-07-2018 Through 11-07-2018",
}
