@inproceedings{9c0f041800184235ae473032befe661c,
title = "OCM image texture analysis for tissue classification",
abstract = "This paper proposes a texture analysis technique applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Local binary pattern (LBP) image features are extracted. In order to improve classification precision, a new variant of LBP feature, average LBP (ALBP) is proposed. The new LBP is integrated with the original LBP feature to improve classification precision. Our experiments show that by integrating a selected set of LBP and ALBP features, very high classification accuracy is achieved using a AdaBoost meta classifier combined with neural network weak classifiers.",
author = "Sunhua Wan and Lee, {Hsiang Chieh} and Fujimoto, {James G.} and Xiaolei Huang and Chao Zhou",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",
year = "2014",
month = jul,
day = "29",
doi = "10.1109/isbi.2014.6867817",
language = "English (US)",
series = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "93--96",
booktitle = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
address = "United States",
}