@inproceedings{6b18c2cb5eb9450b83d441542a9a075f,
title = "Automatic segmentation of bone tissue from computed tomography using a volumetric local binary patterns based method",
abstract = "Segmentation of scanned tissue volumes of three-dimensional (3D) images often involves - at least partially - some manual process, as there is no standardized automatic method. A well-performing automatic segmentation would be preferable, not only because it would improve segmentation speed, but also because it would be user-independent and provide more objectivity to the task. Here we extend a 3D local binary patterns (LBP) based trabecular bone segmentation method with adaptive local thresholding and additional segmentation parameters to make it more robust yet still perform adequately when compared to traditional user-assisted segmentation. We estimate parameters for the new segmentation method (AMLM) in our experimental setting, and have two micro-computed tomography (µCT) scanned bovine trabecular bone tissue volumes segmented by both the AMLM and two experienced users. Comparison of the results shows superior performance of the AMLM.",
keywords = "3D, LBP, Micro-CT, Segmentation",
author = "Jukka Kaipala and L{\'o}pez, {Miguel Bordallo} and Simo Saarakkala and J{\'e}r{\^o}me Thevenot",
year = "2017",
month = jun
day = "1",
doi = "10.1007/978-3-319-59129-2_19",
language = "English",
isbn = "978-3-319-59128-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "221--232",
editor = "Puneet Sharma and Bianchi, {Filippo Maria}",
booktitle = "Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings",
address = "Germany",
note = "20th Scandinavian Conference on Image Analysis, SCIA 2017 ; Conference date: 12-06-2017 Through 14-06-2017",
}