@inproceedings{51c9190c4c294f339b89717bda3b7d56,
title = "Tumour ROI estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer",
abstract = "Quantitative ultrasound (QUS) methods provide a promising framework that can non-invasively and inexpensively be used to predict or assess the tumour response to cancer treatment. The first step in using the QUS methods is to select a region of interest (ROI) inside the tumour in ultrasound images. Manual segmentation, however, is very time consuming and tedious. In this paper, a semi-automated approach will be proposed to roughly localize an ROI for a tumour in ultrasound images of patients with locally advanced breast cancer (LABC). Content-based barcodes, a recently introduced binary descriptor based on Radon transform, were used in order to find similar cases and estimate a bounding box surrounding the tumour. Experiments with 33 B-scan images resulted in promising results with an accuracy of 81%.",
keywords = "Breast cancer, Radon barcodes, response monitoring, segmentation, treatment prediction, ultrasound",
author = "Tizhoosh, {Hamid R.} and Gangeh, {Mehrdad J.} and Hadi Tadayyon and Czarnota, {Gregory J.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",
year = "2016",
month = jun,
day = "15",
doi = "10.1109/ISBI.2016.7493478",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1185--1189",
booktitle = "2016 IEEE International Symposium on Biomedical Imaging",
}