Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer
This addresses the need for faster tumor ROI localization in ultrasound for patients with locally advanced breast cancer, though it is incremental as it builds on existing barcode techniques.
The paper tackles the problem of manually segmenting tumor regions in ultrasound images for breast cancer patients, which is time-consuming, by proposing a semi-automated method using Radon barcodes to estimate a bounding box with 81% accuracy on 33 B-scan images.
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 $81\%$.