Tumour ROI estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer

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%.

[1]  B. Cheson,et al.  Positron-emission tomography and assessment of cancer therapy. , 2006, The New England journal of medicine.

[2]  Kevin M Brindle,et al.  Imaging tumour cell metabolism using hyperpolarized 13C magnetic resonance spectroscopy. , 2010, Biochemical Society transactions.

[3]  Mehrdad J. Gangeh,et al.  Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer , 2016, IEEE Transactions on Medical Imaging.

[4]  Kevin Brindle,et al.  New approaches for imaging tumour responses to treatment , 2008, Nature Reviews Cancer.

[5]  Ronald H. Silverman,et al.  Ultrasonic spectrum analysis for tissue assays and therapy evaluation , 1997, Int. J. Imaging Syst. Technol..

[6]  Mehrdad J. Gangeh,et al.  Quantitative evaluation of cell death response in vitro and in vivo using conventional-frequency ultrasound , 2015, Oncoscience.

[7]  Mehrdad J. Gangeh,et al.  Cancer therapy prognosis using quantitative ultrasound spectroscopy and a kernel-based metric , 2014, Medical Imaging.

[8]  Gregory J. Czarnota,et al.  Ultrasound detection of cell death , 2010 .

[9]  S. Giordano,et al.  Update on locally advanced breast cancer. , 2003, The oncologist.

[10]  Hadi Tadayyon,et al.  Breast tumour visualization using 3D quantitative ultrasound methods , 2016, SPIE Medical Imaging.

[11]  Vivek Jain,et al.  A Survey : On Content Based Image Retrieval , 2013 .

[12]  Douglas L. Jones,et al.  Line and boundary detection in speckle images , 1998, IEEE Trans. Image Process..

[13]  M. Yaffe,et al.  Functional Imaging Using Diffuse Optical Spectroscopy of Neoadjuvant Chemotherapy Response in Women with Locally Advanced Breast Cancer , 2010, Clinical Cancer Research.

[14]  Hamid R. Tizhoosh,et al.  Barcode annotations for medical image retrieval: A preliminary investigation , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[15]  Mohamed S. Kamel,et al.  Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy , 2014, IEEE Transactions on Medical Imaging.

[16]  Ashish Mohan Yadav,et al.  A Survey on Content Based Image Retrieval Systems , 2014 .

[17]  Jeppiaar Nagar,et al.  A Survey on Content Based Image Retrieval , 2013 .

[18]  Hamid R. Tizhoosh,et al.  Autoencoding the retrieval relevance of medical images , 2015, 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA).

[19]  J W Hunt,et al.  © 1999 Cancer Research Campaign Article no. bjoc.1999.0724 Ultrasound imaging of apoptosis: high-resolution noninvasive , 2022 .

[20]  Hamid R. Tizhoosh.  Fuzzy-Bildverarbeitung - Einführung in Theorie und Praxis , 1997 .

[21]  Payel Ghosh,et al.  Review of medical image retrieval systems and future directions , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).