Breast Ultrasound Images Database

Breast ultrasound is a pivotal diagnostic tool in approaching breast lesions, and the propagation of computer-aided diagnosis (CAD) systems has further increased the utility of these images...

Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be associated with better overall clinical outcomes. Ultrasonography is a vital imaging modality in managing breast lesions. In addition, the development of computer-aided diagnosis (CAD) systems has further enhanced the importance of this imaging modality. Proper development of robust and reproducible CAD systems depends on the inclusion of different data from different populations and centers to considerate all variations in breast cancer pathology and minimize confounding factors. The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions. Using this and similar pieces of data can aid in the development of robust CAD systems.

If you use this dataset, please cite:

[1] A. Abbasian Ardakani, A. Mohammadi, M. Mirza-Aghazadeh-Attari, U.R. Acharya, An open-access breast lesion ultrasound image database‏: Applicable in artificial intelligence studies, Computers in Biology and Medicine, 152 (2023) 106438. https://doi.org/10.1016/j.compbiomed.2022.106438

[2] H. Hamyoon, W. Yee Chan, A. Mohammadi, T. Yusuf Kuzan, M. Mirza-Aghazadeh-Attari, W.L. Leong, K. Murzoglu Altintoprak, A. Vijayananthan, K. Rahmat, N. Ab Mumin, S. Sam Leong, S. Ejtehadifar, F. Faeghi, J. Abolghasemi, E.J. Ciaccio, U. Rajendra Acharya, A. Abbasian Ardakani, Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts, European Journal of Radiology, 157 (2022) 110591. https://doi.org/10.1016/j.ejrad.2022.110591

[3] H. Homayoun, W.Y. Chan, T.Y. Kuzan, W.L. Leong, K.M. Altintoprak, A. Mohammadi, A. Vijayananthan, K. Rahmat, S.S. Leong, M. Mirza-Aghazadeh-Attari, S. Ejtehadifar, F. Faeghi, U.R. Acharya, A.A. Ardakani, Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study, Biocybernetics and Biomedical Engineering, 42 (2022) 921-933. https://doi.org/10.1016/j.bbe.2022.07.004

Sample ultrasound image of benign (A) and malignant (B) breast lesion, and their corresponding mask and resulted fusion images.

Benign

Malignant

Picture of Ardakani AA

Ardakani AA

He received his Ph.D. in Medical Physics in 2018 from the Iran University of Medical Sciences (IUMS), specializing in medical imaging and using artificial intelligence in radiological diagnosis. His research interests focus on the physics of medical imaging systems, quantitative analysis of medical images, and applying artificial intelligence in diagnostic radiology procedures. He is an assistant professor of Medical Physics at Shahid Beheshti University of Medical Sciences.

2 Responses

  1. hi there, could you please provide me the link of the BLUI dataset? i search on this website, but i couldn’t find it.
    best regards,

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