@article{3064203,
    title = "Automatic identification and localisation of potential malignancies in contrast-enhanced ultrasound liver scans using spatio-temporal features",
    author = "Bakas, S. and Makris, D. and Sidhu, P.S. and Chatzimichail, K.",
    journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    year = "2014",
    volume = "8676",
    pages = "13-22",
    publisher = "Springer-Verlag",
    doi = "10.1007/978-3-319-13692-9_2",
    keywords = "Automation;  Computer aided diagnosis;  Diagnosis;  Liver;  Ultrasonics, Clustering;  Contrast enhanced ultrasound;  Focal liver lesions;  Localisation;  Perfusion, Medical imaging",
    abstract = "The identification and localisation of a focal liver lesion (FLL) in Contrast-Enhanced Ultrasound (CEUS) video sequences is crucial for liver cancer diagnosis, treatment planning and follow-up management. Currently, localisation and classification of FLLs between benign and malignant cases in CEUS are routinely performed manually by radiologists, in order to proceed with making a diagnosis, leading to subjective results, prone to misinterpretation and human error. This paper describes a methodology to assist clinicians who regularly perform these tasks, by discharging benign FLL cases and localise potential malignancies in a fully automatic manner by exploiting the perfusion dynamics of a CEUS video. The proposed framework uses local variations of intensity to distinguish between hyper- and hypo-enhancing regions and then analyse their spatial configuration to identify potentially malignant cases. Automatic localisation of the potential malignancy on the image plane is then addressed by clustering, using Expectation-Maximisation for Gaussian Mixture Models. A novel feature that combines description of local dynamic behaviour with spatial proximity is used in this process. Quantitative evaluation, on real clinical data from a retrospective multicentre study, demonstrates the value of the proposed method. © Springer International Publishing Switzerland 2014."
}