Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/3019
Título: Fatty liver characterization and classification by ultrasound
Autor: Ribeiro, Ricardo
Sanches, João
Palavras-chave: Ultrasound
Steatosis diagnosis
Pattern recognition
Computer imaging
Image processing
Computer vision
Computer graphics
Artificial intelligence
Data: 2009
Editora: Springer
Citação: Ribeiro R, Sanches J. Fatty liver characterization and classification by ultrasound. In Araújo H, Mendonça AM, Pinho AJ, Torres MI, editors. Pattern recognition and image analysis. Berlin: Springer; 2009. p. 354-61.
Resumo: Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.
Peer review: yes
URI: http://hdl.handle.net/10400.21/3019
ISBN: 978-3-642-02172-5
Versão do Editor: http://link.springer.com/chapter/10.1007%2F978-3-642-02172-5_46
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