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20 March 2015 Texture classification of anatomical structures in CT using a context-free machine learning approach
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Abstract
Medical images contain a large amount of visual information about structures and anomalies in the human body. To make sense of this information, human interpretation is often essential. On the other hand, computer-based approaches can exploit information contained in the images by numerically measuring and quantifying specific visual features. Annotation of organs and other anatomical regions is an important step before computing numerical features on medical images. In this paper, a texture-based organ classification algorithm is presented, which can be used to reduce the time required for annotating medical images. The texture of organs is analyzed using a combination of state-of-the-art techniques: the Riesz transform and a bag of meaningful visual words. The effect of a meaningfulness transformation in the visual word space yields two important advantages that can be seen in the results. The number of descriptors is enormously reduced down to 10% of the original size, whereas classification accuracy is improved by up to 25% with respect to the baseline approach.
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Oscar Alfonso Jiménez del Toro, Antonio Foncubierta-Rodríguez, Adrien Depeursinge, and Henning Müller "Texture classification of anatomical structures in CT using a context-free machine learning approach", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140W (20 March 2015); https://doi.org/10.1117/12.2082273
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