Abstract
The non-invasive diagnosis of the malignant tumors is a very important issue in nowadays research. Our purpose is to elaborate computerized, texture-based methods for performing automatic recognition of the hepatocellular carcinoma, the most frequent malignant liver tumor, using only information from ultrasound images. We previously defined the textural model of HCC, consisting in the exhaustive set of the textural features, relevant for HCC characterization, and in their specific values for the HCC class. In this work, we improve the textural model and the classification process, through dimensionality reduction techniques. From the feature extraction methods, we implemented the most representative ones - Principal Component Analysis (PCA), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (GDA) and combinations of these methods. We also assessed the combination of the feature extraction techniques with feature selection techniques. All these methods were evaluated for distinguishing HCC from the cirrhotic liver parenchyma on which it evolves.
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Mitrea, D., Nedevschi, S., Socaciu, M., Badea, R. (2011). The Role of the Feature Extraction Methods in Improving the Textural Model of the Hepatocellular Carcinoma, Based on Ultrasound Images. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22389-1_44
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DOI: https://doi.org/10.1007/978-3-642-22389-1_44
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