Autori: Mitrea D, Nedevschi S, Lupsor M., Socaciu M, Badea R

Editorial: Proceedings of International Conference on Advancements of Medicine and Health Care through Technology. Springer Berlin Heidelberg, DOI: 10.1007/978-3-642-04292-8_59, 26, p.267-272, 2009.

Rezumat:

The purpose of our study is to elaborate a reliable method in order to characterize and differentiate the hepatocellular carcinoma in a non-invasive way, based only on information obtained from ultrasound images. Texture is a very important feature, which can reveal subtle characteristics of the tissue in ultrasound images, the computerized methods for texture characterization being able to overpass the limits of the subjective human eye. The textural features are analyzed using specific methods from the field of statistical pattern classification, the final objective being that of performing computer-aided and automatic diagnosis of HCC. Thus, we build the imagistic textural model of HCC, consisting in the exhaustive set of relevant textural features, which best characterize HCC, and their specific values in the case of HCC. In order to obtain the imagistic textural model, the following steps are due: 1.) an image analysis phase, consisting in the computation of the textural features; 2.) a learning step, involving the selection of the relevant textural features and the estimation of their specific values; 3) a validation phase, consisting in the evaluation of the imagistic textural model. In this paper we aim to build an improved imagistic textural model of HCC, by refining the processes of feature selection and classification. The feature selection will be realized by comparing the results obtained with efficient feature selection methods, applied individually, or in combination. The classification will be performed using the most appropriate classifiers, as well as metaclassifiers. HCC will be compared with other visually similar tissues: the cirrhotic parenchyma on which it evolves and the benign tumors.

Cuvinte cheie: texture - feature selection - imagistic textural model - classification - hepatocellular carcinoma

URL: http://www.springerlink.com/content/j6037v42234v1w93/