Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S (2007). Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artificial intelligence in medicine, 41(1), pp. 25-37. Amsterdam: Elsevier 10.1016/j.artmed.2007.05.002

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The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition

UniBE Contributor:

Mougiakakou, Stavroula

ISSN:

0933-3657

Publisher:

Elsevier

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 15:23

Last Modified:

17 Mar 2015 22:49

Publisher DOI:

10.1016/j.artmed.2007.05.002

PubMed ID:

17624744

Web of Science ID:

000249821200003

URI:

https://boris.unibe.ch/id/eprint/37258 (FactScience: 207282)

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