Classification of hepatic lesions from CT images using texture features and neural networks

Gletsos, M; Mougiakakou, SG; Matsopoulos, GK; Nikita, KS; Nikita, AS; Kelekis, D

dc.contributor.author	Gletsos, M	en
dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Matsopoulos, GK	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Nikita, AS	en
dc.contributor.author	Kelekis, D	en
dc.date.accessioned	2014-03-01T02:41:46Z
dc.date.available	2014-03-01T02:41:46Z
dc.date.issued	2001	en
dc.identifier.issn	04549244	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30617
dc.subject	Classification	en
dc.subject	Liver CT images	en
dc.subject	Neural network	en
dc.subject	Sequential floating forward selection	en
dc.subject	Texture features	en
dc.title	Classification of hepatic lesions from CT images using texture features and neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2001.1017353	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2001.1017353	en
heal.publicationDate	2001	en
heal.abstract	In this paper a computer-aided diagnostic system for the classification of hepatic lesions from Computed Tomography (CT) images is presented. Regions of Interest (ROI's) taken from non-enhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas (a total of 147 samples), have been used as input to the system. The system consists of two levels: the feature extraction and the classification levels. The feature extraction level calculates the average grey scale and 48 texture characteristics, which are derived from the spatial grey-level co-occurrence matrices, obtained from the ROI's. The classifier level consists of three sequentially placed feed-forward Neural Networks (NN's), which are activated sequentially. The first NN classifies into normal or pathological liver regions. The pathological liver regions are classified by the second NN into cysts or ""other disease"". The third NN classifies ""other disease"" into hemangiomas and hepatocellular carcinomas. In order to enhance the performance of the classifier and improve the execution time, the dimensionality of the initial feature vector has been reduced using the sequential forward floating selection method for each individual NN input vector. A total classification rate of 98% has been achieved.	en
heal.journalName	Annual Reports of the Research Reactor Institute, Kyoto University	en
dc.identifier.doi	10.1109/IEMBS.2001.1017353	en
dc.identifier.volume	3	en
dc.identifier.spage	2748	en
dc.identifier.epage	2751	en