A Computer-Aided Diagnostic System to Characterize CT Focal Liver Lesions: Design and Optimization of a Neural Network Classifier

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	2014-03-01T01:18:30Z
dc.date.available	2014-03-01T01:18:30Z
dc.date.issued	2003	en
dc.identifier.issn	1089-7771	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15052
dc.subject	Feature selection	en
dc.subject	Liver CT	en
dc.subject	Neural networks	en
dc.subject	Texture features	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Mathematical & Computational Biology	en
dc.subject.classification	Medical Informatics	en
dc.subject.other	Biopsy	en
dc.subject.other	Computer aided design	en
dc.subject.other	Decision support systems	en
dc.subject.other	Feature extraction	en
dc.subject.other	Gastroenterology	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Mammography	en
dc.subject.other	Medical imaging	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Pathology	en
dc.subject.other	Statistical methods	en
dc.subject.other	Tumors	en
dc.subject.other	Feature selection	en
dc.subject.other	Liver computed tomography (CT)	en
dc.subject.other	Texture features	en
dc.subject.other	Computerized tomography	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	automated pattern recognition	en
dc.subject.other	clinical trial	en
dc.subject.other	comparative study	en
dc.subject.other	computer assisted diagnosis	en
dc.subject.other	computer assisted tomography	en
dc.subject.other	controlled clinical trial	en
dc.subject.other	controlled study	en
dc.subject.other	cyst	en
dc.subject.other	hemangioma	en
dc.subject.other	human	en
dc.subject.other	image quality	en
dc.subject.other	liver	en
dc.subject.other	liver cell carcinoma	en
dc.subject.other	liver tumor	en
dc.subject.other	methodology	en
dc.subject.other	pathology	en
dc.subject.other	radiography	en
dc.subject.other	reproducibility	en
dc.subject.other	sensitivity and specificity	en
dc.subject.other	validation study	en
dc.subject.other	Algorithms	en
dc.subject.other	Carcinoma, Hepatocellular	en
dc.subject.other	Cysts	en
dc.subject.other	Hemangioma	en
dc.subject.other	Humans	en
dc.subject.other	Liver	en
dc.subject.other	Liver Neoplasms	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Pattern Recognition, Automated	en
dc.subject.other	Radiographic Image Enhancement	en
dc.subject.other	Radiographic Image Interpretation, Computer-Assisted	en
dc.subject.other	Reproducibility of Results	en
dc.subject.other	Sensitivity and Specificity	en
dc.subject.other	Tomography, X-Ray Computed	en
dc.title	A Computer-Aided Diagnostic System to Characterize CT Focal Liver Lesions: Design and Optimization of a Neural Network Classifier	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TITB.2003.813793	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TITB.2003.813793	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or ""other disease."" The third NN classifies ""other disease"" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Information Technology in Biomedicine	en
dc.identifier.doi	10.1109/TITB.2003.813793	en
dc.identifier.isi	ISI:000185338100002	en
dc.identifier.volume	7	en
dc.identifier.issue	3	en
dc.identifier.spage	153	en
dc.identifier.epage	162	en