Computer aided diagnosis based on medical image processing and artificial intelligence methods

Stoitsis, J; Valavanis, I; Mougiakakou, SG; Golemati, S; Nikita, A; Nikita, KS

dc.contributor.author	Stoitsis, J	en
dc.contributor.author	Valavanis, I	en
dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Golemati, S	en
dc.contributor.author	Nikita, A	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T01:23:43Z
dc.date.available	2014-03-01T01:23:43Z
dc.date.issued	2006	en
dc.identifier.issn	0168-9002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17116
dc.subject	Atherosclerosis	en
dc.subject	Computer Aided Diagnosis	en
dc.subject	Focal liver lesions	en
dc.subject	Medical image interpretation	en
dc.subject.classification	Instruments & Instrumentation	en
dc.subject.classification	Nuclear Science & Technology	en
dc.subject.classification	Physics, Particles & Fields	en
dc.subject.classification	Spectroscopy	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Feature extraction	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Medical imaging	en
dc.subject.other	ANalysis of VAriance (ANOVA)	en
dc.subject.other	Atherosclerosis	en
dc.subject.other	Focal liver lesions	en
dc.subject.other	Medical image interpretation	en
dc.subject.other	Computer aided diagnosis	en
dc.title	Computer aided diagnosis based on medical image processing and artificial intelligence methods	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.nima.2006.08.134	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.nima.2006.08.134	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atherotnatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84%, were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis. (c) 2006 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment	en
dc.identifier.doi	10.1016/j.nima.2006.08.134	en
dc.identifier.isi	ISI:000243241300096	en
dc.identifier.volume	569	en
dc.identifier.issue	2 SPEC. ISS.	en
dc.identifier.spage	591	en
dc.identifier.epage	595	en