A comparative study of image features for classification of breast microcalcifications

Andreadis, II; Spyrou, GM; Nikita, KS

dc.contributor.author	Andreadis, II	en
dc.contributor.author	Spyrou, GM	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T01:34:51Z
dc.date.available	2014-03-01T01:34:51Z
dc.date.issued	2011	en
dc.identifier.issn	0957-0233	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20906
dc.subject	CAD	en
dc.subject	feature extraction	en
dc.subject	microcalcifications	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Instruments & Instrumentation	en
dc.subject.other	Breast microcalcifications	en
dc.subject.other	Classification accuracy	en
dc.subject.other	Classification results	en
dc.subject.other	Comparative studies	en
dc.subject.other	Computer-aided diagnosis system	en
dc.subject.other	Diagnostic process	en
dc.subject.other	Digital database of screening mammographies	en
dc.subject.other	Image features	en
dc.subject.other	Mammographic	en
dc.subject.other	Mathematical descriptions	en
dc.subject.other	Microcalcifications	en
dc.subject.other	Receiver operating characteristic curves	en
dc.subject.other	Texture features	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Curve fitting	en
dc.subject.other	Feature extraction	en
dc.subject.other	Mammography	en
dc.subject.other	Textures	en
dc.subject.other	X ray screens	en
dc.subject.other	Classification (of information)	en
dc.title	A comparative study of image features for classification of breast microcalcifications	en
heal.type	journalArticle	en
heal.identifier.primary	10.1088/0957-0233/22/11/114005	en
heal.identifier.secondary	http://dx.doi.org/10.1088/0957-0233/22/11/114005	en
heal.identifier.secondary	114005	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	Computer-aided diagnosis systems for mammography have been developed in order to assist radiologists in the diagnostic process by providing a reliable and objective discrimination of benign and malignant mammographic findings. The effectiveness of such systems is based on the image features extracted from mammograms, which are mainly related to the morphology, texture and optical density of the suspicious abnormality. There are many methodologies reported in the literature able to provide a mathematical description of a mammographic lesion. In this paper, we apply various feature extraction methodologies on cases containing clusters of microcalcifications. Our purpose is to compare their performance in large scale in terms of classification accuracy and to investigate their potentiality in discriminating benign from malignant clusters. Experiments were performed on 1715 cases (882 benign and 833 malignant) extracted from the Digital Database of Screening Mammography, which is the largest publicly available database of mammograms. The results of our study indicated that texture features outperformed the rest of the considered categories, while the combination of the best features optimized the classification results, leading to an area under the receiver operating characteristic curve equal to 0.82. © 2011 IOP Publishing Ltd.	en
heal.publisher	IOP PUBLISHING LTD	en
heal.journalName	Measurement Science and Technology	en
dc.identifier.doi	10.1088/0957-0233/22/11/114005	en
dc.identifier.isi	ISI:000296563500006	en
dc.identifier.volume	22	en
dc.identifier.issue	11	en