Classification algorithms for microcalcifications in mammograms (Review).

Sakka, E; Prentza, A; Koutsouris, D

dc.contributor.author	Sakka, E	en
dc.contributor.author	Prentza, A	en
dc.contributor.author	Koutsouris, D	en
dc.date.accessioned	2014-03-01T11:44:41Z
dc.date.available	2014-03-01T11:44:41Z
dc.date.issued	2006	en
dc.identifier.issn	1021-335X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/37095
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-33645778548&partnerID=40&md5=8f94b224c62ae83d5d559efa7918de59	en
dc.subject	microcalcifications	en
dc.subject	feature extraction	en
dc.subject	classification	en
dc.subject	ROC curve	en
dc.subject.classification	Oncology	en
dc.subject.other	algorithm	en
dc.subject.other	artificial neural network	en
dc.subject.other	automation	en
dc.subject.other	breast tumor	en
dc.subject.other	calcinosis	en
dc.subject.other	differential diagnosis	en
dc.subject.other	female	en
dc.subject.other	fuzzy logic	en
dc.subject.other	human	en
dc.subject.other	mammography	en
dc.subject.other	radiography	en
dc.subject.other	review	en
dc.subject.other	Algorithms	en
dc.subject.other	Automation	en
dc.subject.other	Breast Neoplasms	en
dc.subject.other	Calcinosis	en
dc.subject.other	Diagnosis, Differential	en
dc.subject.other	Female	en
dc.subject.other	Fuzzy Logic	en
dc.subject.other	Humans	en
dc.subject.other	Mammography	en
dc.subject.other	Neural Networks (Computer)	en
dc.title	Classification algorithms for microcalcifications in mammograms (Review).	en
heal.type	other	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Early detection is the key to improve breast cancer prognosis. The only proven effective method of breast cancer early detection is mammography. An early sign of 30-50% of breast cancer is the appearance of clusters of fine, granular microcalcifications and 60-80% of breast carcinomas reveal microcalcification clusters upon histological examination. The high correlation between the appearance of the microcalcification clusters and diseases, proves that computer aided diagnosis (CAD) systems for automated classification of microcalcification clusters will be very useful and helpful for breast cancer control. The fuzzy nature of microcalcification, the low contrast and the low ability of distinguishing them from their surroundings make automated characterization of them extremely difficult. In this study, we give an overview of the currently available literature on characterization of malignant and benign microcalcifications. We compare and evaluate some of the classification algorithms on microcalcifications in mammograms used in various CAD systems, which are separated into categories according to the method in use. Neural networks are used in applications where only a few decisions are required concerning an amount of data. The k-nearest neighbour classifier distinguishes unknown patterns based on the similarity to known samples and the decision tree approach is much simpler than neural networks and does not need extensive knowledge of the probability distribution of the features.	en
heal.publisher	PROFESSOR D A SPANDIDOS	en
heal.journalName	Oncology reports.	en
dc.identifier.isi	ISI:000236066000014	en
dc.identifier.volume	15 Spec no.	en
dc.identifier.spage	1049	en
dc.identifier.epage	1055	en