Fuzzy C-Means driven FHCE contextual segmentation method for mammographic micro-calcifications detection

dc.contributor.author	Μπουγιούκος, Παναγιώτης	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-04T07:27:32Z
dc.date.issued	2015-05-04
dc.identifier.uri	http://hdl.handle.net/11400/9610
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.maneyonline.com/doi/abs/10.1179/136821909X12581187860095	en
dc.subject	Image segmentation
dc.subject	Mammography
dc.subject	Τμηματοποίηση εικόνας
dc.subject	Μαστογραφία
dc.title	Fuzzy C-Means driven FHCE contextual segmentation method for mammographic micro-calcifications detection	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.contributorName	Δημητρόπουλος, Νικόλαος	el
heal.contributorName	Νικηφορίδης, Γεώργιος Χ.	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.identifier.secondary	DOI: http://dx.doi.org/10.1179/136821909X12581187860095
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2010
heal.bibliographicCitation	Bougioukos, P., Glotsos, D., Kostopoulos, S., Daskalakis, A., Kalatzis, I., et al. (June 2010). Fuzzy C-Means driven FHCE contextual segmentation method for mammographic micro-calcifications detection. Imaging Science Journal. 58(3). pp. 146-154. Maney Publishing: 2010.	en
heal.abstract	The frequency histogram of connected elements (FHCE) is a recently proposed algorithm that has successfully been applied in various medical image segmentation tasks. The FHCE is based on the idea that most pixels belong to the same class as their neighbouring pixels. However, the FHCE performance relies to a great extent on the optimal selection of a threshold parameter. Since evaluating segmentation results is a highly subjective process, a collection of threshold values must typically be examined. No algorithm has been proposed to automate the determination of the threshold parameter value of the FHCE. This study presents a method based on the fuzzy C-means clustering algorithm, designed to automatically generate optimal threshold values for the FHCE. This new approach was applied as a part of a structured sequence of image processing steps in order to facilitate segmentation of microcalcifications in digitized mammograms. A unique threshold value was generated for each mammogram, taking into account the different grey-level patterns based on different compositions of various breast tissues in it. The segmentation algorithm was tested on 100 mammograms (50 collected from the Mammographic Image Analysis Society and 50 normal mammograms onto which a number of simulated microcalcifications were generated). The algorithm was able to detect subtle microcalcifications with sensitivity ranging from 93 to 98%, False alarm ratio from 3 to 5% and false negatives variability from 2 to 3%.	en
heal.publisher	Maney Publishing	el
heal.journalName	Imaging Science Journal	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	false