A new morphological segmentation algorithm for biomedical imaging applications

Gorpas, D; Maragos, P; Yova, D

dc.contributor.author	Gorpas, D	en
dc.contributor.author	Maragos, P	en
dc.contributor.author	Yova, D	en
dc.date.accessioned	2014-03-01T02:51:55Z
dc.date.available	2014-03-01T02:51:55Z
dc.date.issued	2009	en
dc.identifier.issn	0277786X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35749
dc.subject	Biomedical image segmentation	en
dc.subject	Contrast enhancement	en
dc.subject	Image gradient	en
dc.subject	Morphological sequential filtering	en
dc.subject	Regional minima	en
dc.subject	Watershed transformation	en
dc.subject.other	Biomedical image segmentation	en
dc.subject.other	Contrast enhancement	en
dc.subject.other	Image gradient	en
dc.subject.other	Morphological sequential filtering	en
dc.subject.other	Regional minima	en
dc.subject.other	Watershed transformation	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer vision	en
dc.subject.other	Digital image storage	en
dc.subject.other	Image acquisition	en
dc.subject.other	Imaging systems	en
dc.subject.other	Landforms	en
dc.subject.other	Medical imaging	en
dc.subject.other	Sequential switching	en
dc.subject.other	Watersheds	en
dc.subject.other	Image segmentation	en
dc.title	A new morphological segmentation algorithm for biomedical imaging applications	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.805574	en
heal.identifier.secondary	72510C	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.805574	en
heal.publicationDate	2009	en
heal.abstract	Images of high geometrical complexity are found in various applications in the fields of image processing and computer vision. Medical imaging is such an application, where the processing of digitized images reveals vital information for the therapeutic or diagnostic algorithms. However, the segmentation of these images has been proved to be one of the most challenging topics in modern computer vision algorithms. The light interaction with tissues and the geometrical complexity with the tangent objects are among the most common reasons that many segmentation techniques nowadays are strictly related to specific applications and image acquisition protocols. In this paper a sophisticated segmentation algorithm is introduced that succeeds into overcoming the application dependent accuracy levels. This algorithm is based on morphological sequential filtering, combined with a watershed transformation. The results on various biomedical test images present increased accuracy, which is independent of the image acquisition protocol. This method can provide researchers with a valuable tool, which makes the classification or the follow-up faster, more accurate and objective. © 2009 SPIE.	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.doi	10.1117/12.805574	en
dc.identifier.volume	7251	en