Image segmentation for biomedical applications based on alternating sequential filtering and watershed transformation

Gorpas, D; Yova, D

dc.contributor.author	Gorpas, D	en
dc.contributor.author	Yova, D	en
dc.date.accessioned	2014-03-01T02:52:00Z
dc.date.available	2014-03-01T02:52:00Z
dc.date.issued	2009	en
dc.identifier.issn	16057422	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35811
dc.subject	Alternating sequential filtering	en
dc.subject	Contrast enhancement	en
dc.subject	Image segmentation	en
dc.subject	Morphological filtering	en
dc.subject	Watershed transformation	en
dc.subject.other	Acquisition systems	en
dc.subject.other	Alternating sequential filtering	en
dc.subject.other	Biomedical applications	en
dc.subject.other	Biomedical images	en
dc.subject.other	Biomedical imaging	en
dc.subject.other	Complex geometries	en
dc.subject.other	Contrast enhancement	en
dc.subject.other	False detections	en
dc.subject.other	Intensity profiles	en
dc.subject.other	Morphological filtering	en
dc.subject.other	Regions of interest	en
dc.subject.other	Sequential filtering	en
dc.subject.other	Tissue interactions	en
dc.subject.other	Watershed transformation	en
dc.subject.other	Watershed transformations	en
dc.subject.other	Digital image storage	en
dc.subject.other	Landforms	en
dc.subject.other	Metal recovery	en
dc.subject.other	Sequential switching	en
dc.subject.other	Watersheds	en
dc.subject.other	Image segmentation	en
dc.title	Image segmentation for biomedical applications based on alternating sequential filtering and watershed transformation	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.831715	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.831715	en
heal.identifier.secondary	73700F	en
heal.publicationDate	2009	en
heal.abstract	One of the major challenges in biomedical imaging is the extraction of quantified information from the acquired images. Light and tissue interaction leads to the acquisition of images that present inconsistent intensity profiles and thus the accurate identification of the regions of interest is a rather complicated process. On the other hand, the complex geometries and the tangent objects that very often are present in the acquired images, lead to either false detections or to the merging, shrinkage or expansion of the regions of interest. In this paper an algorithm, which is based on alternating sequential filtering and watershed transformation, is proposed for the segmentation of biomedical images. This algorithm has been tested over two applications, each one based on different acquisition system, and the results illustrate its accuracy in segmenting the regions of interest. © 2009 SPIE-OSA.	en
heal.journalName	Progress in Biomedical Optics and Imaging - Proceedings of SPIE	en
dc.identifier.doi	10.1117/12.831715	en
dc.identifier.volume	7370	en