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Proceedings Paper

A new morphological segmentation algorithm for biomedical imaging applications
Author(s): D. Gorpas; P. Maragos; D. Yova
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Paper 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.

Paper Details

Date Published: 2 February 2009
PDF: 9 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510C (2 February 2009); doi: 10.1117/12.805574
Show Author Affiliations
D. Gorpas, National Technical Univ. of Athens (Greece)
P. Maragos, National Technical Univ. of Athens (Greece)
D. Yova, National Technical Univ. of Athens (Greece)

Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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