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Combination of color and focus segmentation for medical images with low depth-of-field

Wirth, T.; Naber, A.; Nahm, W.

Abstract:
Image segmentation plays an increasingly important role in image processing. It allows for various applications including the analysis of an image for automatic image understanding and the integration of complementary data. During vascular surgeries, the blood flow in the vessels has to be checked constantly, which could be facilitated by a segmentation of the affected vessels. The segmentation of medical images is still done manually, which depends on the surgeon’s experience and is time-consuming. As a result, there is a growing need for automatic image segmentation methods. We propose an unsupervised method to detect the regions of no interest (RONI) in intraoperative images with low depth-of-field (DOF). The proposed method is divided into three steps. First, a color segmentation using a clustering algorithm is performed. In a second step, we assume that the regions of interest (ROI) are in focus whereas the RONI are unfocused. This allows us to segment the image using an edge-based focus measure. Finally, we combine the focused edges with the color RONI to determine the final segmentation result. When tested on different intraoperative images of aneurysm clipping surgeries, the algorithm is able to segment most of the RONI not belonging to the pulsating vessel of interest. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000090645
Veröffentlicht am 12.02.2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Biomedizinische Technik (IBT)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2364-5504
urn:nbn:de:swb:90-906457
KITopen-ID: 1000090645
Erschienen in Current directions in biomedical engineering
Band 4
Heft 1
Seiten 345-349
Vorab online veröffentlicht am 22.09.2018
Schlagworte image segmentation, regions of no interest, low depth-of-field (DOF), edge linking
Nachgewiesen in Scopus
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