Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening

Authors:
Patrik Malm;Anders Brun
Affiliations:
Centre for Image Analysis, Uppsala, Sweden SE-751 05;Centre for Image Analysis, Uppsala, Sweden SE-751 05
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Year:
2009

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Abstract

In this paper, we describe a nuclei segmentation algorithm for Pap smears that uses anisotropic dilation for curve closing. Edge detection methods often return broken edges that need to be closed to achieve a proper segmentation. Our method performs dilation using Riemannian distance maps that are derived from the local structure tensor field in the image. We show that our curve closing improve the segmentation along weak edges and significantly increases the overall performance of segmentation. This is validated in a thorough study on realistic synthetic cell images from our Pap smear simulator. The algorithm is also demonstrated on bright-field microscope images of real Pap smears from cervical cancer screening.