Journal of Pathology Informatics Journal of Pathology Informatics
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SYMPOSIUM - ORIGINAL RESEARCH
Year : 2011  |  Volume : 2  |  Issue : 2  |  Page : 5

A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering


1 VISILAB - Intelligent Systems and Computer Vision Group, University of Castilla la Mancha, Ciudad Real, Spain
2 Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
3 Department of Anatomic Pathology, University General Hospital of Ciudad Real, Ciudad Real, Spain

Correspondence Address:
Gloria Bueno
VISILAB - Intelligent Systems and Computer Vision Group, University of Castilla la Mancha, Ciudad Real
Spain
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.92032

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With modern automated microscopes and digital cameras, pathologists no longer have to examine samples looking through microscope binoculars. Instead, the slide is digitized to an image, which can then be examined on a screen. This creates the possibility for computers to analyze the image. In this work, a fully automated approach to region of interest (ROI) segmentation in prostate biopsy images is proposed. This will allow the pathologists to focus on the most important areas of the image. The method proposed is based on level-set and mean filtering techniques for lumen centered expansion and cell density localization respectively. The novelty of the technique lies in the ability to detect complete ROIs, where a ROI is composed by the conjunction of three different structures, that is, lumen, cytoplasm, and cells, as well as regions with a high density of cells. The method is capable of dealing with full biopsies digitized at different magnifications. In this paper, results are shown with a set of 100 H and E slides, digitized at 5×, and ranging from 12 MB to 500 MB. The tests carried out show an average specificity above 99% across the board and average sensitivities of 95% and 80%, respectively, for the lumen centered expansion and cell density localization. The algorithms were also tested with images at 10× magnification (up to 1228 MB) obtaining similar results.


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