The liver comprises cell layers of hepatocytes called trabeculae,
which are separated by vascular sinusoids. Under- standing the
structure of hepatic trabeculae and liver sinusoids in hematoxylin and eosin
(HE)-stained liver specimens is important for the differential diagnosis of
liver diseases. In this study, we develop an approach to extracting liver
sinusoids from HE-stained images. The proposed approach involves: 1) a
new orientation-selective filter (OS filter) for edge enhancement and image
denoising, 2) the clustering of image pixels to identify candidate sinusoids,
and 3) a classification procedure that discards unlikely candidates and selects
the final sinusoid areas. Experimental studies using a database of 16 images
with a resolution of 512 × 512 pixels showed that the proposed approach could
segment liver sinusoid pixels with 81% of specificity and
94% of sensitivity. A comparison with a method based on bilateral filters
showed that this method improved the sensitivity for all images with an average
improvement of 4% and no difference in specificity. The results were presented
to a group of pathologists and they confirmed that the images were highly
representative of the tissue morphology features.
Cite this paper
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