Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.
Skip to main content

Image focus quality is of utmost importance in digital microscopes because the pathologist cannot accurately characterize the tissue state without focused images. We propose to train a classifier to measure the focus quality of microscopy scans based on an extensive set of image features. However, classifiers rely heavily on the quality and quantity of the training data, and collecting annotated data is tedious and expensive. We therefore propose a new method to automatically generate large amounts of training data using image stacks. Our experiments demonstrate that a classifier trained with the image stacks performs comparably with one trained with manually annotated data. The classifier is able to accurately detect out-of-focus regions, provide focus quality feedback to the user, and identify potential problems of the microscopy design. © 2010 Springer-Verlag.

Original publication

DOI

10.1007/978-3-642-15745-5_55

Type

Journal article

Journal

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publication Date

22/11/2010

Volume

6362 LNCS

Pages

446 - 453