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Proceedings Paper

Toward consistent cell segmentation: quality assessment of cell segments via appearance and geometry features
Author(s): Andrew Brinker; Annika Fredrikson; Xiaofan Zhang; Richard Sourvenir; Shaoting Zhang
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Paper Abstract

Computer-Aided Diagnosis (CAD) systems based on histopathological images rely on quality low-level image processing, including cell segmentation. Many methods for cell segmentation lack in generality and struggle with the wide variety of cell appearance and inter-cell structure present in histopathological images. We present a computationally efficient system to classify segmentation results as the first step toward automatic segment correction. This general method can applied to existing or future cell segmentation methods to provide corrections for low-quality results. Specifically, with a small collection of easy-to-compute features, we can identify incorrect segments with a high degree of accuracy, which then can be used to determine the needed corrections based on the type of segmentation failure present.

Paper Details

Date Published: 19 March 2015
PDF: 6 pages
Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200O (19 March 2015); doi: 10.1117/12.2082329
Show Author Affiliations
Andrew Brinker, California State Univ. (United States)
Annika Fredrikson, Principia College (United States)
Xiaofan Zhang, The Univ. of North Carolina at Charlotte (United States)
Richard Sourvenir, The Univ. of North Carolina at Charlotte (United States)
Shaoting Zhang, The Univ. of North Carolina at Charlotte (United States)

Published in SPIE Proceedings Vol. 9420:
Medical Imaging 2015: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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