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.

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

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