Paper
29 March 2013 Content-based white blood cell retrieval on bright-field pathology images
Xin Qi, Rebekah H. Gensure, David J. Foran, Lin Yang
Author Affiliations +
Proceedings Volume 8676, Medical Imaging 2013: Digital Pathology; 86760L (2013) https://doi.org/10.1117/12.2006439
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
The purpose of this work was to evaluate a newly developed content-based retrieval approach for characterizing a range of different white blood cells from a database of imaged peripheral blood smears. Specimens were imaged using a 20× magnification to provide adequate resolution and sufficiently large field of view. The resulting database included a test ensemble of 96 images (1000×1000 pixels each). In this work, we propose a four-step content-based retrieval method and evaluate its performance. The content-based image retrieval (CBIR) method starts from white blood cell identification, followed by three sequential steps including coarse-searching, refined searching, and finally mean-shift clustering using a hierarchical annular histogram (HAH). The prototype system was shown to reliably retrieve those candidate images exhibiting the highest-ranked (most similar) characteristics to the query. The results presented here show that the algorithm was able to parse out subtle staining differences and spatial patterns and distributions for the entire range of white blood cells under study. Central to the design of the system is that it capitalizes on lessons learned by our team while observing human experts when they are asked to carry out these same tasks.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xin Qi, Rebekah H. Gensure, David J. Foran, and Lin Yang "Content-based white blood cell retrieval on bright-field pathology images", Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760L (29 March 2013); https://doi.org/10.1117/12.2006439
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Blood

Databases

Image retrieval

Pathology

Image segmentation

Content based image retrieval

Algorithm development

RELATED CONTENT

Image quality and segmentation
Proceedings of SPIE (March 13 2018)
Image retrieval for identifying house plants
Proceedings of SPIE (February 10 2010)
Fermat theorem and elliptic color histogram features
Proceedings of SPIE (January 13 2003)
Tools for texture- and color-based search of images
Proceedings of SPIE (June 03 1997)
Content-based image retrieval
Proceedings of SPIE (February 26 2010)

Back to Top