Journal
MEDICAL IMAGE ANALYSIS
Volume 26, Issue 1, Pages 306-315Publisher
ELSEVIER
DOI: 10.1016/j.media.2015.10.005
Keywords
Histopathological image analysis; Cell segmentation; Large-scale; Image retrieval; Hashing
Categories
Funding
- NIH [R01 AR06547901A1]
- Oak Ridge Associated Universities
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Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells. (C) 2015 Elsevier B.V. All rights reserved.
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