Journal
MEDICAL IMAGE ANALYSIS
Volume 34, Issue -, Pages 3-12Publisher
ELSEVIER
DOI: 10.1016/j.media.2016.07.011
Keywords
Hashing; Feature fusion; Histopathology; Computer-aided diagnosis (CAD); Content-based image retrieval (CBIR)
Categories
Funding
- Direct For Computer & Info Scie & Enginr
- CISE Information Technology Research [1069258] Funding Source: National Science Foundation
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Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5 ms query time, comparing favorably with traditional methods. (C) 2016 Elsevier B.V. All rights reserved.
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