Content based subimage retrieval in pathology images
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Content-based image retrieval systems for digital pathology require sub-image retrieval rather than the whole image retrieval for the system to be of clinical use. Digital pathology has been attracting many researchers due to their high space and computational requirements. Content-based sub-image retrieval systems for pathology images have wide applicability in computer aided diagnosis by allowing the pathologist to retrieve similar cases to a new case along with the diagnosis information. These images are huge in size and thus the pathologist is interested in retrieving specific structures from the whole images in the database along with the previous diagnosis of the retrieved sub-image. We propose a content-based sub-image retrieval system (sCBIR) framework for high resolution digital pathology images. We utilize scale-invariant feature extraction (SIFT) and present an efficient and robust searching mechanism for indexing the images as well as for query execution of sub-image retrieval. We show results of testing our system on a set of queries for specific structures of interest for pathologists in clinical use. The outcomes of the sCBIR system are compared to manual search and there is an 80% match in the top five searches. However, in dealing with high resolution images, localized indexing and retrieval strategies are time consuming and computationally expensive. We introduce a distributed element in content-based sub-image retrieval system architecture for indexing and retrieving high resolution pathology images. This helps distribute the task over various sites and avoids the computational overloading of a centralized strategy. We describe the full architecture including indexing and retrieval algorithms of our system. We validate our system using a database of 50 high resolution pathology images and apply our indexing and retrieval algorithms.