Content based sub-image retrieval system for high resolution pathology images using salient interest points.
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 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 and present an efficient and robust searching mechanism for indexing the images as well as for query execution of sub-image retrieval. We present a working sCBIR system and 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.
Mehta
Neville
N
Department of Computer Science and Engineering University at Buffalo, NY 14260, USA.
Alomari
Raja' S
RS
Chaudhary
Vipin
V
D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D003201:Computers; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D008490:Medical Informatics / Q000379:methods; D016011:Normal Distribution; D010336:Pathology / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D011859:Radiography; D012984:Software