Content-based Medical Image Annotation and Retrieval using Perceptual Hashing Algorithm
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TLDR
Perceptual Hash (P-Hash) Algorithm for the similar image retrieval based on the Query-Image is proposed, which is faster and efficient approach for web-based application.Abstract:
Nowadays, Medical Imaging is emerged well with computer applications. The diagnosis process is fully automated; based on the medical image, cases are reasoned to examine the causes such as Cancer Prediction, Age Estimation, and Injuries and so on. Automatic Image Annotation (AIA) is widely used in Medical field. Generally, the images are the retrieved using CBIR (Content-based Image Retrieval) technique. In this paper, we are proposing a novel method for the Image Retrieval. This paper uses Perceptual Hash (P-Hash) Algorithm for the similar image retrieval based on the Query-Image. The hash value of the Query image is matched with the medical training-set images and the corresponding keywords are automatically tagged. In this methodology, the system computes the similarity measure or score based on the Hamming Distance of two Hash strings. This methodology is faster and efficient approach for web-based application. In addition to this, robustness of the algorithm could be examined by altering (change in the image orientation) the query-image may not affect the hash value.read more
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Dissertation
Novi algoritam za izradu percepcijskih sažetaka temeljen na izdvajanju atributa biometrijskih karakteristika
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References
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Automatic categorization of medical images for content-based retrieval and data mining.
Thomas Martin Lehmann,Mark Oliver Güld,Thomas Deselaers,Daniel Keysers,Henning Schubert,Klaus Spitzer,Hermann Ney,Berthold B. Wein +7 more
TL;DR: This paper evaluates automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined, which is sufficient for medical CBIR applications.